Ahmed2_Bert.ipynb

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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/Nouran-Khallaf/bert_score/blob/master/Ahmed2_Bert.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGnlRWvkY-2c"
      },
      "source": [
        "# Readability Classifier with AraBERT\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJ6MhJYYBCwu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03352210-5864-45d5-991f-78db2b2326b6"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Jan  7 19:50:23 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 495.44       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 K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   54C    P8    31W / 149W |      0MiB / 11441MiB |      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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kj_7Tz0-pK69",
        "outputId": "4b1176c7-4653-4333-bbe6-6a170fa724f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install -q -U watermark"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "markdown 3.3.6 requires importlib-metadata>=4.4; python_version < \"3.10\", but you have importlib-metadata 2.1.2 which is incompatible.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jjsbi1u3QFEM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c77c90b-eb0a-4668-e952-a61df5586354"
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 5.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Collecting 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",
            "\u001b[K     |████████████████████████████████| 3.3 MB 34.3 MB/s \n",
            "\u001b[?25hCollecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 46.9 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.2.1-py3-none-any.whl (61 kB)\n",
            "\u001b[K     |████████████████████████████████| 61 kB 490 kB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 43.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (2.1.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, 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",
            "Successfully installed huggingface-hub-0.2.1 pyyaml-6.0 sacremoses-0.0.46 tokenizers-0.10.3 transformers-4.15.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJqoaFpVpoM8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72b36ec2-0f4b-4cb6-ae29-1989c59faeee"
      },
      "source": [
        "%reload_ext watermark\n",
        "%watermark -v -p numpy,pandas,torch,transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python implementation: CPython\n",
            "Python version       : 3.7.12\n",
            "IPython version      : 5.5.0\n",
            "\n",
            "numpy       : 1.19.5\n",
            "pandas      : 1.1.5\n",
            "torch       : 1.10.0+cu111\n",
            "transformers: 4.15.0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEGcZI7zfi5c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c68943fc-d6f7-4a8e-d940-9ad0f48d146b"
      },
      "source": [
        "import transformers\n",
        "from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n",
        "import torch\n",
        "from transformers import XLMRobertaTokenizer\n",
        "from transformers import XLMRobertaForSequenceClassification\n",
        "from transformers import RobertaTokenizer, RobertaModel,XLMRobertaModel\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from collections import defaultdict\n",
        "from textwrap import wrap\n",
        "\n",
        "from torch import nn, optim\n",
        "from torch.utils.data import Dataset, DataLoader,RandomSampler\n",
        "import torch.nn.functional as F\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "\n",
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
        "\n",
        "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
        "\n",
        "rcParams['figure.figsize'] = 12, 8\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufzPdoTtNikq"
      },
      "source": [
        "## Data Exploration\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "kCgIYgDhGDvl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zqs_1FABicRn",
        "outputId": "8d781869-f65d-4725-a0c0-7f26c667b661",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-87fa3beb-48cd-4553-b7b5-6e0bf9792780\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-87fa3beb-48cd-4553-b7b5-6e0bf9792780\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving ss.csv to ss.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4FfNPBMevmt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "1f74d313-c518-47fc-94f1-063aaa5411ad"
      },
      "source": [
        "####Data_2 Classes\n",
        "class_names = ['Certain' ,'uncertain']\n",
        "df = pd.read_csv(\"ss.csv\")\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d2c55bc1-c520-4444-be78-92ae0824b763\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
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              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>category</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The accompanying financial statements have bee...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>As described in Notes 2 and 9 to the Company's...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>As described in Note 5 to the Company's consol...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>). The significant estimation was primarily du...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>). The Company accounted for the acquisition u...</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d2c55bc1-c520-4444-be78-92ae0824b763');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                text  category\n",
              "0  The accompanying financial statements have bee...         0\n",
              "1  As described in Notes 2 and 9 to the Company's...         1\n",
              "2  As described in Note 5 to the Company's consol...         1\n",
              "3  ). The significant estimation was primarily du...         1\n",
              "4  ). The Company accounted for the acquisition u...         1"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tdemgxhvee5A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "9eea7600-fc7d-45b9-9b43-808d2fa7d96b"
      },
      "source": [
        "####Data_3 Classes\n",
        "class_names = ['Simple' ,'Moderate','Complex']\n",
        "df = pd.read_csv(\"Albert_All.csv\")\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>label</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>وهذا بلا شك يعطي انطباعاً بأهميته وصلته بالواق...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>ومن الجميل أن يخصص المرء لنفسه بعض الوقت دون ت...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>ومع أن مجالات علم اللغة متعددة</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>وما كدر صفو العمرة إلا ضياع إحدى أخواتي ،ولكن ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>وما أهمية كل تخصص ومجالاته</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   label                                               Text\n",
              "0      0  وهذا بلا شك يعطي انطباعاً بأهميته وصلته بالواق...\n",
              "1      0  ومن الجميل أن يخصص المرء لنفسه بعض الوقت دون ت...\n",
              "2      0                    ومع أن مجالات علم اللغة متعددة \n",
              "3      0  وما كدر صفو العمرة إلا ضياع إحدى أخواتي ،ولكن ...\n",
              "4      0                         وما أهمية كل تخصص ومجالاته"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3tY3ECJDPaz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "outputId": "6ca87efb-52d2-4ec9-b216-5767df177fd3"
      },
      "source": [
        "ax = sns.countplot(df.category)\n",
        "plt.xlabel('certainaty')\n",
        "ax.set_xticklabels(class_names);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 736,
              "height": 493
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aHyGuTFgyPO"
      },
      "source": [
        "## Data Preprocessing\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7Mj-0ne--5t"
      },
      "source": [
        "PRE_TRAINED_MODEL_NAME = 'bert-base-uncased'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3AfJSZ8NNLF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "998dc8d1a67a4225a1570db76717a4e9",
            "5e2f484d73ee454d95260c072cbda76e",
            "7f439d3e5f774dc6aa5569b8a855f591",
            "fdbb2aa20f894ed2b1c9aaf9607cec3d",
            "51fffedb05f548eeb7cc6e9a2ef0bc9a",
            "b947766c3f38446ea1e8682e5d258f2f",
            "cb30b694d8d94e34a1666c65498ce040",
            "ee5e8854d0a84a6e973844b7bad55310",
            "5728407b6e6e42e1998a5d37585559bf",
            "0b97e1064aee48cd8803ea8cb94ea96f",
            "cffa5ec4dcdf46f28b2e58f685eb2063",
            "db5a9ee0e1c5438999344cbf838af800",
            "15d3d4b5fcec43f99d24b2522673eaab",
            "8357ddd9738f49bc9e49689f275a4f18",
            "5a837598c42842b5a32355fc26100b17",
            "250a4066ccc443f48b5ae82b633b1abf",
            "208623db8b57415fb994e79eea004076",
            "a693669629964f9491685947d6f59c9e",
            "ab0fcebbcb354cf2a5c2377c1fe1c857",
            "933bc3576316406a8afe25b11aa395d2",
            "9a854d0b78824e31bed6a21854b89c01",
            "4f85f6591c0b4e79a1220d0a3bf4cd3e",
            "eb1e358dc17a44458b5c4be8a9cbcc13",
            "9ce32d1520e84b0a9fa015660bd6f160",
            "affdcd2ac93443c2aa8e9d934980b82d",
            "867c2755b0394f59a79a8a102300be72",
            "831d55efac3347c1a9579113753bb774",
            "75ae7b15ab2348e9bbb9a9646ed8fec3",
            "277ad12f71324a409d4d4cc8d4c5aa16",
            "cf4f0a97ab954c7d879bcd4eefdd9fb7",
            "fa66993e06964bb280192559493cfb4f",
            "dcf00a0fc4134ba6a95e4f164b089dbb",
            "446559b5d9ea443888a1e67cc6dbc8a4",
            "759bc7da5aa04256911f3a23d2b09142",
            "cdefada379644a8889e3ef7d519650a0",
            "747e27fe40af472680cf4cee10978879",
            "e82bd4f9ac3544b797e6497d5f09f011",
            "3115f68f31f94963ae1ce4d91ab4a063",
            "11c3c97722764cdf9db01e3f4c2be44d",
            "7cebf72a24c440a29c348ac24c24780d",
            "fc3742036017418081f47b19f2135c0f",
            "9b6f9c9d020441ab9d1426cd091b3793",
            "4fa06d7b1b754fcfa23694c53dc6f291",
            "0b8a769d10614b08ad80fe548af75f41"
          ]
        },
        "outputId": "ffeeb9aa-cf25-4a27-90fd-866041624769"
      },
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "998dc8d1a67a4225a1570db76717a4e9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db5a9ee0e1c5438999344cbf838af800",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "model_id": "eb1e358dc17a44458b5c4be8a9cbcc13",
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              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "759bc7da5aa04256911f3a23d2b09142",
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              "version_major": 2
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              "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waKjYxTDuaWt"
      },
      "source": [
        "### Choosing Sequence Length\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUnE5CT9hbeZ"
      },
      "source": [
        "token_lens = []\n",
        "for txt in df.text:\n",
        "  tokens = tokenizer.encode(txt, max_length=512,truncation=True)\n",
        "  token_lens.append(len(tokens))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SzE1j4jxmUtd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "outputId": "f9f0dbe1-ac07-4b69-f031-4372267136cd"
      },
      "source": [
        "sns.distplot(token_lens)\n",
        "plt.xlim([0, 256]);\n",
        "plt.xlabel('Token count');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 749,
              "height": 489
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7xSmJtLuoxW"
      },
      "source": [
        "MAX_LEN = 280"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2BPgRJ7YBK0"
      },
      "source": [
        "###Class to Creat PyTorch DataSet\n",
        "class GPReviewDataset(Dataset):\n",
        "\n",
        "  def __init__(self, reviews, targets, tokenizer, max_len):\n",
        "    self.reviews = reviews\n",
        "    self.targets = targets\n",
        "    self.tokenizer = tokenizer\n",
        "    self.max_len = max_len\n",
        "  \n",
        "  def __len__(self):\n",
        "    return len(self.reviews)\n",
        "  \n",
        "  def __getitem__(self, item):\n",
        "    review = str(self.reviews[item])\n",
        "    target = self.targets[item]\n",
        "\n",
        "    encoding = self.tokenizer.encode_plus(\n",
        "      review,\n",
        "      add_special_tokens=True,\n",
        "      max_length=self.max_len,\n",
        "      return_token_type_ids=False,\n",
        "      padding = \"max_length\",\n",
        "      return_attention_mask=True,truncation=True,\n",
        "      return_tensors='pt',\n",
        "    )\n",
        "\n",
        "    return {\n",
        "      'review_text': review,\n",
        "      'input_ids': encoding['input_ids'].flatten(),\n",
        "      'attention_mask': encoding['attention_mask'].flatten(),\n",
        "      'targets': torch.tensor(target, dtype=torch.long)\n",
        "    }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTEOnNWhftLZ"
      },
      "source": [
        "###Data_Loader Helper\n",
        "def create_data_loader(df, tokenizer, max_len, batch_size):\n",
        "  ds = GPReviewDataset(\n",
        "    reviews=df.text.to_numpy(),\n",
        "    targets=df.category.to_numpy(),\n",
        "    tokenizer=tokenizer,\n",
        "    max_len=max_len\n",
        "  )\n",
        "\n",
        "  return DataLoader(\n",
        "    ds,\n",
        "    batch_size=batch_size,\n",
        "    num_workers=4\n",
        "  )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-vWzoo81dvO"
      },
      "source": [
        "###Train_Validation_Test split\n",
        "df_train, df_test = train_test_split(df, test_size=0.1, random_state=RANDOM_SEED)\n",
        "df_val, df_test = train_test_split(df_test, test_size=0.5, random_state=RANDOM_SEED)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xz3ZOQXVPCwh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc8fab88-a2b2-4304-82e3-6af4d7a65bd8"
      },
      "source": [
        "df_train.shape, df_val.shape, df_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((179, 2), (10, 2), (10, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjywGIaES72u"
      },
      "source": [
        "###Randamize Training Sample\n",
        "df_train_Sample1,df_rest= train_test_split(df_train, test_size=0.9, random_state=RANDOM_SEED)\n",
        "df_train_Sample2,df_rest= train_test_split(df_train, test_size=0.8, random_state=RANDOM_SEED)\n",
        "df_train_Sample3,df_rest= train_test_split(df_train, test_size=0.7, random_state=RANDOM_SEED)\n",
        "df_train_Sample4,df_rest= train_test_split(df_train, test_size=0.6, random_state=RANDOM_SEED)\n",
        "df_train_Sample5,df_rest= train_test_split(df_train, test_size=0.5, random_state=RANDOM_SEED)\n",
        "df_train_Sample6,df_rest= train_test_split(df_train, test_size=0.4, random_state=RANDOM_SEED)\n",
        "df_train_Sample7,df_rest= train_test_split(df_train, test_size=0.3, random_state=RANDOM_SEED)\n",
        "df_train_Sample8,df_rest= train_test_split(df_train, test_size=0.2, random_state=RANDOM_SEED)\n",
        "df_train_Sample9,df_rest= train_test_split(df_train, test_size=0.1, random_state=RANDOM_SEED)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93WKoKg4xTl9",
        "outputId": "65c188c1-5a88-4697-85b2-606cdd2b6e45"
      },
      "source": [
        "len(df_train_Sample1),len(df_train_Sample2),len(df_train_Sample3),len(df_train_Sample4),len(df_train_Sample5),len(df_train_Sample6),len(df_train_Sample7),len(df_train_Sample8),len(df_train_Sample9)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17, 35, 53, 71, 89, 107, 125, 143, 161)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vODDxMKsPHqI",
        "outputId": "83ddd7c4-a6e1-467e-93d4-9d1c182c1510",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "BATCH_SIZE = 16\n",
        "\n",
        "train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sL6tD-g1TKLm",
        "outputId": "dddc6ff2-41ed-4226-c2de-4825fb4f9c64",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_data_loader_sample1 = create_data_loader(df_train_Sample1, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "train_data_loader_sample2 = create_data_loader(df_train_Sample2, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "train_data_loader_sample3 = create_data_loader(df_train_Sample3, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "train_data_loader_sample4 = create_data_loader(df_train_Sample4, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "train_data_loader_sample5 = create_data_loader(df_train_Sample5, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "train_data_loader_sample6 = create_data_loader(df_train_Sample6, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "train_data_loader_sample7 = create_data_loader(df_train_Sample7, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "train_data_loader_sample8 = create_data_loader(df_train_Sample8, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "train_data_loader_sample9 = create_data_loader(df_train_Sample9, tokenizer, MAX_LEN, BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgrxwG-CTmsB"
      },
      "source": [
        "##Creatinng list of the training iteration\n",
        "train_loader = []\n",
        "train_loader.append(train_data_loader_sample1)\n",
        "train_loader.append(train_data_loader_sample2)\n",
        "train_loader.append(train_data_loader_sample3)\n",
        "train_loader.append(train_data_loader_sample4)\n",
        "train_loader.append(train_data_loader_sample5)\n",
        "train_loader.append(train_data_loader_sample6)\n",
        "train_loader.append(train_data_loader_sample7)\n",
        "train_loader.append(train_data_loader_sample8)\n",
        "train_loader.append(train_data_loader_sample9)\n",
        "train_loader.append(train_data_loader)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0P41FayISNRI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
          "referenced_widgets": [
            "97fed419b43a4a39b8cb42f755b05688",
            "c0008f61ac6444f0bd4a798ce92d56b1",
            "21eac3099abc44ea813db98dd6b1655c",
            "ef0db30da70f4a5d91d74926d4036252",
            "5626cb627c474e33a10ca960f4ad842b",
            "486a84651175473580a629027e3fe843",
            "3e374e98e725458587a81355e0506eae",
            "3fae087db6f348ed879a5a9260a00518",
            "34ba11cd97b74a8890fbd3aaff488b29",
            "ddfc003cc2834cdc84b372df5a8874a8",
            "514667dd2c524d818641dd03ecddaace"
          ]
        },
        "outputId": "adae30f9-3625-488c-9223-ea8cc9631c4f"
      },
      "source": [
        "##Specify the model\n",
        "bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "97fed419b43a4a39b8cb42f755b05688",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_mRflxPl32F"
      },
      "source": [
        "##the Classifer using AraBert Model\n",
        "class ReadabilityClassifier(nn.Module):\n",
        "\n",
        "  def __init__(self, n_classes):\n",
        "    super(ReadabilityClassifier, self).__init__()\n",
        "    self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)\n",
        "    self.drop = nn.Dropout(p=0.3)\n",
        "    self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\n",
        "  \n",
        "  def forward(self, input_ids, attention_mask):\n",
        "    X= self.bert(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "    _=X[0]\n",
        "    pooled_output=X[1] \n",
        "    output = self.drop(pooled_output)\n",
        "    return self.out(torch.as_tensor(output))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0yQnuSFsjDp",
        "outputId": "7334cd2e-7fcd-4f6c-e9fe-60866e2c65d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "##Set the model to GPU\n",
        "model = ReadabilityClassifier(len(class_names))\n",
        "model = model.to(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9xikRdtRN1N"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5v-ArJ2fCCcU"
      },
      "source": [
        "##parameters, Epochs, optimizer, Learning rate\n",
        "EPOCHS = 2\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=2e-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",
        ")\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss().to(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzl9UhuNx1_Q"
      },
      "source": [
        "def train_epoch(\n",
        "  model, \n",
        "  data_loader, \n",
        "  loss_fn, \n",
        "  optimizer, \n",
        "  device, \n",
        "  scheduler, \n",
        "  n_examples\n",
        "):\n",
        "  model = model.train()\n",
        "\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "  \n",
        "  for d in data_loader:\n",
        "    input_ids = d[\"input_ids\"].to(device)\n",
        "    attention_mask = d[\"attention_mask\"].to(device)\n",
        "    targets = d[\"targets\"].to(device)\n",
        "\n",
        "    outputs = model(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    loss = loss_fn(outputs, targets)\n",
        "\n",
        "    correct_predictions += torch.sum(preds == targets)\n",
        "    losses.append(loss.item())\n",
        "\n",
        "    loss.backward()\n",
        "    nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "    optimizer.step()\n",
        "    scheduler.step()\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXeRorVGIKre"
      },
      "source": [
        "def eval_model(model, data_loader, loss_fn, device, n_examples):\n",
        "  model = model.eval()\n",
        "\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      targets = d[\"targets\"].to(device)\n",
        "\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "      loss = loss_fn(outputs, targets)\n",
        "\n",
        "      correct_predictions += torch.sum(preds == targets)\n",
        "      losses.append(loss.item())\n",
        "\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zhHoFNsxufs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ead4f24a-56a6-4ec5-c831-8a1205ef0ff1"
      },
      "source": [
        "%%time\n",
        "\n",
        "history = defaultdict(list)\n",
        "best_accuracy = 0\n",
        "i=1\n",
        "for epoch in range(EPOCHS):\n",
        "  for sample in train_loader:\n",
        "    \n",
        "    print(f'Epoch {epoch + 1}/{EPOCHS}')\n",
        "    print('-' * 10)\n",
        "    print(f'Sample {i} / {len(train_loader)}')\n",
        "    print('-' * 10)\n",
        "    train_acc, train_loss = train_epoch(\n",
        "        model,\n",
        "        sample,    \n",
        "        loss_fn, \n",
        "        optimizer, \n",
        "        device, \n",
        "        scheduler, \n",
        "        len(df_train)\n",
        "        )\n",
        "\n",
        "    print(f'Train loss {train_loss} accuracy {train_acc}')\n",
        "\n",
        "    val_acc, val_loss = eval_model(\n",
        "        model,\n",
        "        val_data_loader,\n",
        "        loss_fn, \n",
        "        device, \n",
        "        len(df_val)\n",
        "        )\n",
        "\n",
        "    print(f'Val   loss {val_loss} accuracy {val_acc}')\n",
        "    print()\n",
        "\n",
        "    history['train_acc'].append(train_acc)\n",
        "    history['train_loss'].append(train_loss)\n",
        "    history['val_acc'].append(val_acc)\n",
        "    history['val_loss'].append(val_loss)\n",
        "    i=i+1\n",
        "    if val_acc > best_accuracy:\n",
        "      torch.save(model.state_dict(), 'best_model_2Classes_base.bin')\n",
        "      best_accuracy = val_acc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "----------\n",
            "Sample 1 / 10\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss 0.8768296837806702 accuracy 0.0558659217877095\n",
            "Val   loss 0.8253911137580872 accuracy 0.1\n",
            "\n",
            "Epoch 1/2\n",
            "----------\n",
            "Sample 2 / 10\n",
            "----------\n",
            "Train loss 0.7074742515881857 accuracy 0.09497206703910614\n",
            "Val   loss 0.5710329413414001 accuracy 0.9\n",
            "\n",
            "Epoch 1/2\n",
            "----------\n",
            "Sample 3 / 10\n",
            "----------\n",
            "Train loss 0.5655911862850189 accuracy 0.2122905027932961\n",
            "Val   loss 0.3820273280143738 accuracy 0.9\n",
            "\n",
            "Epoch 1/2\n",
            "----------\n",
            "Sample 4 / 10\n",
            "----------\n",
            "Train loss 0.4380781829357147 accuracy 0.30726256983240224\n",
            "Val   loss 0.3127250373363495 accuracy 0.9\n",
            "\n",
            "Epoch 1/2\n",
            "----------\n",
            "Sample 5 / 10\n",
            "----------\n",
            "Train loss 0.44648560136556625 accuracy 0.40782122905027934\n",
            "Val   loss 0.28234535455703735 accuracy 0.9\n",
            "\n",
            "Epoch 1/2\n",
            "----------\n",
            "Sample 6 / 10\n",
            "----------\n",
            "Train loss 0.37691608709948404 accuracy 0.49720670391061456\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 1/2\n",
            "----------\n",
            "Sample 7 / 10\n",
            "----------\n",
            "Train loss 0.34852549992501736 accuracy 0.5754189944134078\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 1/2\n",
            "----------\n",
            "Sample 8 / 10\n",
            "----------\n",
            "Train loss 0.3496984839439392 accuracy 0.6871508379888268\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 1/2\n",
            "----------\n",
            "Sample 9 / 10\n",
            "----------\n",
            "Train loss 0.35453065552494745 accuracy 0.7709497206703911\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 1/2\n",
            "----------\n",
            "Sample 10 / 10\n",
            "----------\n",
            "Train loss 0.3658063216134906 accuracy 0.8100558659217877\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 2/2\n",
            "----------\n",
            "Sample 11 / 10\n",
            "----------\n",
            "Train loss 0.28477679193019867 accuracy 0.0893854748603352\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 2/2\n",
            "----------\n",
            "Sample 12 / 10\n",
            "----------\n",
            "Train loss 0.25350117186705273 accuracy 0.17318435754189945\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 2/2\n",
            "----------\n",
            "Sample 13 / 10\n",
            "----------\n",
            "Train loss 0.24898898974061012 accuracy 0.2737430167597765\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 2/2\n",
            "----------\n",
            "Sample 14 / 10\n",
            "----------\n",
            "Train loss 0.2883396536111832 accuracy 0.3463687150837989\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 2/2\n",
            "----------\n",
            "Sample 15 / 10\n",
            "----------\n",
            "Train loss 0.3370976746082306 accuracy 0.446927374301676\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 2/2\n",
            "----------\n",
            "Sample 16 / 10\n",
            "----------\n",
            "Train loss 0.35940047672816683 accuracy 0.5083798882681564\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 2/2\n",
            "----------\n",
            "Sample 17 / 10\n",
            "----------\n",
            "Train loss 0.3508568946272135 accuracy 0.6033519553072626\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 2/2\n",
            "----------\n",
            "Sample 18 / 10\n",
            "----------\n",
            "Train loss 0.36682158874140847 accuracy 0.6759776536312849\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 2/2\n",
            "----------\n",
            "Sample 19 / 10\n",
            "----------\n",
            "Train loss 0.36497839337045496 accuracy 0.7653631284916201\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "Epoch 2/2\n",
            "----------\n",
            "Sample 20 / 10\n",
            "----------\n",
            "Train loss 0.3573230778177579 accuracy 0.8100558659217877\n",
            "Val   loss 0.2706977128982544 accuracy 0.9\n",
            "\n",
            "CPU times: user 3min 16s, sys: 11.4 s, total: 3min 27s\n",
            "Wall time: 3min 46s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FWG7kBm372V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "9afb90f2-d8b7-498f-b901-b16481452e9b"
      },
      "source": [
        "plt.plot(history['train_acc'], label='train accuracy')\n",
        "plt.plot(history['val_acc'], label='validation accuracy')\n",
        "\n",
        "plt.title('Training history')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Training Sample')\n",
        "plt.legend()\n",
        "plt.ylim([0, 1]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 732,
              "height": 506
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "_wdNu0Ed51La",
        "outputId": "d903b3a5-e48b-45b8-b74f-8124cecd33d5"
      },
      "source": [
        "plt.plot(history['train_loss'], label='train loss')\n",
        "plt.plot(history['val_loss'], label='validation loss')\n",
        "plt.title('Training Loss history')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Training Sample')\n",
        "plt.legend()\n",
        "plt.ylim([0, 1]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 732,
              "height": 506
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3HZb3NWFtFf"
      },
      "source": [
        "## Evaluation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jS3gJ_qBEljD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "631546d0-9a35-4a40-c82d-fa13c11f45f0"
      },
      "source": [
        "test_acc, _ = eval_model(\n",
        "  model,\n",
        "  test_data_loader,\n",
        "  loss_fn,\n",
        "  device,\n",
        "  len(df_test)\n",
        ")\n",
        "\n",
        "test_acc.item()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7000000000000001"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EgR6MuNS8jr_"
      },
      "source": [
        "def get_predictions(model, data_loader):\n",
        "  model = model.eval()\n",
        "  \n",
        "  review_texts = []\n",
        "  predictions = []\n",
        "  prediction_probs = []\n",
        "  real_values = []\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\n",
        "\n",
        "      texts = d[\"review_text\"]\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      targets = d[\"targets\"].to(device)\n",
        "\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "      probs = F.softmax(outputs, dim=1)\n",
        "\n",
        "      review_texts.extend(texts)\n",
        "      predictions.extend(preds)\n",
        "      prediction_probs.extend(probs)\n",
        "      real_values.extend(targets)\n",
        "\n",
        "  predictions = torch.stack(predictions).cpu()\n",
        "  prediction_probs = torch.stack(prediction_probs).cpu()\n",
        "  real_values = torch.stack(real_values).cpu()\n",
        "  return review_texts, predictions, prediction_probs, real_values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JoEIqu9A_uIF",
        "outputId": "38e84d79-2008-4fe6-d964-d98620fb1147",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "##New Prediction Data\n",
        "df_test=pd.read_excel('/content/parallel_Simple_Test.xlsx')\n",
        "test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 925
        },
        "id": "yyZ4KcPaq184",
        "outputId": "388787bc-59da-42c4-a07a-f484a992fa6a"
      },
      "source": [
        "df_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>هذا أمر غير مقبول وأنا أفهم نفاد صبر الناس الذ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>من غير المقبول أن مشروعا من هذا النوع وفرضت عل...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>ويدخر بعض المجالات والبرلمان يجب تسليط الضوء ع...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>(الاجتماع انفض في 12:00)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>كل واحد منا، وأنا واثق، صدمت من جراء هذه المأس...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>لذلك أريد أن أشكركم على العمل الذي تبادل لاطلا...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>ولذا فمن المخيب للآمال.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>وقد اعترف الإذلال المستمر ضد النمسا على الأقل.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>ولكني أود أن أؤكد نقطة أخرى.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>انهم لا يستطيعون العودة الى الوراء.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>نحن بحاجة إلى أن تكون واضحة في هذا الصدد.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>الأمور قد تتغير في هذا الصدد أيضا.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>القصف التركي في شمال العراق.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>فإنه لا يكفي لتدريب القوى العاملة لتلبية احتيا...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>انها ليست في كل نفس الشيء، حتى لا يكون هنا لأش...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>أريد أن يكون واضحا أن، على الرغم من أنني صوت ل...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>ولذلك فقد معلومات قيمة لإدارة المساعدات الجماعة.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0</td>\n",
              "      <td>في الواقع، على مدى السنوات العشر الماضية، ظلت ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>أعتقد أن التغييرات ثلاثة أضعاف.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0</td>\n",
              "      <td>يمكننا القول هنا السرية المصرفية.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "      <td>مجانا.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0</td>\n",
              "      <td>الجميع يبحث عن وسيلة مشرفة.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0</td>\n",
              "      <td>آمل أن يكون هذا سوف يتغير.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0</td>\n",
              "      <td>صناعة تهريب مزدهرة في الواقع.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0</td>\n",
              "      <td>ويمكن تجنب هذه المأساة.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0</td>\n",
              "      <td>لكل فرد الحق من درس معترف بها.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0</td>\n",
              "      <td>الخضر يوافقون بشدة لسياسة رفع أسعار الفائدة ال...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0</td>\n",
              "      <td>ومن مسؤوليتنا.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    label                                               Text\n",
              "0       0  هذا أمر غير مقبول وأنا أفهم نفاد صبر الناس الذ...\n",
              "1       0  من غير المقبول أن مشروعا من هذا النوع وفرضت عل...\n",
              "2       0  ويدخر بعض المجالات والبرلمان يجب تسليط الضوء ع...\n",
              "3       0                           (الاجتماع انفض في 12:00)\n",
              "4       0  كل واحد منا، وأنا واثق، صدمت من جراء هذه المأس...\n",
              "5       0  لذلك أريد أن أشكركم على العمل الذي تبادل لاطلا...\n",
              "6       0                            ولذا فمن المخيب للآمال.\n",
              "7       0     وقد اعترف الإذلال المستمر ضد النمسا على الأقل.\n",
              "8       0                       ولكني أود أن أؤكد نقطة أخرى.\n",
              "9       0                انهم لا يستطيعون العودة الى الوراء.\n",
              "10      0          نحن بحاجة إلى أن تكون واضحة في هذا الصدد.\n",
              "11      0                 الأمور قد تتغير في هذا الصدد أيضا.\n",
              "12      0                       القصف التركي في شمال العراق.\n",
              "13      0  فإنه لا يكفي لتدريب القوى العاملة لتلبية احتيا...\n",
              "14      0  انها ليست في كل نفس الشيء، حتى لا يكون هنا لأش...\n",
              "15      0  أريد أن يكون واضحا أن، على الرغم من أنني صوت ل...\n",
              "16      0   ولذلك فقد معلومات قيمة لإدارة المساعدات الجماعة.\n",
              "17      0  في الواقع، على مدى السنوات العشر الماضية، ظلت ...\n",
              "18      0                    أعتقد أن التغييرات ثلاثة أضعاف.\n",
              "19      0                  يمكننا القول هنا السرية المصرفية.\n",
              "20      0                                             مجانا.\n",
              "21      0                        الجميع يبحث عن وسيلة مشرفة.\n",
              "22      0                         آمل أن يكون هذا سوف يتغير.\n",
              "23      0                      صناعة تهريب مزدهرة في الواقع.\n",
              "24      0                            ويمكن تجنب هذه المأساة.\n",
              "25      0                     لكل فرد الحق من درس معترف بها.\n",
              "26      0  الخضر يوافقون بشدة لسياسة رفع أسعار الفائدة ال...\n",
              "27      0                                     ومن مسؤوليتنا."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHdPZr60-0c_",
        "outputId": "6b582c91-a718-43d2-9b74-aa4918e58262",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(\n",
        "  model,\n",
        "  test_data_loader\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNCOH9rFQTWY",
        "outputId": "bb3cbed9-392b-4eb3-9fdf-a1529123ab49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 925
        }
      },
      "source": [
        "pd.DataFrame(y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1</td>\n",
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            "text/plain": [
              "    0\n",
              "0   1\n",
              "1   1\n",
              "2   1\n",
              "3   0\n",
              "4   1\n",
              "5   1\n",
              "6   1\n",
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              "8   1\n",
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              "24  1\n",
              "25  1\n",
              "26  1\n",
              "27  1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8a9_8-ND3Is",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fefd8168-e5f1-42be-b2f2-1913408d1504"
      },
      "source": [
        "print(classification_report(y_test, y_pred, target_names=class_names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Simple       1.00      0.04      0.07        28\n",
            "     Complex       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.04        28\n",
            "   macro avg       0.50      0.02      0.03        28\n",
            "weighted avg       1.00      0.04      0.07        28\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6d1qxsc__DTh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "outputId": "7c31c593-20b9-4164-d0cb-b0fa0e3c125e"
      },
      "source": [
        "def show_confusion_matrix(confusion_matrix):\n",
        "  hmap = sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "  hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')\n",
        "  hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=30, ha='right')\n",
        "  plt.ylabel('True level')\n",
        "  plt.xlabel('Predicted difficulty');\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "df_cm = pd.DataFrame(cm, index=class_names, columns=class_names)\n",
        "show_confusion_matrix(df_cm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 733,
              "height": 517
            }
          }
        }
      ]
    }
  ]
}