Bert_classification.ipynb

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  "cells": [
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        "!pip install plotly\n"
      ],
      "metadata": {
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            "\u001b[K     |████████████████████████████████| 4.7 MB 6.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 101 kB 12.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 50.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 72.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 316 kB 9.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.4 MB 62.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 233 kB 70.9 MB/s \n",
            "\u001b[?25h  Building wheel for nltk (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for libwapiti (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 53 kB 1.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 175 kB 14.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 235 kB 63.6 MB/s \n",
            "\u001b[?25h  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (5.5.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly) (8.0.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jc5mLEaf_crV"
      },
      "outputs": [],
      "source": [
        "import numpy as np \n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.utils import shuffle\n",
        "from bs4 import BeautifulSoup\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import hazm\n",
        "from hazm import Lemmatizer\n",
        "from cleantext import clean\n",
        "import nltk\n",
        "\n",
        "\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import os\n",
        "import re\n",
        "import json\n",
        "import copy\n",
        "import collections\n",
        "\n",
        "from transformers import BertConfig, BertTokenizer\n",
        "from transformers import TFBertModel, TFBertForSequenceClassification\n",
        "from transformers import glue_convert_examples_to_features\n",
        "\n",
        "import tensorflow as tf\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MAX_LEN = 128\n",
        "TRAIN_BATCH_SIZE = 16\n",
        "VALID_BATCH_SIZE = 16\n",
        "TEST_BATCH_SIZE = 16\n",
        "\n",
        "EPOCHS = 3\n",
        "EEVERY_EPOCH = 1000\n",
        "LEARNING_RATE = 2e-5\n",
        "CLIP = 0.0\n",
        "\n",
        "MODEL_NAME_OR_PATH = 'HooshvareLab/bert-fa-base-uncased'\n",
        "OUTPUT_PATH = '/content/bert-fa-base-uncased-sentiment-taaghceh/pytorch_model.bin'\n",
        "\n",
        "os.makedirs(os.path.dirname(OUTPUT_PATH), exist_ok=True)"
      ],
      "metadata": {
        "id": "A4DepL2e-sZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "height": 39
        },
        "id": "gIO17uiJC5ES",
        "outputId": "6febc48f-697f-45c3-fc42-7d672765498c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3896bd68-4ca4-4c22-ad26-591c26c9ee7c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-3896bd68-4ca4-4c22-ad26-591c26c9ee7c\">\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> "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('dataset1_org.csv',header=None)\n",
        "data.drop(0,inplace=True,axis=1)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "LTUi7ezpDQv5",
        "outputId": "9384ef19-294f-444f-a760-ae7435aa9348"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            1                                                  2\n",
              "0        پرسش        @pejvak313 ارتباطش رو با واکسن توضیح میدین؟\n",
              "1        پرسش  @mamadporii نه میدونی فشار اومده بدجور.۲سال کر...\n",
              "2        پرسش  @ly69204411 سازمان بهداشت جهاني خودش امار ميده...\n",
              "3        پرسش                              @kimnacri واسه کرونا؟\n",
              "4        پرسش  @raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...\n",
              "...       ...                                                ...\n",
              "7263     پرسش  به‌نظر شما عامل اصلی کمبود واردات واکسن #کرونا...\n",
              "7264     خنثی  @amoyepesarshoja @bahar_sh_y منی که عکس با ماس...\n",
              "7265  عصبانیت  به نظرم لیست کشته شده های این مدت در اثر آلودگ...\n",
              "7266       غم                              @kimiyaws 🤦🏻‍♀️ کرونا\n",
              "7267  عصبانیت  میشه عکس با ماسکتونو منشن کنید؟\\nمیشه عکس چشات...\n",
              "\n",
              "[7268 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-692b573f-7252-4440-8538-8122561ebe1d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>1</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@pejvak313 ارتباطش رو با واکسن توضیح میدین؟</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@mamadporii نه میدونی فشار اومده بدجور.۲سال کر...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@ly69204411 سازمان بهداشت جهاني خودش امار ميده...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@kimnacri واسه کرونا؟</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7263</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>به‌نظر شما عامل اصلی کمبود واردات واکسن #کرونا...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7264</th>\n",
              "      <td>خنثی</td>\n",
              "      <td>@amoyepesarshoja @bahar_sh_y منی که عکس با ماس...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7265</th>\n",
              "      <td>عصبانیت</td>\n",
              "      <td>به نظرم لیست کشته شده های این مدت در اثر آلودگ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7266</th>\n",
              "      <td>غم</td>\n",
              "      <td>@kimiyaws 🤦🏻‍♀️ کرونا</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7267</th>\n",
              "      <td>عصبانیت</td>\n",
              "      <td>میشه عکس با ماسکتونو منشن کنید؟\\nمیشه عکس چشات...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7268 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-692b573f-7252-4440-8538-8122561ebe1d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-692b573f-7252-4440-8538-8122561ebe1d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-692b573f-7252-4440-8538-8122561ebe1d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[1].replace('ترس','ترس ',inplace=True)"
      ],
      "metadata": {
        "id": "SnTYXBmiSlXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[1].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpUfEf80T85c",
        "outputId": "29d2ebde-83d5-49e0-95e2-3b690a868745"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['پرسش', 'ترس ', 'تعجب', 'خنثی', 'خنده', 'خوشحالی', 'عصبانیت', 'غم'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('stopwords.txt') as stopwords_file:\n",
        "    stopwords = stopwords_file.readlines()\n",
        "nltk_stopwords = [str(line).replace('\\n', '') for line in stopwords]"
      ],
      "metadata": {
        "id": "tedYnc7Lh1tS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['comment_len_by_words'] = data[2].apply(lambda t: len(hazm.word_tokenize(t)))\n",
        "data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "htct-FWHrBs9",
        "outputId": "e7604e76-937a-4967-f4bd-bf74f2878ac9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            1                                                  2  \\\n",
              "0        پرسش        @pejvak313 ارتباطش رو با واکسن توضیح میدین؟   \n",
              "1        پرسش  @mamadporii نه میدونی فشار اومده بدجور.۲سال کر...   \n",
              "2        پرسش  @ly69204411 سازمان بهداشت جهاني خودش امار ميده...   \n",
              "3        پرسش                              @kimnacri واسه کرونا؟   \n",
              "4        پرسش  @raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...   \n",
              "...       ...                                                ...   \n",
              "7263     پرسش  به‌نظر شما عامل اصلی کمبود واردات واکسن #کرونا...   \n",
              "7264     خنثی  @amoyepesarshoja @bahar_sh_y منی که عکس با ماس...   \n",
              "7265  عصبانیت  به نظرم لیست کشته شده های این مدت در اثر آلودگ...   \n",
              "7266       غم                              @kimiyaws 🤦🏻‍♀️ کرونا   \n",
              "7267  عصبانیت  میشه عکس با ماسکتونو منشن کنید؟\\nمیشه عکس چشات...   \n",
              "\n",
              "      comment_len_by_words  \n",
              "0                        9  \n",
              "1                       57  \n",
              "2                       31  \n",
              "3                        4  \n",
              "4                       65  \n",
              "...                    ...  \n",
              "7263                    15  \n",
              "7264                     9  \n",
              "7265                    27  \n",
              "7266                     3  \n",
              "7267                    33  \n",
              "\n",
              "[7268 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9e8b98d-a6bf-4e9f-a5d9-3f6ca3714362\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>1</th>\n",
              "      <th>2</th>\n",
              "      <th>comment_len_by_words</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@pejvak313 ارتباطش رو با واکسن توضیح میدین؟</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@mamadporii نه میدونی فشار اومده بدجور.۲سال کر...</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@ly69204411 سازمان بهداشت جهاني خودش امار ميده...</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@kimnacri واسه کرونا؟</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7263</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>به‌نظر شما عامل اصلی کمبود واردات واکسن #کرونا...</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7264</th>\n",
              "      <td>خنثی</td>\n",
              "      <td>@amoyepesarshoja @bahar_sh_y منی که عکس با ماس...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7265</th>\n",
              "      <td>عصبانیت</td>\n",
              "      <td>به نظرم لیست کشته شده های این مدت در اثر آلودگ...</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7266</th>\n",
              "      <td>غم</td>\n",
              "      <td>@kimiyaws 🤦🏻‍♀️ کرونا</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7267</th>\n",
              "      <td>عصبانیت</td>\n",
              "      <td>میشه عکس با ماسکتونو منشن کنید؟\\nمیشه عکس چشات...</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7268 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9e8b98d-a6bf-4e9f-a5d9-3f6ca3714362')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e9e8b98d-a6bf-4e9f-a5d9-3f6ca3714362 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e9e8b98d-a6bf-4e9f-a5d9-3f6ca3714362');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_max_len = data[\"comment_len_by_words\"].min(), data[\"comment_len_by_words\"].max()\n",
        "print(f'Min: {min_max_len[0]} \\tMax: {min_max_len[1]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jLV3YwMFNBB",
        "outputId": "38f0f4c6-b6cc-457e-9040-670bf94dadb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min: 2 \tMax: 75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def data_gl_than(data, less_than=100.0, greater_than=0.0, col='comment_len_by_words'):\n",
        "    data_length = data[col].values\n",
        "\n",
        "    data_glt = sum([1 for length in data_length if greater_than < length <= less_than])\n",
        "\n",
        "    data_glt_rate = (data_glt / len(data_length)) * 100\n",
        "\n",
        "    print(f'Texts with word length of greater than {greater_than} and less than {less_than} includes {data_glt_rate:.2f}% of the whole!')"
      ],
      "metadata": {
        "id": "egykeqztF7nV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_gl_than(data, 256, 3)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dOENJFv_LzAj",
        "outputId": "6a30b92f-1f0b-4689-eb2e-a9d78b32dd4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Texts with word length of greater than 3 and less than 256 includes 99.53% of the whole!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove comments with the length of fewer than three words\n",
        "minlim, maxlim = 3, 256\n",
        "\n",
        "data['comment_len_by_words'] = data['comment_len_by_words'].apply(lambda len_t: len_t if minlim < len_t <= maxlim else None)\n",
        "data = data.dropna(subset=['comment_len_by_words'])\n",
        "data = data.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "6L1AJESSL2Ts"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def cleanhtml(raw_html):\n",
        "    cleanr = re.compile('<.*?>')\n",
        "    cleantext = re.sub(cleanr, '', raw_html)\n",
        "    return cleantext\n",
        "\n",
        "def cleaning(text):\n",
        "    text = text.strip()\n",
        "    \n",
        "    # regular cleaning\n",
        "    text = clean(text,\n",
        "        fix_unicode=True,\n",
        "        to_ascii=False,\n",
        "        lower=True,\n",
        "        no_line_breaks=True,\n",
        "        no_urls=True,\n",
        "        no_emails=True,\n",
        "        no_phone_numbers=True,\n",
        "        no_numbers=False,\n",
        "        no_digits=False,\n",
        "        no_currency_symbols=True,\n",
        "        no_punct=False,\n",
        "        replace_with_url=\"\",\n",
        "        replace_with_email=\"\",\n",
        "        replace_with_phone_number=\"\",\n",
        "        replace_with_number=\"\",\n",
        "        replace_with_digit=\"0\",\n",
        "        replace_with_currency_symbol=\"\",\n",
        "    )\n",
        "\n",
        "    # cleaning htmls\n",
        "    text = cleanhtml(text)\n",
        "    \n",
        "    # normalizing\n",
        "    normalizer = hazm.Normalizer()\n",
        "    text = normalizer.normalize(text)\n",
        "    # removing wierd patterns\n",
        "    wierd_pattern = re.compile(\"[\"\n",
        "        u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
        "        u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
        "        u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
        "        u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
        "        u\"\\U00002702-\\U000027B0\"\n",
        "        u\"\\U000024C2-\\U0001F251\"\n",
        "        u\"\\U0001f926-\\U0001f937\"\n",
        "        u'\\U00010000-\\U0010ffff'\n",
        "        u\"\\u200d\"\n",
        "        u\"\\u2640-\\u2642\"\n",
        "        u\"\\u2600-\\u2B55\"\n",
        "        u\"\\u23cf\"\n",
        "        u\"\\u23e9\"\n",
        "        u\"\\u231a\"\n",
        "        u\"\\u3030\"\n",
        "        u\"\\ufe0f\"\n",
        "        u\"\\u2069\"\n",
        "        u\"\\u2066\"\n",
        "        # u\"\\u200c\"\n",
        "        u\"\\u2068\"\n",
        "        u\"\\u2067\"\n",
        "        \"]+\", flags=re.UNICODE)\n",
        "    \n",
        "    text = wierd_pattern.sub(r'', text)\n",
        "    \n",
        "    # removing extra spaces, hashtags\n",
        "    text = re.sub(\"#\", \"\", text)\n",
        "    text = re.sub(\"\\s+\", \" \", text)\n",
        "    \n",
        "    return text"
      ],
      "metadata": {
        "id": "OQ1e6mdPMyNn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ouoiBraKMy3O",
        "outputId": "48c6756d-b044-481e-d0f5-f2e42d669c54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            1                                                  2  \\\n",
              "0        پرسش        @pejvak313 ارتباطش رو با واکسن توضیح میدین؟   \n",
              "1        پرسش  @mamadporii نه میدونی فشار اومده بدجور.۲سال کر...   \n",
              "2        پرسش  @ly69204411 سازمان بهداشت جهاني خودش امار ميده...   \n",
              "3        پرسش                              @kimnacri واسه کرونا؟   \n",
              "4        پرسش  @raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...   \n",
              "...       ...                                                ...   \n",
              "7229       غم  @MahdiarSaeedian این نمونه که خدمتتون عرض کردم...   \n",
              "7230     پرسش  به‌نظر شما عامل اصلی کمبود واردات واکسن #کرونا...   \n",
              "7231     خنثی  @amoyepesarshoja @bahar_sh_y منی که عکس با ماس...   \n",
              "7232  عصبانیت  به نظرم لیست کشته شده های این مدت در اثر آلودگ...   \n",
              "7233  عصبانیت  میشه عکس با ماسکتونو منشن کنید؟\\nمیشه عکس چشات...   \n",
              "\n",
              "      comment_len_by_words  \n",
              "0                      9.0  \n",
              "1                     57.0  \n",
              "2                     31.0  \n",
              "3                      4.0  \n",
              "4                     65.0  \n",
              "...                    ...  \n",
              "7229                  64.0  \n",
              "7230                  15.0  \n",
              "7231                   9.0  \n",
              "7232                  27.0  \n",
              "7233                  33.0  \n",
              "\n",
              "[7234 rows x 3 columns]"
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              "      <td>@pejvak313 ارتباطش رو با واکسن توضیح میدین؟</td>\n",
              "      <td>9.0</td>\n",
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              "      <td>57.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@ly69204411 سازمان بهداشت جهاني خودش امار ميده...</td>\n",
              "      <td>31.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@kimnacri واسه کرونا؟</td>\n",
              "      <td>4.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7229</th>\n",
              "      <td>غم</td>\n",
              "      <td>@MahdiarSaeedian این نمونه که خدمتتون عرض کردم...</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7230</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>به‌نظر شما عامل اصلی کمبود واردات واکسن #کرونا...</td>\n",
              "      <td>15.0</td>\n",
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              "    <tr>\n",
              "      <th>7231</th>\n",
              "      <td>خنثی</td>\n",
              "      <td>@amoyepesarshoja @bahar_sh_y منی که عکس با ماس...</td>\n",
              "      <td>9.0</td>\n",
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              "    <tr>\n",
              "      <th>7232</th>\n",
              "      <td>عصبانیت</td>\n",
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              "      <td>27.0</td>\n",
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              "    <tr>\n",
              "      <th>7233</th>\n",
              "      <td>عصبانیت</td>\n",
              "      <td>میشه عکس با ماسکتونو منشن کنید؟\\nمیشه عکس چشات...</td>\n",
              "      <td>33.0</td>\n",
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    {
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      "source": [
        "data['cleaned_comment'] = data[2].apply(cleaning)\n",
        "\n",
        "# calculate the length of comments based on their words\n",
        "data['cleaned_comment_len_by_words'] = data['cleaned_comment'].apply(lambda t: len(hazm.word_tokenize(t)))\n",
        "\n",
        "# remove comments with the length of fewer than three words\n",
        "data['cleaned_comment_len_by_words'] = data['cleaned_comment_len_by_words'].apply(lambda len_t: len_t if minlim < len_t <= maxlim else len_t)\n",
        "data = data.dropna(subset=['cleaned_comment_len_by_words'])\n",
        "data = data.reset_index(drop=True)\n",
        "\n",
        "data.head()"
      ],
      "metadata": {
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          "height": 206
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        "id": "MxZkQdJ5ftCf",
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              "      1                                                  2  \\\n",
              "0  پرسش        @pejvak313 ارتباطش رو با واکسن توضیح میدین؟   \n",
              "1  پرسش  @mamadporii نه میدونی فشار اومده بدجور.۲سال کر...   \n",
              "2  پرسش  @ly69204411 سازمان بهداشت جهاني خودش امار ميده...   \n",
              "3  پرسش                              @kimnacri واسه کرونا؟   \n",
              "4  پرسش  @raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...   \n",
              "\n",
              "   comment_len_by_words                                    cleaned_comment  \\\n",
              "0                   9.0        @pejvak۳۱۳ ارتباطش رو با واکسن توضیح میدین؟   \n",
              "1                  57.0  @mamadporii نه میدونی فشار اومده بدجور.۲سال کر...   \n",
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              "3                   4.0                              @kimnacri واسه کرونا؟   \n",
              "4                  65.0  @raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...   \n",
              "\n",
              "   cleaned_comment_len_by_words  \n",
              "0                             9  \n",
              "1                            56  \n",
              "2                            30  \n",
              "3                             4  \n",
              "4                            65  "
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              "      <td>57.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>پرسش</td>\n",
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              "      <td>31.0</td>\n",
              "      <td>@ly۶۹۲۰۴۴۱۱ سازمان بهداشت جهانی خودش امار میده...</td>\n",
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              "      <td>@raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...</td>\n",
              "      <td>65.0</td>\n",
              "      <td>@raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "data['tweet_without_stopwords'] = data['cleaned_comment'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stopwords)]))\n"
      ],
      "metadata": {
        "id": "qYawEiI3hKkg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data[[1,'cleaned_comment']]\n",
        "data.columns = ['label','comment']\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uU2e9bFGtkNo",
        "outputId": "91659075-5bcc-4b5e-d8a0-db5030066af7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  label                                            comment\n",
              "0  پرسش        @pejvak۳۱۳ ارتباطش رو با واکسن توضیح میدین؟\n",
              "1  پرسش  @mamadporii نه میدونی فشار اومده بدجور.۲سال کر...\n",
              "2  پرسش  @ly۶۹۲۰۴۴۱۱ سازمان بهداشت جهانی خودش امار میده...\n",
              "3  پرسش                              @kimnacri واسه کرونا؟\n",
              "4  پرسش  @raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو..."
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>comment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@pejvak۳۱۳ ارتباطش رو با واکسن توضیح میدین؟</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@mamadporii نه میدونی فشار اومده بدجور.۲سال کر...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@ly۶۹۲۰۴۴۱۱ سازمان بهداشت جهانی خودش امار میده...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@kimnacri واسه کرونا؟</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...</td>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "groupby_label = data.groupby('label')['label'].count()\n",
        "\n",
        "fig.add_trace(go.Bar(\n",
        "    x=list(sorted(groupby_label.index)),\n",
        "    y=groupby_label.tolist(),\n",
        "    text=groupby_label.tolist(),\n",
        "    textposition='auto'\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title_text='Distribution of label within comments [DATA]',\n",
        "    xaxis_title_text='Label',\n",
        "    yaxis_title_text='Frequency',\n",
        "    bargap=0.2,\n",
        "    bargroupgap=0.2)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "CPiLSRrKtvYm",
        "outputId": "b9ae92de-4614-4811-f8f1-5620f8668cc4"
      },
      "execution_count": null,
      "outputs": [
        {
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    },
    {
      "cell_type": "code",
      "source": [
        "labels = list(sorted(data['label'].unique()))\n",
        "label2id = {label: i for i, label in enumerate(labels)}\n",
        "id2label = {v: k for k, v in label2id.items()}\n",
        "print(f'label2id: {label2id}')\n",
        "print(f'id2label: {id2label}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJ71-4hiHz5n",
        "outputId": "3b1c27f0-edca-47e6-c617-f220868ff9c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label2id: {'ترس ': 0, 'تعجب': 1, 'خنثی': 2, 'خنده': 3, 'خوشحالی': 4, 'عصبانیت': 5, 'غم': 6, 'پرسش': 7}\n",
            "id2label: {0: 'ترس ', 1: 'تعجب', 2: 'خنثی', 3: 'خنده', 4: 'خوشحالی', 5: 'عصبانیت', 6: 'غم', 7: 'پرسش'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data['label_id'] = data['label'].apply(lambda t: labels.index(t))\n",
        "\n",
        "train, test = train_test_split(data, test_size=0.1, random_state=1, stratify=data['label'])\n",
        "train, valid = train_test_split(train, test_size=0.1, random_state=1, stratify=train['label'])\n",
        "\n",
        "train = train.reset_index(drop=True)\n",
        "valid = valid.reset_index(drop=True)\n",
        "test = test.reset_index(drop=True)\n",
        "\n",
        "x_train, y_train = train['comment'].values.tolist(), train['label_id'].values.tolist()\n",
        "x_valid, y_valid = valid['comment'].values.tolist(), valid['label_id'].values.tolist()\n",
        "x_test, y_test = test['comment'].values.tolist(), test['label_id'].values.tolist()\n",
        "\n",
        "print(train.shape)\n",
        "print(valid.shape)\n",
        "print(test.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahbyxh4G82H1",
        "outputId": "3e7e7600-6e4d-4789-91c5-f52cde8b0337"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5859, 3)\n",
            "(651, 3)\n",
            "(724, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(MODEL_NAME_OR_PATH)\n",
        "config = BertConfig.from_pretrained(\n",
        "    MODEL_NAME_OR_PATH, **{\n",
        "        'label2id': label2id,\n",
        "        'id2label': id2label,\n",
        "    })\n",
        "\n",
        "print(config.to_json_string())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 871,
          "referenced_widgets": [
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        "outputId": "1047de57-b9db-4275-cf12-f2e7850ccedc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading vocab.txt:   0%|          | 0.00/1.14M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/440 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e825667d4ee482bb1817646850b6900"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"\\u062a\\u0631\\u0633 \",\n",
            "    \"1\": \"\\u062a\\u0639\\u062c\\u0628\",\n",
            "    \"2\": \"\\u062e\\u0646\\u062b\\u06cc\",\n",
            "    \"3\": \"\\u062e\\u0646\\u062f\\u0647\",\n",
            "    \"4\": \"\\u062e\\u0648\\u0634\\u062d\\u0627\\u0644\\u06cc\",\n",
            "    \"5\": \"\\u0639\\u0635\\u0628\\u0627\\u0646\\u06cc\\u062a\",\n",
            "    \"6\": \"\\u063a\\u0645\",\n",
            "    \"7\": \"\\u067e\\u0631\\u0633\\u0634\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"\\u062a\\u0631\\u0633 \": 0,\n",
            "    \"\\u062a\\u0639\\u062c\\u0628\": 1,\n",
            "    \"\\u062e\\u0646\\u062b\\u06cc\": 2,\n",
            "    \"\\u062e\\u0646\\u062f\\u0647\": 3,\n",
            "    \"\\u062e\\u0648\\u0634\\u062d\\u0627\\u0644\\u06cc\": 4,\n",
            "    \"\\u0639\\u0635\\u0628\\u0627\\u0646\\u06cc\\u062a\": 5,\n",
            "    \"\\u063a\\u0645\": 6,\n",
            "    \"\\u067e\\u0631\\u0633\\u0634\": 7\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.21.1\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 100000\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "I0n2werg6hbB",
        "outputId": "c356b592-92eb-4761-e601-08c7935e4cfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        label                                            comment  label_id\n",
              "0        پرسش        @pejvak۳۱۳ ارتباطش رو با واکسن توضیح میدین؟         7\n",
              "1        پرسش  @mamadporii نه میدونی فشار اومده بدجور.۲سال کر...         7\n",
              "2        پرسش  @ly۶۹۲۰۴۴۱۱ سازمان بهداشت جهانی خودش امار میده...         7\n",
              "3        پرسش                              @kimnacri واسه کرونا؟         7\n",
              "4        پرسش  @raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...         7\n",
              "...       ...                                                ...       ...\n",
              "7229       غم  @mahdiarsaeedian این نمونه که خدمتتون عرض کردم...         6\n",
              "7230     پرسش  به‌نظر شما عامل اصلی کمبود واردات واکسن کرونا ...         7\n",
              "7231     خنثی  @amoyepesarshoja @bahar_sh_y منی که عکس با ماس...         2\n",
              "7232  عصبانیت  به نظرم لیست کشته شده‌های این مدت در اثر آلودگ...         5\n",
              "7233  عصبانیت  میشه عکس با ماسکتونو منشن کنید؟ میشه عکس چشاتو...         5\n",
              "\n",
              "[7234 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f14f59fc-ff62-4a7f-b1a6-f65be697a609\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "    }\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>comment</th>\n",
              "      <th>label_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@pejvak۳۱۳ ارتباطش رو با واکسن توضیح میدین؟</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@mamadporii نه میدونی فشار اومده بدجور.۲سال کر...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@ly۶۹۲۰۴۴۱۱ سازمان بهداشت جهانی خودش امار میده...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@kimnacri واسه کرونا؟</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>@raziehtaheryan کاهش شیب تورم، یه امر طبیعی بو...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7229</th>\n",
              "      <td>غم</td>\n",
              "      <td>@mahdiarsaeedian این نمونه که خدمتتون عرض کردم...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7230</th>\n",
              "      <td>پرسش</td>\n",
              "      <td>به‌نظر شما عامل اصلی کمبود واردات واکسن کرونا ...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7231</th>\n",
              "      <td>خنثی</td>\n",
              "      <td>@amoyepesarshoja @bahar_sh_y منی که عکس با ماس...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7232</th>\n",
              "      <td>عصبانیت</td>\n",
              "      <td>به نظرم لیست کشته شده‌های این مدت در اثر آلودگ...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7233</th>\n",
              "      <td>عصبانیت</td>\n",
              "      <td>میشه عکس با ماسکتونو منشن کنید؟ میشه عکس چشاتو...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7234 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f14f59fc-ff62-4a7f-b1a6-f65be697a609')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f14f59fc-ff62-4a7f-b1a6-f65be697a609 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f14f59fc-ff62-4a7f-b1a6-f65be697a609');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = np.random.randint(0, len(train))\n",
        "sample_comment = train.iloc[idx]['comment']\n",
        "sample_label = train.iloc[idx]['label']\n",
        "\n",
        "print(f'Sample: \\n{sample_comment}\\n{sample_label}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G5CCxZ-9xALy",
        "outputId": "500c6d36-3158-4af1-9146-fa504f5e66b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample: \n",
            "بچای علوم پزشکی کرونا نمیگیرن، فقط ما فرهنگیانیا کرونا میگیریم! من دانشگاه میخواممممممم.\n",
            "غم\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.tokenize(sample_comment)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "print(f'  Comment: {sample_comment}')\n",
        "print(f'Token IDs: {token_ids}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8IxPC_KoxZz9",
        "outputId": "ce1bf442-bf30-4af0-9bac-aa6d12b9415e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Comment: بچای علوم پزشکی کرونا نمیگیرن، فقط ما فرهنگیانیا کرونا میگیریم! من دانشگاه میخواممممممم.\n",
            "Token IDs: [11916, 2784, 4458, 4640, 12646, 29077, 2011, 1348, 3765, 2964, 13456, 2874, 12646, 12109, 1001, 2842, 3363, 17953, 14469, 14469, 14469, 1012]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoding = tokenizer.encode_plus(\n",
        "    sample_comment,\n",
        "    max_length=32,\n",
        "    truncation=True,\n",
        "    add_special_tokens=True, # Add '[CLS]' and '[SEP]'\n",
        "    return_token_type_ids=True,\n",
        "    return_attention_mask=True,\n",
        "    padding='max_length',\n",
        "    return_tensors='pt',  # Return tensors\n",
        ")\n",
        "\n",
        "print(f'Keys: {encoding.keys()}\\n')\n",
        "for k in encoding.keys():\n",
        "    print(f'{k}:\\n{encoding[k]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82LOfP489hXn",
        "outputId": "a083b656-7600-459d-c11d-b3ab7b9fe119"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys: dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])\n",
            "\n",
            "input_ids:\n",
            "tensor([[    2, 11916,  2784,  4458,  4640, 12646, 29077,  2011,  1348,  3765,\n",
            "          2964, 13456,  2874, 12646, 12109,  1001,  2842,  3363, 17953, 14469,\n",
            "         14469, 14469,  1012,     4,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0]])\n",
            "token_type_ids:\n",
            "tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0]])\n",
            "attention_mask:\n",
            "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class InputExample:\n",
        "    \"\"\" A single example for simple sequence classification. \"\"\"\n",
        "\n",
        "    def __init__(self, guid, text_a, text_b=None, label=None):\n",
        "        \"\"\" Constructs a InputExample. \"\"\"\n",
        "        self.guid = guid\n",
        "        self.text_a = text_a\n",
        "        self.text_b = text_b\n",
        "        self.label = label\n",
        "\n",
        "\n",
        "def make_examples(tokenizer, x, y=None, maxlen=128, output_mode=\"classification\", is_tf_dataset=True):\n",
        "    examples = []\n",
        "    y = y if isinstance(y, list) or isinstance(y, np.ndarray) else [None] * len(x)\n",
        "\n",
        "    for i, (_x, _y) in tqdm(enumerate(zip(x, y)), position=0, total=len(x)):\n",
        "        guid = \"%s\" % i\n",
        "        label = int(_y)\n",
        "        \n",
        "        if isinstance(_x, str):\n",
        "            text_a = _x\n",
        "            text_b = None\n",
        "        else:\n",
        "            assert len(_x) == 2\n",
        "            text_a = _x[0]\n",
        "            text_b = _x[1]\n",
        "        \n",
        "        examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n",
        "    \n",
        "    features = glue_convert_examples_to_features(\n",
        "        examples, \n",
        "        tokenizer, \n",
        "        maxlen, \n",
        "        output_mode=output_mode, \n",
        "        label_list=list(np.unique(y)))\n",
        "\n",
        "    all_input_ids = []\n",
        "    all_attention_masks = []\n",
        "    all_token_type_ids = []\n",
        "    all_labels = []\n",
        "\n",
        "    for f in tqdm(features, position=0, total=len(examples)):\n",
        "        if is_tf_dataset:\n",
        "            all_input_ids.append(tf.constant(f.input_ids))\n",
        "            all_attention_masks.append(tf.constant(f.attention_mask))\n",
        "            all_token_type_ids.append(tf.constant(f.token_type_ids))\n",
        "            all_labels.append(tf.constant(f.label))\n",
        "        else:\n",
        "            all_input_ids.append(f.input_ids)\n",
        "            all_attention_masks.append(f.attention_mask)\n",
        "            all_token_type_ids.append(f.token_type_ids)\n",
        "            all_labels.append(f.label)\n",
        "\n",
        "    if is_tf_dataset:\n",
        "        dataset = tf.data.Dataset.from_tensor_slices(({\n",
        "            'input_ids': all_input_ids,\n",
        "            'attention_mask': all_attention_masks,\n",
        "            'token_type_ids': all_token_type_ids\n",
        "        }, all_labels))\n",
        "\n",
        "        return dataset, features\n",
        "    \n",
        "    xdata = [np.array(all_input_ids), np.array(all_attention_masks), np.array(all_token_type_ids)]\n",
        "    ydata = all_labels\n",
        "\n",
        "    return [xdata, ydata], features"
      ],
      "metadata": {
        "id": "rnJtoVpw-Q-L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset_base, train_examples = make_examples(tokenizer, x_train, y_train, maxlen=128)\n",
        "valid_dataset_base, valid_examples = make_examples(tokenizer, x_valid, y_valid, maxlen=128)\n",
        "\n",
        "test_dataset_base, test_examples = make_examples(tokenizer, x_test, y_test, maxlen=128)\n",
        "[xtest, ytest], test_examples = make_examples(tokenizer, x_test, y_test, maxlen=128, is_tf_dataset=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365,
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          ]
        },
        "id": "To0iRNT_BNNs",
        "outputId": "5ef84ca0-6bc2-4896-c175-0a0710168979"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "e2268303f6214772a9d20e25b6f900f1"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/data/processors/glue.py:66: FutureWarning:\n",
            "\n",
            "This function will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
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        },
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            "application/vnd.jupyter.widget-view+json": {
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        {
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "8babcffd5f034d8d84ff8d6e1c5812f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "392ad3b4b3404c8aaa9255bb48deab84"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for value in train_dataset_base.take(1):\n",
        "    print(f'     input_ids: {value[0][\"input_ids\"]}')\n",
        "    print(f'        target: {value[1]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0zRyRkzBTts",
        "outputId": "b15a6307-260d-4442-f6d9-f24e8a8ff2c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     input_ids: [    2  1020 83314  2030     1  1455 24961 51088 25725  2077  2050  1348\n",
            "  3245  1379  3372 13558  1350 48449  2831  3163  1039  2822 12646  3296\n",
            "  1350     4     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0]\n",
            "        target: 7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_training_dataset(dataset, batch_size):\n",
        "    dataset = dataset.repeat()\n",
        "    dataset = dataset.shuffle(2048)\n",
        "    dataset = dataset.batch(batch_size)\n",
        "\n",
        "    return dataset\n",
        "\n",
        "def get_validation_dataset(dataset, batch_size):\n",
        "    dataset = dataset.batch(batch_size)\n",
        "\n",
        "    return dataset"
      ],
      "metadata": {
        "id": "31cCPN_UBW_z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = get_training_dataset(train_dataset_base, TRAIN_BATCH_SIZE)\n",
        "valid_dataset = get_training_dataset(valid_dataset_base, VALID_BATCH_SIZE)\n",
        "\n",
        "train_steps = len(train_examples) // TRAIN_BATCH_SIZE\n",
        "valid_steps = len(valid_examples) // VALID_BATCH_SIZE\n",
        "\n",
        "train_steps, valid_steps"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bGNqxTYBZs7",
        "outputId": "496078fa-5b9a-46cf-8dd7-75db80321695"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(366, 40)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(model_name, config, learning_rate=3e-5):\n",
        "    model = TFBertForSequenceClassification.from_pretrained(model_name, config=config)\n",
        "\n",
        "    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n",
        "    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "    metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')\n",
        "    model.compile(optimizer=optimizer, loss=loss, metrics=[metric])\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "ud3Me1INBbFs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = build_model(MODEL_NAME_OR_PATH, config, learning_rate=LEARNING_RATE)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141,
          "referenced_widgets": [
            "d0807b25376e4a348dccab1f788fade0",
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        },
        "id": "_MVcpfc5BeH0",
        "outputId": "0cf5af9a-79b8-4c02-f3c5-5d6eefd35e93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tf_model.h5:   0%|          | 0.00/919M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0807b25376e4a348dccab1f788fade0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at HooshvareLab/bert-fa-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "r = model.fit(\n",
        "    train_dataset,\n",
        "    validation_data=valid_dataset,\n",
        "    steps_per_epoch=train_steps,\n",
        "    validation_steps=valid_steps,\n",
        "    epochs=EPOCHS,\n",
        "    verbose=1)\n",
        "\n",
        "final_accuracy = r.history['val_accuracy']\n",
        "print('FINAL ACCURACY MEAN: ', np.mean(final_accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9puVlmxVDbbk",
        "outputId": "6f2fa64b-f645-4399-ae33-9761d0c25da7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "366/366 [==============================] - 200s 468ms/step - loss: 1.7883 - accuracy: 0.3578 - val_loss: 1.6226 - val_accuracy: 0.4703\n",
            "Epoch 2/3\n",
            "366/366 [==============================] - 170s 464ms/step - loss: 1.1843 - accuracy: 0.6224 - val_loss: 1.0891 - val_accuracy: 0.6719\n",
            "Epoch 3/3\n",
            "366/366 [==============================] - 170s 463ms/step - loss: 0.5016 - accuracy: 0.8600 - val_loss: 0.7347 - val_accuracy: 0.7937\n",
            "FINAL ACCURACY MEAN:  0.6453124980131785\n",
            "CPU times: user 6min 8s, sys: 58.4 s, total: 7min 6s\n",
            "Wall time: 9min 49s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(os.path.dirname(OUTPUT_PATH))\n"
      ],
      "metadata": {
        "id": "rEJ8KOHsL7Es"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ev = model.evaluate(test_dataset_base.batch(TEST_BATCH_SIZE))\n",
        "print()\n",
        "print(f'Evaluation: {ev}')\n",
        "print()\n",
        "\n",
        "predictions = model.predict(xtest)\n",
        "ypred = predictions[0].argmax(axis=-1).tolist()\n",
        "\n",
        "print()\n",
        "print(classification_report(ytest, ypred, target_names=labels))\n",
        "print()\n",
        "\n",
        "print(f'F1: {f1_score(ytest, ypred, average=\"weighted\")}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Smiv4pMRNFub",
        "outputId": "2b14c531-d518-40fd-b13d-a0a765f527bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "46/46 [==============================] - 8s 161ms/step - loss: 0.8176 - accuracy: 0.7956\n",
            "\n",
            "Evaluation: [0.8175584077835083, 0.7955800890922546]\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        ترس        0.80      0.63      0.71        57\n",
            "        تعجب       0.77      0.74      0.75        68\n",
            "        خنثی       0.82      0.79      0.80       179\n",
            "        خنده       0.94      0.70      0.80        63\n",
            "     خوشحالی       0.72      0.72      0.72        60\n",
            "     عصبانیت       0.70      0.95      0.81        98\n",
            "          غم       0.82      0.84      0.83       118\n",
            "        پرسش       0.85      0.86      0.86        81\n",
            "\n",
            "    accuracy                           0.80       724\n",
            "   macro avg       0.80      0.78      0.78       724\n",
            "weighted avg       0.80      0.80      0.79       724\n",
            "\n",
            "\n",
            "F1: 0.7944161823820468\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MSuxOkhIJtNt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}