ML_NLP_Arabic_Dialect_Identification_in_the_Wild.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/youssefkamil/Arabic-Dialect-Identification/blob/main/ML_NLP_Arabic_Dialect_Identification_in_the_Wild.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sQkYBgFwexY9"
      },
      "source": [
        "#Import Lib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "maDc5c5oH71T"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "from wordcloud import WordCloud\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import matplotlib.pyplot as plt\n",
        "# import arabic_reshaper\n",
        "import nltk\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.naive_bayes import GaussianNB,ComplementNB,MultinomialNB,BernoulliNB\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import make_scorer\n",
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, classification_report\n",
        "from time import time\n",
        "# import arabicnlp "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1DumOuIVUiYi",
        "outputId": "7a2a43f6-60db-4e0f-801a-50827c61460b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#train_predict function"
      ],
      "metadata": {
        "id": "g9OI50Rno3p7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_predict(classifier,X_train,y_train,X_test,y_test, average= 'macro'): \n",
        "    '''\n",
        "\n",
        "    inputs:\n",
        "       - classifier: the learning algorithm to be trained and predicted on\n",
        "       - x_train  : train set Features\n",
        "       - y_train  : train set Labels\n",
        "       - x_test   : test set Features\n",
        "       - y_train  : test set Labels\n",
        " \n",
        "    '''\n",
        "    \n",
        "    results = {}\n",
        "    \n",
        "    # Fit the classifier to the training data and Calculate the time\n",
        "    start_train = time()\n",
        "    classifier = classifier.fit(X_train, y_train)\n",
        "    end_train = time()\n",
        "    results['train_time'] = round((end_train-start_train), 2)\n",
        "\n",
        "    # Predict the training/testing labels and Calculate the time\n",
        "    start_test = time()\n",
        "    y_pred_train = classifier.predict(X_train)\n",
        "    y_pred_test = classifier.predict(X_test)\n",
        "    end_test = time()   \n",
        "    results['pred_time'] = round((end_test-start_test), 2)\n",
        "            \n",
        "    # Model evaluation (training/testing data) using accuracy\n",
        "    results['acc_train'] = round(accuracy_score(y_train, y_pred_train), 2)\n",
        "    results['acc_test'] = round(accuracy_score(y_test, y_pred_test), 2)\n",
        "    \n",
        "    # Model evaluation (training/testing data) using f1-score\n",
        "    results['f1_train'] = round(f1_score(y_train, y_pred_train, average= average), 2)\n",
        "    results['f1_test'] = round(f1_score(y_test, y_pred_test, average= average), 2)\n",
        "    \n",
        "    # Display classification_report\n",
        "    report = classification_report(y_test, y_pred_test)\n",
        "       \n",
        "    \n",
        "    # Return the results\n",
        "    return results, report"
      ],
      "metadata": {
        "id": "CpaVFI1To3p9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkVBUMgEihKV"
      },
      "source": [
        "#**Load cleaned data first approach**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZmGBJg63elx_"
      },
      "outputs": [],
      "source": [
        "clean_df=pd.read_csv('/content/drive/MyDrive/Arabic Dialect/cleaned tweets.csv',lineterminator='\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "35CZYxXxzFQJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "74a4645e-aee5-4b26-cd45-c31a97be4acd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Unnamed: 0            id dialect  \\\n",
              "0                0  1.175358e+18      IQ   \n",
              "1                1  1.175416e+18      IQ   \n",
              "2                2  1.175450e+18      IQ   \n",
              "3                3  1.175471e+18      IQ   \n",
              "4                4  1.175497e+18      IQ   \n",
              "...            ...           ...     ...   \n",
              "458193      458193  1.019485e+18      BH   \n",
              "458194      458194  1.021083e+18      BH   \n",
              "458195      458195  1.017478e+18      BH   \n",
              "458196      458196  1.022430e+18      BH   \n",
              "458197      458197  1.022410e+18      BH   \n",
              "\n",
              "                                                    tweet  \n",
              "0                              لكن بالنهايه  ينتفض  يغير   \n",
              "1        يعني هذا محسوب علي البشر  حيونه ووحشيه  وتطلب...  \n",
              "2                                     مبين من كلامه خليجي  \n",
              "3                               يسلملي مرورك وروحك الحلوه  \n",
              "4                                 وين هل الغيبه  اخ محمد   \n",
              "...                                                   ...  \n",
              "458193                           مبسوطين منك اللي باسطانا  \n",
              "458194                              والله ماينده ابش يختي  \n",
              "458195   شو عملنا لك حنا تهربي مننا احنا مساكين ليش بت...  \n",
              "458196                         الله يبارك فيها وبالعافيه   \n",
              "458197                       السحله ضيفي ي بتطلع لك سحليه  \n",
              "\n",
              "[458198 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-35db7d49-e8b3-4ca2-8f18-d11f9d9a7231\">\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>Unnamed: 0</th>\n",
              "      <th>id</th>\n",
              "      <th>dialect</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1.175358e+18</td>\n",
              "      <td>IQ</td>\n",
              "      <td>لكن بالنهايه  ينتفض  يغير</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1.175416e+18</td>\n",
              "      <td>IQ</td>\n",
              "      <td>يعني هذا محسوب علي البشر  حيونه ووحشيه  وتطلب...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1.175450e+18</td>\n",
              "      <td>IQ</td>\n",
              "      <td>مبين من كلامه خليجي</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1.175471e+18</td>\n",
              "      <td>IQ</td>\n",
              "      <td>يسلملي مرورك وروحك الحلوه</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1.175497e+18</td>\n",
              "      <td>IQ</td>\n",
              "      <td>وين هل الغيبه  اخ محمد</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>458193</th>\n",
              "      <td>458193</td>\n",
              "      <td>1.019485e+18</td>\n",
              "      <td>BH</td>\n",
              "      <td>مبسوطين منك اللي باسطانا</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>458194</th>\n",
              "      <td>458194</td>\n",
              "      <td>1.021083e+18</td>\n",
              "      <td>BH</td>\n",
              "      <td>والله ماينده ابش يختي</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>458195</th>\n",
              "      <td>458195</td>\n",
              "      <td>1.017478e+18</td>\n",
              "      <td>BH</td>\n",
              "      <td>شو عملنا لك حنا تهربي مننا احنا مساكين ليش بت...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>458196</th>\n",
              "      <td>458196</td>\n",
              "      <td>1.022430e+18</td>\n",
              "      <td>BH</td>\n",
              "      <td>الله يبارك فيها وبالعافيه</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>458197</th>\n",
              "      <td>458197</td>\n",
              "      <td>1.022410e+18</td>\n",
              "      <td>BH</td>\n",
              "      <td>السحله ضيفي ي بتطلع لك سحليه</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>458198 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35db7d49-e8b3-4ca2-8f18-d11f9d9a7231')\"\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-35db7d49-e8b3-4ca2-8f18-d11f9d9a7231 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-35db7d49-e8b3-4ca2-8f18-d11f9d9a7231');\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": 4
        }
      ],
      "source": [
        "clean_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tg--8DUPZt9V"
      },
      "outputs": [],
      "source": [
        "clean_df=clean_df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hqe3L2tkWtP1"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X=clean_df['tweet'].values.tolist()\n",
        "y=pd.get_dummies(clean_df['dialect'].values)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1)"
      ],
      "metadata": {
        "id": "_Ov0gP-cYnU9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##ConutVectorizer\n",
        "###ngram_range = (1, 2) , max_features=1,500,000\n"
      ],
      "metadata": {
        "id": "gXYgqpKKno6Q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uizH2jMwYKhc",
        "outputId": "849831c8-fcc8-4b30-e6a5-d1c1d0fe611e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vocabulary :  1500000\n"
          ]
        }
      ],
      "source": [
        "cv = CountVectorizer(binary=False,ngram_range=(1,2),\n",
        "                     max_features=1500000,\n",
        "                    #  min_df=2\n",
        "                     )\n",
        "cv.fit(x_train)\n",
        "print(\"vocabulary : \",len(cv.vocabulary_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyy2rwP0fgPY",
        "outputId": "dacb9e6b-aba4-4057-f1f3-70fdafdd0ed9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'وش': 1282940,\n",
              " 'هالمقارنه': 1175705,\n",
              " 'الزق': 58145,\n",
              " 'مجموعتكم': 938789,\n",
              " 'اسهل': 20712,\n",
              " 'من': 1038986,\n",
              " 'مجموعتنا': 938794,\n",
              " 'يعني': 1618236,\n",
              " 'تلعب': 201589,\n",
              " 'ضد': 290208,\n",
              " 'فرق': 416129,\n",
              " 'سهله': 267598,\n",
              " 'وتقارن': 1222737,\n",
              " 'نفسك': 1147486,\n",
              " 'فيني': 471238,\n",
              " 'ايش': 129992,\n",
              " 'الهبل': 100560,\n",
              " 'هذا': 1177983,\n",
              " 'وش هالمقارنه': 1284178,\n",
              " 'مجموعتكم اسهل': 938790,\n",
              " 'اسهل من': 20730,\n",
              " 'مجموعتنا يعني': 938800,\n",
              " 'يعني تلعب': 1620150,\n",
              " 'تلعب ضد': 201609,\n",
              " 'ضد فرق': 290406,\n",
              " 'فرق سهله': 416315,\n",
              " 'وتقارن نفسك': 1222739,\n",
              " 'نفسك فيني': 1148010,\n",
              " 'فيني ايش': 471361,\n",
              " 'ايش الهبل': 130034,\n",
              " 'الهبل هذا': 100576,\n",
              " 'لا': 643418,\n",
              " 'يسوي': 1589365,\n",
              " 'اجتماع': 5631,\n",
              " 'علشان': 308274,\n",
              " 'يبرز': 1515633,\n",
              " 'هيبته': 1187373,\n",
              " 'لا يسوي': 651627,\n",
              " 'يسوي اجتماع': 1589371,\n",
              " 'يبرز هيبته': 1515644,\n",
              " 'واحد': 1190156,\n",
              " 'ليل': 804356,\n",
              " 'نهار': 1165907,\n",
              " 'بيهاجم': 179647,\n",
              " 'مصر': 1009364,\n",
              " 'ورءيسها': 1261820,\n",
              " 'وحكومتها': 1246787,\n",
              " 'وشعبها': 1287079,\n",
              " 'منتظر': 1047507,\n",
              " 'ايه': 130648,\n",
              " 'الجبله': 46479,\n",
              " 'ده': 242502,\n",
              " 'وهو': 1424477,\n",
              " 'راجع': 247770,\n",
              " 'الخارج': 52074,\n",
              " 'يعملو': 1617568,\n",
              " 'له': 770274,\n",
              " 'تعظيم': 196286,\n",
              " 'سلام': 265181,\n",
              " 'كويس': 634855,\n",
              " 'انهم': 121865,\n",
              " 'فتشوه': 407502,\n",
              " 'بس': 152713,\n",
              " 'واحد ليل': 1190809,\n",
              " 'ليل نهار': 804489,\n",
              " 'نهار بيهاجم': 1166011,\n",
              " 'بيهاجم مصر': 179649,\n",
              " 'مصر ورءيسها': 1009708,\n",
              " 'ورءيسها وحكومتها': 1261829,\n",
              " 'وحكومتها وشعبها': 1246792,\n",
              " 'وشعبها منتظر': 1287133,\n",
              " 'منتظر ايه': 1047509,\n",
              " 'ده وهو': 243342,\n",
              " 'وهو راجع': 1425062,\n",
              " 'راجع من': 247799,\n",
              " 'من الخارج': 1040240,\n",
              " 'يعملو له': 1617676,\n",
              " 'له تعظيم': 771127,\n",
              " 'تعظيم سلام': 196287,\n",
              " 'سلام ده': 265195,\n",
              " 'ده كويس': 243108,\n",
              " 'كويس انهم': 634940,\n",
              " 'فتشوه بس': 407503,\n",
              " 'دامه': 239006,\n",
              " 'بدون': 149818,\n",
              " 'قلوب': 535535,\n",
              " 'المصدر': 92936,\n",
              " 'والله': 1200612,\n",
              " 'هنه': 1183077,\n",
              " 'بدون قلوب': 150110,\n",
              " 'قلوب يعني': 535714,\n",
              " 'يعني المصدر': 1619093,\n",
              " 'ولك': 1366575,\n",
              " 'يخرب': 1554760,\n",
              " 'بيتكم': 174438,\n",
              " 'ايييش': 131470,\n",
              " 'هاظ': 1173670,\n",
              " 'ولك يخرب': 1366948,\n",
              " 'يخرب بيتكم': 1554810,\n",
              " 'هههههههههه': 1184353,\n",
              " 'يلي': 1656804,\n",
              " 'صاير': 284390,\n",
              " 'ليش': 798351,\n",
              " 'نفسنا': 1148785,\n",
              " 'هههههههههه والله': 1184419,\n",
              " 'والله هذا': 1201960,\n",
              " 'هذا يلي': 1179256,\n",
              " 'يلي صاير': 1657720,\n",
              " 'صاير ليش': 284475,\n",
              " 'ليش بنكذبو': 799353,\n",
              " 'منتخب': 1047358,\n",
              " 'يرفع': 1572111,\n",
              " 'الراس': 56267,\n",
              " 'فعلا': 425124,\n",
              " 'ملحمه': 1037050,\n",
              " 'كرويه': 588203,\n",
              " 'تاريخيه': 180698,\n",
              " 'كل': 594821,\n",
              " 'شي': 280428,\n",
              " 'عدل': 302464,\n",
              " 'سويناه': 268610,\n",
              " 'في': 447131,\n",
              " 'هالبطوله': 1174043,\n",
              " 'اقوي': 30242,\n",
              " 'خط': 233401,\n",
              " 'هجوم': 1177448,\n",
              " 'واقوي': 1194996,\n",
              " 'دفاع': 241101,\n",
              " 'والمنتخب': 1203511,\n",
              " 'الوحيد': 102112,\n",
              " 'بكلين': 165670,\n",
              " 'شيت': 282263,\n",
              " 'ومادخل': 1379405,\n",
              " 'فينا': 470255,\n",
              " 'ولا': 1346918,\n",
              " 'هدف': 1177682,\n",
              " 'وهداف': 1420246,\n",
              " 'البطوله': 41403,\n",
              " 'فريقنا': 418808,\n",
              " 'ماشاءلله': 861298,\n",
              " 'تبارك': 181533,\n",
              " 'الله': 79646,\n",
              " 'الحمدلله': 51066,\n",
              " 'رب': 250690,\n",
              " 'العالمين': 68223,\n",
              " 'والله منتخب': 1201885,\n",
              " 'منتخب يرفع': 1047418,\n",
              " 'يرفع الراس': 1572130,\n",
              " 'الراس فعلا': 56285,\n",
              " 'فعلا ملحمه': 426342,\n",
              " 'كرويه تاريخيه': 588214,\n",
              " 'كل شي': 597734,\n",
              " 'شي عدل': 281226,\n",
              " 'في هالبطوله': 461625,\n",
              " 'اقوي خط': 30253,\n",
              " 'خط دفاع': 233430,\n",
              " 'بكلين شيت': 165671,\n",
              " 'ومادخل فينا': 1379406,\n",
              " 'فينا ولا': 470958,\n",
              " 'ولا هدف': 1355790,\n",
              " 'وهداف البطوله': 1420249,\n",
              " 'البطوله من': 41432,\n",
              " 'من فريقنا': 1044882,\n",
              " 'فريقنا ماشاءلله': 418865,\n",
              " 'ماشاءلله تبارك': 861304,\n",
              " 'تبارك الله': 181535,\n",
              " 'الله الحمدلله': 79802,\n",
              " 'الحمدلله رب': 51112,\n",
              " 'رب العالمين': 250733,\n",
              " 'هههههه': 1183679,\n",
              " 'غر': 366830,\n",
              " 'لين': 805532,\n",
              " 'تعيا': 196867,\n",
              " 'غر لين': 366866,\n",
              " 'لين تعيا': 805922,\n",
              " 'اقل': 29671,\n",
              " 'راتب': 247706,\n",
              " 'قطر': 521561,\n",
              " '٢٥٠٠٠': 1693977,\n",
              " 'اكثر': 30933,\n",
              " 'لايتجاوز': 680686,\n",
              " '١٠٠٠': 1691342,\n",
              " 'جنيه': 213315,\n",
              " 'لو': 778313,\n",
              " 'نخلي': 1115017,\n",
              " '٤٠٠٠': 1694878,\n",
              " 'ريال': 256936,\n",
              " 'فقط': 429097,\n",
              " 'للبنزين': 734124,\n",
              " 'ماتفرق': 846538,\n",
              " 'معنا': 1028134,\n",
              " 'اقل راتب': 29690,\n",
              " 'راتب في': 247720,\n",
              " 'في قطر': 458993,\n",
              " 'قطر ٢٥٠٠٠': 523379,\n",
              " '٢٥٠٠٠ اكثر': 1693978,\n",
              " 'في مصر': 460534,\n",
              " 'لايتجاوز ١٠٠٠': 680688,\n",
              " '١٠٠٠ جنيه': 1691348,\n",
              " 'جنيه يعني': 213373,\n",
              " 'يعني لو': 1621687,\n",
              " 'لو نخلي': 784413,\n",
              " 'نخلي ٤٠٠٠': 1115104,\n",
              " '٤٠٠٠ ريال': 1694885,\n",
              " 'ريال فقط': 256972,\n",
              " 'فقط للبنزين': 429640,\n",
              " 'للبنزين ماتفرق': 734127,\n",
              " 'ماتفرق معنا': 846556,\n",
              " 'ربنا': 251217,\n",
              " 'يقدرك': 1635169,\n",
              " 'مره': 974194,\n",
              " 'تكملي': 200744,\n",
              " 'ومتكونش': 1385656,\n",
              " 'دي': 244614,\n",
              " 'اخر': 11559,\n",
              " 'ربنا يقدرك': 251584,\n",
              " 'يقدرك كل': 1635176,\n",
              " 'كل مره': 599093,\n",
              " 'مره تكملي': 975146,\n",
              " 'ومتكونش دي': 1385659,\n",
              " 'دي اخر': 244626,\n",
              " 'اخر مره': 11810,\n",
              " 'ذوقك': 247115,\n",
              " 'رفيع': 254950,\n",
              " 'افضل': 28308,\n",
              " 'مسلسل': 994394,\n",
              " 'شفته': 275013,\n",
              " 'ذوقك رفيع': 247125,\n",
              " 'افضل مسلسل': 28442,\n",
              " 'مسلسل شفته': 994642,\n",
              " 'الطويل': 67269,\n",
              " 'عوير': 351000,\n",
              " 'دا': 237694,\n",
              " 'منو': 1049869,\n",
              " 'يا': 1466775,\n",
              " 'حوته': 229119,\n",
              " 'عوير دا': 351007,\n",
              " 'دا منو': 238008,\n",
              " 'منو يا': 1050315,\n",
              " 'يا حوته': 1469905,\n",
              " 'اسولف': 20865,\n",
              " 'ان': 109167,\n",
              " 'شاء': 269626,\n",
              " 'قبل': 498952,\n",
              " 'لا اسولف': 643763,\n",
              " 'ان شاء': 110146,\n",
              " 'شاء الله': 269631,\n",
              " 'الله قبل': 80714,\n",
              " 'بعد': 159614,\n",
              " 'الكم': 78136,\n",
              " 'الي': 103481,\n",
              " 'سمعته': 266174,\n",
              " 'عن': 322152,\n",
              " 'اختبارات': 10840,\n",
              " 'بحترم': 146957,\n",
              " 'نفسي': 1151174,\n",
              " 'اتادب': 2917,\n",
              " 'بذاكر': 150662,\n",
              " 'شويتين': 280168,\n",
              " 'بعد الكم': 159852,\n",
              " 'الكم الي': 78138,\n",
              " 'الي سمعته': 104302,\n",
              " 'عن اختبارات': 322244,\n",
              " 'بحترم نفسي': 146963,\n",
              " 'نفسي اتادب': 1151191,\n",
              " 'اعتراف': 25367,\n",
              " 'وبعرف': 1209332,\n",
              " 'انو': 122124,\n",
              " 'رح': 252916,\n",
              " 'يجيب': 1539521,\n",
              " 'وجع': 1232392,\n",
              " 'راس': 249072,\n",
              " 'انا': 110764,\n",
              " 'بكره': 165036,\n",
              " 'مبادرون': 903577,\n",
              " 'وبعرف انو': 1209334,\n",
              " 'انو رح': 122372,\n",
              " 'رح يجيب': 253315,\n",
              " 'يجيب وجع': 1539931,\n",
              " 'وجع راس': 1232475,\n",
              " 'راس انا': 249118,\n",
              " 'انا بكره': 111583,\n",
              " 'خلقهوهمحمدافنديلك': 235199,\n",
              " 'الامير': 38246,\n",
              " 'مقاطع': 1034601,\n",
              " 'منتجات': 1047327,\n",
              " 'تركيا': 189706,\n",
              " 'المطلوب': 93435,\n",
              " 'مننا': 1049063,\n",
              " 'كشعب': 590745,\n",
              " 'نقف': 1157203,\n",
              " 'مع': 1010959,\n",
              " 'حكومتنا': 226322,\n",
              " 'ونبدا': 1407107,\n",
              " 'مقاطعه': 1034737,\n",
              " 'جماعيه': 212042,\n",
              " 'مقاطع منتجات': 1034679,\n",
              " 'المطلوب مننا': 93448,\n",
              " 'مننا كشعب': 1049072,\n",
              " 'كشعب نقف': 590792,\n",
              " 'نقف مع': 1157212,\n",
              " 'مع حكومتنا': 1012014,\n",
              " 'ونبدا مقاطعه': 1407112,\n",
              " 'مقاطعه جماعيه': 1034762,\n",
              " 'لتعيشبهالبلد': 688896,\n",
              " 'بدك': 148921,\n",
              " 'تكون': 200774,\n",
              " 'شاهد': 270577,\n",
              " 'ما': 813238,\n",
              " 'شفش': 275207,\n",
              " 'حاجه': 215140,\n",
              " 'لتعيشبهالبلد بدك': 688897,\n",
              " 'بدك تكون': 149006,\n",
              " 'شاهد ما': 270584,\n",
              " 'ما شفش': 824697,\n",
              " 'شفش حاجه': 275208,\n",
              " 'يحليييلك': 1551717,\n",
              " 'منضغط': 1048148,\n",
              " 'ماتتحمل': 840455,\n",
              " 'المزح': 91638,\n",
              " 'يحليييلك منضغط': 1551719,\n",
              " 'ماتتحمل المزح': 840459,\n",
              " 'ههههههههههههههههههههههههههههه': 1184723,\n",
              " 'خالد': 231054,\n",
              " 'مشعل': 1008297,\n",
              " 'يقولك': 1641139,\n",
              " 'محتاجك': 942720,\n",
              " 'يقولك انا': 1641278,\n",
              " 'انا محتاجك': 112872,\n",
              " 'محتاجك يا': 942738,\n",
              " 'يا خالد': 1469982,\n",
              " 'مصيبه': 1010155,\n",
              " 'لما': 756709,\n",
              " 'ناس': 1088840,\n",
              " 'بتكبر': 144786,\n",
              " 'مابتنضج': 835720,\n",
              " 'مصيبه لما': 1010184,\n",
              " 'لما بتتعايش': 757588,\n",
              " 'مع ناس': 1012635,\n",
              " 'ناس بتكبر': 1089476,\n",
              " 'يقهرني': 1639173,\n",
              " 'غير': 377803,\n",
              " 'اللي': 82388,\n",
              " 'شافني': 270269,\n",
              " 'زمان': 258863,\n",
              " 'ويسالني': 1445413,\n",
              " 'انت': 113803,\n",
              " 'حي': 229300,\n",
              " 'ماخذ': 853253,\n",
              " 'اجازه': 5437,\n",
              " 'المقبره': 94602,\n",
              " 'وجاي': 1230820,\n",
              " 'اسلم': 19803,\n",
              " 'عليك': 314241,\n",
              " 'ما يقهرني': 831842,\n",
              " 'يقهرني غير': 1639186,\n",
              " 'غير اللي': 378864,\n",
              " 'اللي ما': 86413,\n",
              " 'ما شافني': 824488,\n",
              " 'شافني من': 270271,\n",
              " 'من زمان': 1043739,\n",
              " 'زمان ويسالني': 259064,\n",
              " 'ويسالني انت': 1445415,\n",
              " 'انت حي': 114421,\n",
              " 'حي لا': 229349,\n",
              " 'لا والله': 650567,\n",
              " 'والله ماخذ': 1201679,\n",
              " 'ماخذ اجازه': 853255,\n",
              " 'اجازه من': 5501,\n",
              " 'من المقبره': 1041615,\n",
              " 'المقبره وجاي': 94604,\n",
              " 'وجاي اسلم': 1230822,\n",
              " 'اسلم عليك': 19807,\n",
              " 'ربي': 251706,\n",
              " 'ايطول': 130305,\n",
              " 'عمرها': 318197,\n",
              " 'ايطول في': 130306,\n",
              " 'في عمرها': 458194,\n",
              " 'قلت': 531557,\n",
              " 'تتكلم': 183655,\n",
              " 'هجمه': 1177422,\n",
              " 'معينه': 1029113,\n",
              " 'والحين': 1197595,\n",
              " 'تقول': 199524,\n",
              " 'حاله': 216928,\n",
              " 'شفيك': 275346,\n",
              " 'السير': 61645,\n",
              " 'فيرجسون': 464440,\n",
              " 'قبل قلت': 500442,\n",
              " 'قلت ما': 532469,\n",
              " 'ما تتكلم': 819588,\n",
              " 'تتكلم عن': 183684,\n",
              " 'عن هجمه': 327738,\n",
              " 'هجمه معينه': 1177429,\n",
              " 'والحين تقول': 1197606,\n",
              " 'تقول تتكلم': 199594,\n",
              " 'عن حاله': 325307,\n",
              " 'يا السير': 1467563,\n",
              " 'راح': 247916,\n",
              " 'اعطيك': 26282,\n",
              " 'بلوك': 167461,\n",
              " 'حتي': 219064,\n",
              " 'اسمع': 20138,\n",
              " 'ردك': 253946,\n",
              " 'المنافق': 95448,\n",
              " 'قتلتونا': 503079,\n",
              " 'بغباءكم': 162616,\n",
              " 'كانكم': 563295,\n",
              " 'شعب': 273500,\n",
              " 'القرن': 75379,\n",
              " 'الطباشيري': 66696,\n",
              " 'راح اعطيك': 248015,\n",
              " 'اعطيك بلوك': 26284,\n",
              " 'بلوك حتي': 167488,\n",
              " 'حتي لا': 219757,\n",
              " 'لا اسمع': 643755,\n",
              " 'قتلتونا بغباءكم': 503083,\n",
              " 'كانكم شعب': 563311,\n",
              " 'شعب من': 273672,\n",
              " 'من القرن': 1041186,\n",
              " 'لكن': 724583,\n",
              " 'ملعبوش': 1037145,\n",
              " 'مزيان': 981108,\n",
              " 'منعرف': 1048386,\n",
              " 'شنو': 277323,\n",
              " 'يوقع': 1682657,\n",
              " 'الشوط': 64131,\n",
              " 'التاني': 43202,\n",
              " 'غالبا': 362705,\n",
              " 'الترجي': 43849,\n",
              " 'فايزه': 404131,\n",
              " 'لكن ملعبوش': 727475,\n",
              " 'مزيان منعرف': 981172,\n",
              " 'منعرف شنو': 1048391,\n",
              " 'شنو يوقع': 277782,\n",
              " 'يوقع الشوط': 1682661,\n",
              " 'الشوط التاني': 64133,\n",
              " 'غالبا الترجي': 362720,\n",
              " 'زوز': 259674,\n",
              " 'احلي': 8895,\n",
              " 'بعض': 161413,\n",
              " 'احلي من': 9188,\n",
              " 'من بعض': 1042251,\n",
              " 'ساندويش': 262499,\n",
              " 'جبنه': 209460,\n",
              " 'شيبس': 282245,\n",
              " 'ليز': 797813,\n",
              " 'ساندويش جبنه': 262500,\n",
              " 'جبنه مع': 209465,\n",
              " 'سو': 267690,\n",
              " 'بتسويه': 143208,\n",
              " 'حياتك': 229404,\n",
              " 'رقبتك': 255024,\n",
              " 'تنزلها': 203480,\n",
              " 'لاحد': 658563,\n",
              " 'فالدنيا': 397382,\n",
              " 'سو الي': 267696,\n",
              " 'في حياتك': 455259,\n",
              " 'حياتك لكن': 229464,\n",
              " 'لكن رقبتك': 726353,\n",
              " 'لا تنزلها': 647008,\n",
              " 'لاحد فالدنيا': 658623,\n",
              " 'انسي': 118226,\n",
              " 'الموضوع': 96632,\n",
              " 'كانت': 560633,\n",
              " 'فقاعه': 428220,\n",
              " 'عملت': 318656,\n",
              " 'صوت': 288675,\n",
              " 'بوقتها': 172944,\n",
              " 'ثم': 207356,\n",
              " 'تلاشت': 201309,\n",
              " 'محد': 945205,\n",
              " 'فاضي': 393124,\n",
              " 'يتابع': 1523021,\n",
              " 'اداره': 13023,\n",
              " 'النادي': 97316,\n",
              " 'مالهم': 881542,\n",
              " 'خلق': 235102,\n",
              " 'هاللويه': 1175497,\n",
              " 'انسي الموضوع': 118239,\n",
              " 'الموضوع كانت': 96775,\n",
              " 'كانت فقاعه': 562125,\n",
              " 'فقاعه عملت': 428228,\n",
              " 'محد فاضي': 945710,\n",
              " 'فاضي يتابع': 393546,\n",
              " 'يتابع الموضوع': 1523064,\n",
              " 'الموضوع من': 96822,\n",
              " 'من اداره': 1039147,\n",
              " 'اداره النادي': 13048,\n",
              " 'مالهم خلق': 881624,\n",
              " 'قصقص': 519108,\n",
              " 'حبي': 218072,\n",
              " 'لايف': 681629,\n",
              " 'امبارح': 107131,\n",
              " 'ماحضرتو': 852411,\n",
              " 'لسا': 701185,\n",
              " 'قلبي': 529920,\n",
              " 'بيوجعني': 179837,\n",
              " 'هاي': 1176203,\n",
              " 'رفيقتي': 254975,\n",
              " 'حضرت': 224145,\n",
              " 'وقت': 1329105,\n",
              " 'شافت': 270237,\n",
              " 'اني': 122737,\n",
              " 'مش': 998034,\n",
              " 'موجوده': 1066529,\n",
              " 'صورتلي': 289120,\n",
              " 'يءبر': 1466534,\n",
              " 'وانا': 1204767,\n",
              " 'بحبككك': 146856,\n",
              " 'هلباااا': 1181581,\n",
              " 'هلباات': 1181588,\n",
              " 'وبموت': 1209797,\n",
              " 'فيك': 465873,\n",
              " 'قصقص حبي': 519153,\n",
              " 'حبي من': 218113,\n",
              " 'من لايف': 1045344,\n",
              " 'لايف امبارح': 681644,\n",
              " 'ماحضرتو لسا': 852413,\n",
              " 'لسا قلبي': 701358,\n",
              " 'قلبي بيوجعني': 530268,\n",
              " 'لايف وقت': 681733,\n",
              " 'وقت شافت': 1330004,\n",
              " 'اني مش': 123391,\n",
              " 'مش موجوده': 1004846,\n",
              " 'موجوده صورتلي': 1066700,\n",
              " 'قصقص يءبر': 519237,\n",
              " 'يءبر قلبي': 1466535,\n",
              " 'قلبي وانا': 531083,\n",
              " 'بحبككك هلباااا': 146857,\n",
              " 'هلباااا هلباات': 1181582,\n",
              " 'هلباات وبموت': 1181589,\n",
              " 'وبموت فيك': 1209798,\n",
              " 'التجمع': 43431,\n",
              " 'الاول': 39305,\n",
              " 'منور': 1050545,\n",
              " 'اهو': 124702,\n",
              " 'التجمع الاول': 43433,\n",
              " 'منور اهو': 1050596,\n",
              " 'تربيه': 188894,\n",
              " 'ال': 32267,\n",
              " 'سلول': 265742,\n",
              " 'حسافه': 223134,\n",
              " 'علي': 309001,\n",
              " 'الحرمين': 49292,\n",
              " 'فبلادكم': 405067,\n",
              " 'وانتم': 1206071,\n",
              " 'عديمين': 302758,\n",
              " 'اخلاق': 12192,\n",
              " 'وتربيه': 1213814,\n",
              " 'ال سلول': 32420,\n",
              " 'حسافه علي': 223139,\n",
              " 'علي الحرمين': 309745,\n",
              " 'فبلادكم وانتم': 405069,\n",
              " 'اخلاق وتربيه': 12218,\n",
              " 'عيـش': 355646,\n",
              " 'وا': 1188950,\n",
              " 'علـي': 308640,\n",
              " 'مبـدا': 910013,\n",
              " 'تكـون': 200499,\n",
              " 'اخـر': 12152,\n",
              " 'مـره': 1030145,\n",
              " 'لنـا': 769153,\n",
              " 'بعـض': 161941,\n",
              " 'لانـه': 674197,\n",
              " 'يـوم': 1628212,\n",
              " 'الايـام': 39790,\n",
              " 'فعـلا': 424779,\n",
              " 'عيـش وا': 355647,\n",
              " 'مبـدا مكـن': 910014,\n",
              " 'تكـون اخـر': 200500,\n",
              " 'اخـر مـره': 12155,\n",
              " 'مـره لنـا': 1030148,\n",
              " 'لنـا مع': 769158,\n",
              " 'لانـه يـوم': 674198,\n",
              " 'يـوم من': 1628239,\n",
              " 'مـره فعـلا': 1030146,\n",
              " 'هو': 1184787,\n",
              " 'جابه': 207973,\n",
              " 'لنفسه': 769434,\n",
              " 'هو اللي': 1185038,\n",
              " 'اللي جابه': 84497,\n",
              " 'جابه لنفسه': 207976,\n",
              " 'دواء': 243474,\n",
              " 'لسرطان': 702753,\n",
              " 'الدم': 54550,\n",
              " 'اسمه': 20358,\n",
              " 'جلافيك': 211648,\n",
              " 'سعر': 264296,\n",
              " 'العلبه': 70087,\n",
              " 'الف': 72295,\n",
              " 'موجود': 1065802,\n",
              " 'مجانا': 935683,\n",
              " 'معاه': 1014242,\n",
              " 'الروشته': 57478,\n",
              " 'ومعاه': 1395591,\n",
              " 'يثبت': 1535056,\n",
              " 'مريض': 977917,\n",
              " 'سرطان': 263895,\n",
              " 'يتصل': 1528404,\n",
              " 'بلاش': 166336,\n",
              " 'شير': 282400,\n",
              " 'خد': 232018,\n",
              " 'عشان': 304494,\n",
              " 'يظهر': 1609183,\n",
              " 'لاكبر': 669510,\n",
              " 'عدد': 302361,\n",
              " 'اللهم': 82172,\n",
              " 'اشفي': 21959,\n",
              " 'في دواء': 455821,\n",
              " 'لسرطان الدم': 702754,\n",
              " 'اسمه جلافيك': 20404,\n",
              " 'جلافيك سعر': 211649,\n",
              " 'سعر العلبه': 264312,\n",
              " 'العلبه الف': 70088,\n",
              " 'الف جنيه': 72335,\n",
              " 'جنيه موجود': 213362,\n",
              " 'موجود مجانا': 1066276,\n",
              " 'مجانا اللي': 935689,\n",
              " 'اللي معاه': 86835,\n",
              " 'معاه الروشته': 1014338,\n",
              " 'الروشته ومعاه': 57479,\n",
              " 'ومعاه ما': 1395633,\n",
              " 'ما يثبت': 830048,\n",
              " 'يثبت انو': 1535081,\n",
              " 'انو مريض': 122535,\n",
              " 'مريض سرطان': 978023,\n",
              " 'سرطان يتصل': 263901,\n",
              " 'يتصل علي': 1528432,\n",
              " 'علي بلاش': 311126,\n",
              " 'خد عشان': 232030,\n",
              " 'عشان يظهر': 305717,\n",
              " 'يظهر لاكبر': 1609251,\n",
              " 'لاكبر عدد': 669519,\n",
              " 'عدد اللهم': 302389,\n",
              " 'اللهم اشفي': 82198,\n",
              " 'اشفي كل': 21961,\n",
              " 'كل مريض': 599096,\n",
              " 'معقولشينهار': 1023750,\n",
              " 'تشتي': 192481,\n",
              " 'اب': 251,\n",
              " 'وتتلج': 1211088,\n",
              " 'كلاوالشمس': 604294,\n",
              " 'بلحظه': 166747,\n",
              " 'الغياب': 72070,\n",
              " 'توقف': 205473,\n",
              " 'جمعه': 212280,\n",
              " 'معقولشينهار تشتي': 1023757,\n",
              " 'كلاوالشمس بلحظه': 604295,\n",
              " 'النصرالاتفاق': 99140,\n",
              " 'الكلام': 77745,\n",
              " 'معني': 1028553,\n",
              " 'للي': 754633,\n",
              " 'تري': 190086,\n",
              " 'مابقي': 837230,\n",
              " 'لنا': 766740,\n",
              " 'الا': 32618,\n",
              " 'الكوره': 78389,\n",
              " 'نستمتع': 1123432,\n",
              " 'فيها': 476832,\n",
              " 'هي': 1186573,\n",
              " 'كرهتونا': 587703,\n",
              " 'معني للي': 1028599,\n",
              " 'للي هنااااااااااك': 755316,\n",
              " 'مابقي لنا': 837269,\n",
              " 'لنا الا': 766814,\n",
              " 'الا الكوره': 32806,\n",
              " 'نستمتع فيها': 1123448,\n",
              " 'فيها حتي': 478429,\n",
              " 'حتي هي': 219888,\n",
              " 'كرهتونا فيها': 587712,\n",
              " 'ماعرفت': 866982,\n",
              " 'الشوق': 64145,\n",
              " 'حيل': 230175,\n",
              " 'موجع': 1065555,\n",
              " 'اشتقت': 21507,\n",
              " 'وكل': 1341870,\n",
              " 'عروقي': 303665,\n",
              " 'اوجعتني': 126572,\n",
              " 'ماعرفت ان': 867009,\n",
              " 'ان الشوق': 109520,\n",
              " 'حيل موجع': 230200,\n",
              " 'موجع لين': 1065569,\n",
              " 'لين اشتقت': 805589,\n",
              " 'اشتقت له': 21523,\n",
              " 'له وكل': 772835,\n",
              " 'وكل عروقي': 1342447,\n",
              " 'عروقي اوجعتني': 303666,\n",
              " 'عندنا': 334635,\n",
              " 'سنه': 266774,\n",
              " 'هذا عندنا': 1178848,\n",
              " 'عندنا قبل': 335738,\n",
              " 'قبل سنه': 500233,\n",
              " 'كرسينا': 586212,\n",
              " 'وقهوتنا': 1336632,\n",
              " 'جرحك': 210961,\n",
              " 'انتي': 116699,\n",
              " 'ماتدرين': 842504,\n",
              " 'ليه': 807377,\n",
              " 'احتار': 6886,\n",
              " 'واشكي': 1193555,\n",
              " 'وانتي': 1206253,\n",
              " 'دايم': 239239,\n",
              " 'تساليني': 190795,\n",
              " 'تضحك': 194054,\n",
              " 'ماعمرك': 868244,\n",
              " 'سالتي': 262142,\n",
              " 'تبكي': 182144,\n",
              " 'كرسينا وقهوتنا': 586213,\n",
              " 'وقهوتنا جرحك': 1336634,\n",
              " 'انتي ماتدرين': 117072,\n",
              " 'ماتدرين ليه': 842510,\n",
              " 'ليه احتار': 807422,\n",
              " 'تساليني ليه': 190797,\n",
              " 'ليه تضحك': 808147,\n",
              " 'تضحك والله': 194082,\n",
              " 'ماعمرك سالتي': 868249,\n",
              " 'ليه تبكي': 808075,\n",
              " 'هاتي': 1172901,\n",
              " 'شويه': 280179,\n",
              " 'ملوخيه': 1037451,\n",
              " 'اقولك': 30103,\n",
              " 'اقتراحات': 29001,\n",
              " 'وانا اقولك': 1204890,\n",
              " 'خوش': 236606,\n",
              " 'بالعراق': 137043,\n",
              " 'اكو': 31735,\n",
              " 'قاعده': 489183,\n",
              " 'شعبيه': 273802,\n",
              " 'للجزاءر': 736315,\n",
              " 'خوش والله': 236665,\n",
              " 'بالعراق اكو': 137044,\n",
              " 'قاعده شعبيه': 489620,\n",
              " 'كده': 580398,\n",
              " 'ياكبير': 1500497,\n",
              " 'ليه كده': 808756,\n",
              " 'كده بس': 580853,\n",
              " 'التوجه': 45394,\n",
              " 'اليديد': 105216,\n",
              " 'الظهور': 67723,\n",
              " 'شيله': 282619,\n",
              " 'عباه': 301380,\n",
              " 'عقب': 307157,\n",
              " 'كمن': 622257,\n",
              " 'بنفقد': 170706,\n",
              " 'هويتنا': 1186556,\n",
              " 'فاللبس': 399659,\n",
              " 'رحم': 253606,\n",
              " 'هذا التوجه': 1178136,\n",
              " 'شيله ولا': 282627,\n",
              " 'ولا عباه': 1353016,\n",
              " 'كمن سنه': 622270,\n",
              " 'فاللبس الا': 399660,\n",
              " 'الا من': 33308,\n",
              " 'من رحم': 1043611,\n",
              " 'رحم ربي': 253610,\n",
              " 'خليط': 235789,\n",
              " 'حواليج': 229069,\n",
              " 'وامج': 1204472,\n",
              " 'وابوج': 1189294,\n",
              " 'واللي': 1202390,\n",
              " 'فيهم': 481370,\n",
              " 'منج': 1047613,\n",
              " 'حلق': 226962,\n",
              " 'الانسان': 38685,\n",
              " 'ليءثو': 794443,\n",
              " 'ويتاثر': 1436704,\n",
              " 'هذه': 1179335,\n",
              " 'فطره': 423840,\n",
              " 'اللتي': 79245,\n",
              " 'فطر': 423773,\n",
              " 'الناس': 97433,\n",
              " 'عليها': 316103,\n",
              " 'خليط من': 235791,\n",
              " 'من اللي': 1041355,\n",
              " 'واللي حواليج': 1202496,\n",
              " 'فيهم منج': 482537,\n",
              " 'ليءثو ويتاثر': 794444,\n",
              " 'ويتاثر هذه': 1436711,\n",
              " 'فطره الانسان': 423842,\n",
              " 'فطر الناس': 423774,\n",
              " 'الناس عليها': 98077,\n",
              " 'مگرود': 1084664,\n",
              " 'مسكين': 993751,\n",
              " 'وراح': 1263060,\n",
              " 'ينفگر': 1673189,\n",
              " 'مگرود مسكين': 1084665,\n",
              " 'مسكين وراح': 994072,\n",
              " 'وراح ينفگر': 1263542,\n",
              " 'دايما': 239306,\n",
              " 'مبسوطه': 909385,\n",
              " 'الله دايما': 80367,\n",
              " 'دايما مبسوطه': 239438,\n",
              " 'مبسوطه رب': 909433,\n",
              " 'خلاص': 234209,\n",
              " 'توا': 204623,\n",
              " 'نخطهم': 1114763,\n",
              " 'ونطلقهم': 1413436,\n",
              " 'السبت': 59285,\n",
              " 'هههههههه': 1184074,\n",
              " 'خلاص توا': 234301,\n",
              " 'نخطهم كل': 1114764,\n",
              " 'كل جمعه': 597025,\n",
              " 'ونطلقهم السبت': 1413437,\n",
              " 'السبت هههههههه': 59310,\n",
              " 'انتخبت': 115671,\n",
              " 'برجل': 151281,\n",
              " 'نخيته': 1115326,\n",
              " 'يط': 1604272,\n",
              " 'بخ': 147832,\n",
              " 'راسه': 249305,\n",
              " 'صطر': 287356,\n",
              " 'منا': 1046919,\n",
              " 'ومنا': 1401198,\n",
              " 'نسل': 1124520,\n",
              " 'قوم': 543886,\n",
              " 'المعارك': 93606,\n",
              " 'مستميته': 989460,\n",
              " 'الزعيم': 58083,\n",
              " 'عزومه': 303982,\n",
              " 'ماتثنا': 840900,\n",
              " 'لي': 790229,\n",
              " 'بدا': 148409,\n",
              " 'اللازم': 79060,\n",
              " 'نصيته': 1133887,\n",
              " 'سهوم': 267653,\n",
              " 'المعركه': 93775,\n",
              " 'زبن': 257962,\n",
              " 'المجنا': 90116,\n",
              " 'مانسيت': 886006,\n",
              " 'وفعله': 1323502,\n",
              " 'مانسيته': 886035,\n",
              " 'الوكاد': 103171,\n",
              " 'انه': 120240,\n",
              " 'العليان': 70205,\n",
              " 'حنا': 228574,\n",
              " 'عوضالعلياني': 350432,\n",
              " 'ما انتخبت': 815686,\n",
              " 'اللي برجل': 83328,\n",
              " 'برجل لا': 151282,\n",
              " 'لا نخيته': 650112,\n",
              " 'نخيته يط': 1115329,\n",
              " 'يط بخ': 1604273,\n",
              " 'بخ راسه': 147833,\n",
              " 'راسه صطر': 249327,\n",
              " 'صطر منا': 287357,\n",
              " 'منا ومنا': 1046982,\n",
              " 'ومنا نسل': 1401221,\n",
              " 'نسل قوم': 1124544,\n",
              " 'قوم في': 544023,\n",
              " 'في المعارك': 451908,\n",
              " 'المعارك مستميته': 93607,\n",
              " 'مستميته الزعيم': 989461,\n",
              " 'الزعيم اللي': 58091,\n",
              " 'اللي عزومه': 85753,\n",
              " 'عزومه ماتثنا': 303986,\n",
              " 'ماتثنا حي': 840901,\n",
              " 'حي راس': 229326,\n",
              " 'راس لي': 249154,\n",
              " 'لي بدا': 791047,\n",
              " 'بدا اللازم': 148415,\n",
              " 'اللازم نصيته': 79064,\n",
              " 'نصيته من': 1133890,\n",
              " 'من سهوم': 1043909,\n",
              " 'سهوم المعركه': 267654,\n",
              " 'المعركه زبن': 93777,\n",
              " 'زبن المجنا': 257963,\n",
              " 'المجنا مانسيت': 90117,\n",
              " 'مانسيت اسمه': 886009,\n",
              " 'اسمه وفعله': 20507,\n",
              " 'وفعله مانسيته': 1323503,\n",
              " 'مانسيته الوكاد': 886036,\n",
              " 'الوكاد انه': 103174,\n",
              " 'انه من': 121202,\n",
              " 'من العليان': 1040968,\n",
              " 'العليان حنا': 70206,\n",
              " 'حنا عوضالعلياني': 228645,\n",
              " 'يارب': 1489346,\n",
              " 'ارزقه': 16197,\n",
              " 'الشهاده': 63887,\n",
              " 'عاجلا': 297151,\n",
              " 'اجل': 5891,\n",
              " 'يارب ارزقه': 1489439,\n",
              " 'عاجلا غير': 297154,\n",
              " 'غير اجل': 377840,\n",
              " 'هاند': 1176127,\n",
              " 'ميد': 1077306,\n",
              " 'رسم': 254352,\n",
              " 'يدوي': 1564715,\n",
              " 'هاند ميد': 1176129,\n",
              " 'ميد رسم': 1077314,\n",
              " 'اثنين': 5301,\n",
              " 'طلعوا': 294876,\n",
              " 'والباجي': 1196249,\n",
              " 'مجرد': 937189,\n",
              " 'ارقام': 16503,\n",
              " 'اثنين بس': 5307,\n",
              " 'بس اللي': 153326,\n",
              " 'اللي طلعوا': 85635,\n",
              " 'مجرد ارقام': 937214,\n",
              " 'فيه': 471868,\n",
              " 'اجمل': 6040,\n",
              " 'دايرها': 239201,\n",
              " 'للتليفون': 735585,\n",
              " 'ديالي': 245522,\n",
              " 'فيه اجمل': 471928,\n",
              " 'للتليفون ديالي': 735586,\n",
              " 'اتفق': 4253,\n",
              " 'محتاجين': 943177,\n",
              " 'لاعبين': 667365,\n",
              " 'جوده': 213926,\n",
              " 'وامكانيات': 1204534,\n",
              " 'نحن': 1112289,\n",
              " 'دكه': 241736,\n",
              " 'ماعندنا': 868793,\n",
              " 'توهقنا': 205861,\n",
              " 'نزلنا': 1120792,\n",
              " 'دياكيه': 245433,\n",
              " 'محتاجين لاعبين': 943351,\n",
              " 'لاعبين جوده': 667490,\n",
              " 'نحن حتي': 1112543,\n",
              " 'حتي دكه': 219570,\n",
              " 'ماعندنا لو': 868860,\n",
              " 'لو توهقنا': 781057,\n",
              " 'نزلنا دياكيه': 1120810,\n",
              " 'انتو': 116288,\n",
              " 'الخسرانين': 52661,\n",
              " 'ماراح': 856283,\n",
              " 'تشوفون': 193116,\n",
              " 'ابد': 543,\n",
              " 'قمم': 537541,\n",
              " 'وجمالها': 1233452,\n",
              " 'انتو الخسرانين': 116311,\n",
              " 'ماراح تشوفون': 856463,\n",
              " 'قمم التلال': 537542,\n",
              " 'يبارك': 1513739,\n",
              " 'عدول': 302679,\n",
              " 'الله يبارك': 81388,\n",
              " 'يبارك فيك': 1513845,\n",
              " 'فيك عدول': 466703,\n",
              " 'امس': 107768,\n",
              " 'واليوم': 1204381,\n",
              " 'كنت': 624270,\n",
              " 'بدوره': 149782,\n",
              " 'الكفايات': 77408,\n",
              " 'رءساء': 247328,\n",
              " 'اقسام': 29455,\n",
              " 'ومعلمين': 1396533,\n",
              " 'ويبون': 1436361,\n",
              " 'نمتب': 1162488,\n",
              " 'سلبيات': 265384,\n",
              " 'وايجابيات': 1207204,\n",
              " 'سلبياته': 265388,\n",
              " 'يحسون': 1546761,\n",
              " 'امس واليوم': 107864,\n",
              " 'كنت بدوره': 625084,\n",
              " 'ومعلمين ويبون': 1396538,\n",
              " 'ويبون منا': 1436383,\n",
              " 'نمتب سلبيات': 1162489,\n",
              " 'سلبيات وايجابيات': 265387,\n",
              " 'اكثر بس': 30986,\n",
              " 'بس ان': 153443,\n",
              " 'يسلمك': 1586470,\n",
              " 'اخوي': 12597,\n",
              " 'محمد': 951237,\n",
              " 'الله يسلمك': 81742,\n",
              " 'يسلمك اخوي': 1586479,\n",
              " 'اخوي محمد': 12727,\n",
              " 'جميل': 212659,\n",
              " 'علاج': 307848,\n",
              " 'المعده': 93755,\n",
              " 'حاليا': 217226,\n",
              " 'كلنا': 609650,\n",
              " 'منعاني': 1048370,\n",
              " 'منه': 1049100,\n",
              " 'فنجان': 439825,\n",
              " 'قهوه': 539784,\n",
              " 'ساده': 261375,\n",
              " 'عصره': 306227,\n",
              " 'ليمون': 805444,\n",
              " 'بالشفا': 136431,\n",
              " 'وجع المعده': 1232423,\n",
              " 'اللي حاليا': 84657,\n",
              " 'حاليا كلنا': 217268,\n",
              " 'كلنا منعاني': 610353,\n",
              " 'فنجان قهوه': 439836,\n",
              " 'قهوه ساده': 539949,\n",
              " 'ساده مع': 261379,\n",
              " 'مع عصره': 1012332,\n",
              " 'عصره ليمون': 306228,\n",
              " 'ليمون بالشفا': 805449,\n",
              " 'ساتر': 261288,\n",
              " 'كثر': 576090,\n",
              " 'التليف': 45066,\n",
              " 'الواحد': 101387,\n",
              " 'يطلع': 1606069,\n",
              " 'حايشته': 217509,\n",
              " 'يا ساتر': 1470933,\n",
              " 'ساتر من': 261293,\n",
              " 'من كثر': 1045207,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "cv.vocabulary_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ky_L5sovZNsb"
      },
      "outputs": [],
      "source": [
        "x_train_cv = cv.transform(x_train)\n",
        "x_test_cv = cv.transform(x_test) "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Models training"
      ],
      "metadata": {
        "id": "LdPhaAW0Lxpc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf_MNB = MultinomialNB()\n",
        "clf_LR = LogisticRegression()\n",
        "clf_SVM=SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3, random_state=42,\n",
        "                      # max_iter=100,\n",
        "                      tol=None)\n",
        "clf_list = [ clf_MNB, clf_LR, clf_SVM]\n",
        "\n",
        "# Compare evaluation of the different classifiers\n",
        "clf_evaluation = {}\n",
        "clf_report = {}\n",
        "\n",
        "for clf in clf_list:\n",
        "    clf_evaluation[clf.__class__.__name__], clf_report[clf.__class__.__name__] = train_predict(clf,\n",
        "                                                                                              x_train_cv,\n",
        "                                                                                              np.argmax(y_train.to_numpy(), axis=1),\n",
        "                                                                                              x_test_cv,\n",
        "                                                                                              np.argmax(y_test.to_numpy(), axis=1)\n",
        "                                                                                              )\n",
        "    print(clf_evaluation )"
      ],
      "metadata": {
        "id": "YT3b43KqL8kE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9e9e2ff-9b5d-4549-f47f-0c1ab39aba81"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'MultinomialNB': {'train_time': 2.48, 'pred_time': 1.06, 'acc_train': 0.74, 'acc_test': 0.47, 'f1_train': 0.69, 'f1_test': 0.38}}\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'MultinomialNB': {'train_time': 2.48, 'pred_time': 1.06, 'acc_train': 0.74, 'acc_test': 0.47, 'f1_train': 0.69, 'f1_test': 0.38}, 'LogisticRegression': {'train_time': 711.66, 'pred_time': 0.95, 'acc_train': 0.87, 'acc_test': 0.53, 'f1_train': 0.86, 'f1_test': 0.5}}\n",
            "{'MultinomialNB': {'train_time': 2.48, 'pred_time': 1.06, 'acc_train': 0.74, 'acc_test': 0.47, 'f1_train': 0.69, 'f1_test': 0.38}, 'LogisticRegression': {'train_time': 711.66, 'pred_time': 0.95, 'acc_train': 0.87, 'acc_test': 0.53, 'f1_train': 0.86, 'f1_test': 0.5}, 'SGDClassifier': {'train_time': 4067.53, 'pred_time': 1.9, 'acc_train': 0.76, 'acc_test': 0.5, 'f1_train': 0.75, 'f1_test': 0.45}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_evaluation"
      ],
      "metadata": {
        "id": "S_oWEodcN9r6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d482143-7502-4b40-9ba2-c2ed5e731078"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'LogisticRegression': {'acc_test': 0.53,\n",
              "  'acc_train': 0.87,\n",
              "  'f1_test': 0.5,\n",
              "  'f1_train': 0.86,\n",
              "  'pred_time': 0.95,\n",
              "  'train_time': 711.66},\n",
              " 'MultinomialNB': {'acc_test': 0.47,\n",
              "  'acc_train': 0.74,\n",
              "  'f1_test': 0.38,\n",
              "  'f1_train': 0.69,\n",
              "  'pred_time': 1.06,\n",
              "  'train_time': 2.48},\n",
              " 'SGDClassifier': {'acc_test': 0.5,\n",
              "  'acc_train': 0.76,\n",
              "  'f1_test': 0.45,\n",
              "  'f1_train': 0.75,\n",
              "  'pred_time': 1.9,\n",
              "  'train_time': 4067.53}}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "legend = []\n",
        "for i in range(len(clf_list)):\n",
        "    legend.append(clf_list[i].__class__.__name__)\n",
        "def eval_plot(labels, y_label, title):\n",
        "\n",
        "    clf1 = [clf_evaluation[legend[0]][labels[0]], clf_evaluation[legend[0]][labels[1]]]\n",
        "    clf2 = [clf_evaluation[legend[1]][labels[0]], clf_evaluation[legend[1]][labels[1]]]\n",
        "    clf3 = [clf_evaluation[legend[2]][labels[0]], clf_evaluation[legend[2]][labels[1]]]\n",
        "\n",
        "    x = np.arange(len(labels))  # the label locations\n",
        "    width = 0.2  # the width of the bars\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.bar(x - width/2, clf1, width, label=legend[0])\n",
        "    ax.bar(x + width/2, clf2, width, label=legend[1])\n",
        "    ax.bar(x + width*3/2, clf3, width, label=legend[2])\n",
        "\n",
        "    # Add labels, title, and legend\n",
        "    ax.set_ylabel(y_label)\n",
        "    ax.set_title(title)\n",
        "    ax.set_xticks(x)\n",
        "    ax.set_xticklabels(labels)\n",
        "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "\n",
        "    # Add bars annotation\n",
        "    for i in ax.patches:\n",
        "        # get_x pulls left or right; get_height pushes up or down\n",
        "        ax.text(i.get_x()+(width/2), i.get_height()+0.01,\n",
        "                str(i.get_height()), fontsize=11,\n",
        "                color='black', ha='center')\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "fqaf0kVsP4gc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['train_time', 'pred_time']\n",
        "y_label = 'Time'\n",
        "title = 'Train/Test Time per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "id": "GlRwgnoaP-po",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "0d8d12ef-778a-48e5-a859-97051dc303f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['acc_train', 'acc_test']\n",
        "y_label = 'Acc_Score'\n",
        "title = 'Train/Test Accuracy per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "id": "efNdfLHTQBmQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "d9055ce7-2a2a-4f1b-cc00-81cddae91603"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['f1_train', 'f1_test']\n",
        "y_label = 'F1_Score'\n",
        "title = 'Train/Test F1_Score per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "id": "dB4z9UfZQIWX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "12199830-5ea1-4b32-9968-f078486dad5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##ConutVectorizer\n",
        "###ngram_range = (1, 1) \n"
      ],
      "metadata": {
        "id": "Qk7ylopjYIdR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c32f2a23-7cfa-4e6b-8b9a-b14b4f743e3f",
        "id": "0bf1z-_7YIdT"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vocabulary :  446722\n"
          ]
        }
      ],
      "source": [
        "cv = CountVectorizer(binary=False,ngram_range=(1,1),\n",
        "\n",
        "                     )\n",
        "cv.fit(x_train)\n",
        "print(\"vocabulary : \",len(cv.vocabulary_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f6f11fe-7bc2-470c-efd0-0dc6d90d52f1",
        "id": "nyC_atkFYIdW"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'عندي': 219190,\n",
              " 'عمل': 218535,\n",
              " 'له': 267524,\n",
              " 'يتواصل': 424836,\n",
              " 'معي': 303792,\n",
              " 'الخاص': 37316,\n",
              " 'بعدين': 106084,\n",
              " 'وش': 380287,\n",
              " 'لقفك': 259092,\n",
              " 'داخل': 172317,\n",
              " 'حسابات': 161596,\n",
              " 'القطريين': 53488,\n",
              " 'تنبش': 147912,\n",
              " 'وراهم': 376899,\n",
              " 'ذابحتكم': 177058,\n",
              " 'الغيره': 50589,\n",
              " 'والقهر': 353016,\n",
              " 'فخريه': 228269,\n",
              " 'البصري': 29544,\n",
              " 'ياسويداني': 416847,\n",
              " 'خلونه': 170865,\n",
              " 'نلعب': 324861,\n",
              " 'سله': 190267,\n",
              " 'بس': 102105,\n",
              " 'دا': 172154,\n",
              " 'متسهوك': 288761,\n",
              " 'قوي': 239173,\n",
              " 'بيقلك': 124092,\n",
              " 'مشاعري': 299031,\n",
              " 'وولاء': 406415,\n",
              " 'غلب': 222509,\n",
              " 'شحاته': 194810,\n",
              " 'ابو': 2184,\n",
              " 'كف': 243662,\n",
              " 'سهرانهانا': 191428,\n",
              " 'من': 308682,\n",
              " 'اكتر': 22972,\n",
              " 'الاغاني': 26165,\n",
              " 'اللي': 56664,\n",
              " 'مفرحني': 304931,\n",
              " 'نجاحها': 317934,\n",
              " 'الكبير': 54398,\n",
              " 'تحديدا': 131782,\n",
              " 'بمصر': 112077,\n",
              " 'ولكل': 392982,\n",
              " 'يسالني': 430466,\n",
              " 'عن': 218918,\n",
              " 'تعاون': 140912,\n",
              " 'مع': 301934,\n",
              " 'الفنان': 52269,\n",
              " 'تامر': 127113,\n",
              " 'عاشور': 209375,\n",
              " 'احنا': 8683,\n",
              " 'سبق': 187381,\n",
              " 'وتعاوننا': 368287,\n",
              " 'بهذه': 116333,\n",
              " 'الاغنيه': 26207,\n",
              " 'وهي': 405517,\n",
              " 'الحانو': 35550,\n",
              " 'وكلمات': 390116,\n",
              " 'وسامصبري': 378919,\n",
              " 'وتوزيع': 370607,\n",
              " 'واتمني': 340516,\n",
              " 'ان': 70265,\n",
              " 'يجمعني': 425447,\n",
              " 'غناءي': 222787,\n",
              " 'معه': 303698,\n",
              " 'يوما': 443793,\n",
              " 'ما': 270574,\n",
              " 'ودي': 376221,\n",
              " 'كحك': 242023,\n",
              " 'ولا': 390588,\n",
              " 'لحمه': 254925,\n",
              " 'حببتي': 159101,\n",
              " 'عقبال': 216574,\n",
              " 'متشوفي': 288902,\n",
              " 'حفيدتك': 163240,\n",
              " 'يارب': 416187,\n",
              " 'قلت': 237943,\n",
              " 'تشك': 138635,\n",
              " 'اني': 74310,\n",
              " 'متجنسه': 287833,\n",
              " 'وربي': 377025,\n",
              " 'متخلف': 288224,\n",
              " 'قطريه': 237247,\n",
              " 'غصب': 222141,\n",
              " 'عنك': 219328,\n",
              " 'جاهل': 153180,\n",
              " 'خوفتي': 171639,\n",
              " 'انتي': 71417,\n",
              " 'المرتزقه': 59428,\n",
              " 'كل': 244036,\n",
              " 'فتاوي': 227513,\n",
              " 'القرضاوي': 53210,\n",
              " 'كانوا': 240615,\n",
              " 'يهاجمونه': 442576,\n",
              " 'فيها': 233861,\n",
              " 'الحين': 37202,\n",
              " 'صاروا': 200395,\n",
              " 'هم': 335687,\n",
              " 'ياخذونها': 415759,\n",
              " 'ليبي': 269220,\n",
              " 'نذل': 319497,\n",
              " 'عايش': 212424,\n",
              " 'في': 233151,\n",
              " 'امريكا': 69302,\n",
              " 'اتصل': 4205,\n",
              " 'بيه': 125525,\n",
              " 'خوووه': 171723,\n",
              " 'قاله': 234823,\n",
              " 'روح': 182330,\n",
              " 'بوكـ': 117877,\n",
              " 'مااات': 270665,\n",
              " 'قصدك': 236791,\n",
              " 'مازلتوو': 277695,\n",
              " 'عايشين': 212456,\n",
              " 'انتو': 71368,\n",
              " 'هههههههههههههه': 336865,\n",
              " 'بعتلنا': 105799,\n",
              " 'مصاري': 300239,\n",
              " 'وما': 393844,\n",
              " 'تجي': 131185,\n",
              " 'نيالوووو': 326606,\n",
              " 'صباح': 200731,\n",
              " 'الورد': 67126,\n",
              " 'يا': 413728,\n",
              " 'حبي': 159316,\n",
              " 'انت': 70722,\n",
              " 'والله': 353554,\n",
              " 'محقق': 292615,\n",
              " 'كونن': 246647,\n",
              " 'شكلك': 197400,\n",
              " 'القاضي': 52819,\n",
              " 'والمحامي': 353958,\n",
              " 'والشهود': 351032,\n",
              " 'والمحكمه': 354008,\n",
              " 'والمتهم': 353885,\n",
              " 'مخابينك': 293168,\n",
              " 'لا': 247660,\n",
              " 'تقذف': 144499,\n",
              " 'جزاك': 154700,\n",
              " 'الله': 56404,\n",
              " 'خير': 171921,\n",
              " 'بدون': 99794,\n",
              " 'دليل': 175112,\n",
              " 'مءكد': 270520,\n",
              " 'قاضي': 234576,\n",
              " 'وادانه': 341991,\n",
              " 'ايون': 78125,\n",
              " 'حبايبي': 159076,\n",
              " 'بحبهم': 97570,\n",
              " 'كلهم': 244866,\n",
              " 'ودابا': 375316,\n",
              " 'دير': 176507,\n",
              " 'معانا': 302285,\n",
              " 'المزيان': 59797,\n",
              " 'وقول': 389146,\n",
              " 'لينا': 270013,\n",
              " 'سر': 188396,\n",
              " 'النجاح': 64745,\n",
              " 'ديالك': 176355,\n",
              " 'وحده': 372859,\n",
              " 'سوالف': 191646,\n",
              " 'النسوان': 64969,\n",
              " 'تفت': 143281,\n",
              " 'القلب': 53608,\n",
              " 'وانا': 356460,\n",
              " 'المحيرني': 58724,\n",
              " 'انك': 73809,\n",
              " 'امس': 69351,\n",
              " 'بتفكر': 94599,\n",
              " 'الموضوع': 64001,\n",
              " 'ده': 175571,\n",
              " 'ببجي': 89208,\n",
              " 'وبلباردو': 362244,\n",
              " 'لودو': 268417,\n",
              " 'رجعنا': 179873,\n",
              " 'للاكل': 260193,\n",
              " 'الصحي': 45975,\n",
              " 'بقوه': 109069,\n",
              " 'لما': 265599,\n",
              " 'نركب': 319779,\n",
              " 'الطاءره': 46929,\n",
              " 'انجوع': 71663,\n",
              " 'تفسير': 143639,\n",
              " 'للموضوع': 265019,\n",
              " 'نبدا': 316575,\n",
              " 'عارفها': 209209,\n",
              " 'بتصير': 93517,\n",
              " 'او': 75024,\n",
              " 'ناكل': 316272,\n",
              " 'قبل': 235195,\n",
              " 'اول': 76052,\n",
              " 'تطلع': 140356,\n",
              " 'نستنا': 320435,\n",
              " 'الوجبه': 66966,\n",
              " 'الحصار': 36194,\n",
              " 'عطي': 215927,\n",
              " 'واحد': 341134,\n",
              " 'وحجمه': 372793,\n",
              " 'دول': 176061,\n",
              " 'ربعهم': 179394,\n",
              " 'باقي': 80694,\n",
              " 'يحطونهم': 426592,\n",
              " 'المتحف': 57624,\n",
              " 'يءرخون': 413698,\n",
              " 'كمجرمين': 245157,\n",
              " 'حرب': 160985,\n",
              " 'اتحاد': 3133,\n",
              " 'الكوره': 55427,\n",
              " 'عزبه': 214652,\n",
              " 'ومعندهم': 399289,\n",
              " 'فكر': 231092,\n",
              " 'تخطيطي': 133502,\n",
              " 'لصالح': 256911,\n",
              " 'المنتخب': 63190,\n",
              " 'بيفكرو': 123748,\n",
              " 'ازاي': 12752,\n",
              " 'يلمو': 439932,\n",
              " 'فلوس': 231698,\n",
              " 'لنفسهم': 267338,\n",
              " 'مكان': 306337,\n",
              " 'فيه': 233860,\n",
              " 'فساد': 229488,\n",
              " 'تستصغرون': 136907,\n",
              " 'مصايب': 300303,\n",
              " 'غيركم': 223308,\n",
              " 'عشان': 215121,\n",
              " 'محد': 292170,\n",
              " 'يزعلكم': 430145,\n",
              " 'بالكلام': 86274,\n",
              " 'اتحداك': 3201,\n",
              " 'اذا': 11065,\n",
              " 'بتقراها': 94799,\n",
              " 'تخربط': 133230,\n",
              " 'قد': 235460,\n",
              " 'الوضع': 67344,\n",
              " 'الاقتصادي': 26384,\n",
              " 'بلبنان': 110341,\n",
              " 'زفت': 184524,\n",
              " 'البنات': 30238,\n",
              " 'عم': 218047,\n",
              " 'يتزو': 423265,\n",
              " 'جوا': 156503,\n",
              " 'حب': 159022,\n",
              " 'زمان': 184842,\n",
              " 'ياهوي': 421008,\n",
              " 'يسلمج': 431796,\n",
              " 'امينه': 70231,\n",
              " 'مره': 296087,\n",
              " 'اخطف': 9804,\n",
              " 'صوبه': 203172,\n",
              " 'يمديني': 440288,\n",
              " 'اخذ': 9458,\n",
              " 'ولين': 393746,\n",
              " 'الحينه': 37210,\n",
              " 'خاطري': 167628,\n",
              " 'ماصارت': 278492,\n",
              " 'هالعهود': 330217,\n",
              " 'العنزي': 49542,\n",
              " 'مليون': 308107,\n",
              " 'منشن': 310122,\n",
              " 'نااااار': 315346,\n",
              " 'وناااار': 401295,\n",
              " 'مش': 298925,\n",
              " 'فاهم': 226986,\n",
              " 'شو': 198595,\n",
              " 'بعمل': 106707,\n",
              " 'مبسوط': 286679,\n",
              " 'ههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههه': 336963,\n",
              " 'حلوه': 164360,\n",
              " 'يسر': 431400,\n",
              " 'عاشووو': 209385,\n",
              " 'الكل': 54980,\n",
              " 'مو': 311999,\n",
              " 'السنه': 43430,\n",
              " 'طافت': 205391,\n",
              " 'خذنا': 168733,\n",
              " 'تذاكر': 134419,\n",
              " 'ال': 23780,\n",
              " 'اي': 76573,\n",
              " 'بي': 118293,\n",
              " 'ونص': 402885,\n",
              " 'يالسين': 418985,\n",
              " 'داخلين': 172339,\n",
              " 'ببلاش': 89512,\n",
              " 'لانهم': 251607,\n",
              " 'ربع': 179373,\n",
              " 'فلان': 231366,\n",
              " 'وعلان': 385477,\n",
              " 'يدخلون': 428276,\n",
              " 'ويا': 406669,\n",
              " 'اداراه': 10246,\n",
              " 'الفريق': 51754,\n",
              " 'الثاني': 33707,\n",
              " 'مب': 286013,\n",
              " 'الاداره': 24761,\n",
              " 'عندهم': 219168,\n",
              " 'معارف': 302056,\n",
              " 'هو': 337057,\n",
              " 'تويتر': 151197,\n",
              " 'صاير': 200636,\n",
              " 'انستگرام': 72386,\n",
              " 'لو': 268049,\n",
              " 'غلطانه': 222571,\n",
              " 'حاجه': 157847,\n",
              " 'توحشتك': 150318,\n",
              " 'ثاني': 151913,\n",
              " 'ربي': 179525,\n",
              " 'يفرحنا': 437108,\n",
              " 'انا': 70270,\n",
              " 'وياك': 406935,\n",
              " 'كلامك': 244176,\n",
              " 'صحيح': 201356,\n",
              " 'ومفيش': 399550,\n",
              " 'انسان': 72308,\n",
              " 'مهما': 311810,\n",
              " 'كان': 240486,\n",
              " 'بيحب': 120103,\n",
              " 'النكد': 65512,\n",
              " 'يصتعه': 433249,\n",
              " 'لكن': 259587,\n",
              " 'هي': 337489,\n",
              " 'الظروف': 47727,\n",
              " 'وربنا': 377009,\n",
              " 'يسعد': 431524,\n",
              " 'ايامك': 76650,\n",
              " 'تغذينا': 142434,\n",
              " 'سليمه': 190362,\n",
              " 'ومفيده': 399548,\n",
              " 'ماشفنا': 278361,\n",
              " 'شي': 199262,\n",
              " 'حــر': 162969,\n",
              " 'مغــرم': 304142,\n",
              " 'بشوفه': 103983,\n",
              " 'ابلاده': 1919,\n",
              " 'بيشه': 122370,\n",
              " 'واهل': 357520,\n",
              " 'احساسي': 7920,\n",
              " 'ياااحريه': 413955,\n",
              " 'فراس': 228604,\n",
              " 'اسعد': 15260,\n",
              " 'العريضي': 48628,\n",
              " 'مواليد': 312278,\n",
              " 'جرمانا': 154532,\n",
              " 'اعتقلوه': 19321,\n",
              " 'العفاريت': 49040,\n",
              " 'الزرق': 41266,\n",
              " 'بتاريخ': 89822,\n",
              " 'علي': 217609,\n",
              " 'حاجز': 157834,\n",
              " 'قصر': 236813,\n",
              " 'قناعه': 238484,\n",
              " 'محلات': 292679,\n",
              " 'الملابس': 62602,\n",
              " 'الحريمي': 35998,\n",
              " 'لازم': 249950,\n",
              " 'تعمل': 142036,\n",
              " 'قعده': 237432,\n",
              " 'وكراسي': 389735,\n",
              " 'وشيشه': 381685,\n",
              " 'وممكن': 400167,\n",
              " 'عند': 219056,\n",
              " 'الساده': 41912,\n",
              " 'الازواج': 25166,\n",
              " 'الغلابه': 50392,\n",
              " 'جعل': 154947,\n",
              " 'عمررررررررك': 218357,\n",
              " 'طويل': 207849,\n",
              " 'بو': 116591,\n",
              " 'حمد': 164672,\n",
              " 'قرفانيتوا': 236206,\n",
              " 'الاصل': 25871,\n",
              " 'نحط': 318609,\n",
              " 'خطه': 170019,\n",
              " 'بديله': 99935,\n",
              " 'سليم': 190354,\n",
              " 'ماراح': 277249,\n",
              " 'يسوي': 432124,\n",
              " 'تيار': 151290,\n",
              " 'هحلم': 333778,\n",
              " 'وحلمي': 373760,\n",
              " 'مصدقاه': 300461,\n",
              " 'وان': 356458,\n",
              " 'راح': 178352,\n",
              " 'لفين': 258788,\n",
              " 'هفضل': 335095,\n",
              " 'وراهوان': 376908,\n",
              " 'جيتي': 157187,\n",
              " 'دنيا': 175516,\n",
              " 'عليا': 217611,\n",
              " 'طريقي': 206504,\n",
              " 'بردو': 100764,\n",
              " 'مكملاه': 306953,\n",
              " 'يافندم': 417898,\n",
              " 'غلطقصور': 222604,\n",
              " 'ايه': 77951,\n",
              " 'بتتكلم': 90707,\n",
              " 'عليهم': 217922,\n",
              " 'مقر': 305765,\n",
              " 'حكم': 163700,\n",
              " 'بالعالمين': 85091,\n",
              " 'والاداريه': 345997,\n",
              " 'زيهم': 185858,\n",
              " 'زي': 185582,\n",
              " 'مقرين': 305851,\n",
              " 'للنواب': 265217,\n",
              " 'والوزراء': 355892,\n",
              " 'ماشوفتش': 278421,\n",
              " 'النواب': 65655,\n",
              " 'فالاداريه': 225074,\n",
              " 'وهم': 405273,\n",
              " 'ثم': 152257,\n",
              " 'اخدش': 9404,\n",
              " 'لامن': 251318,\n",
              " 'ميزانيه': 314306,\n",
              " 'الدوله': 39361,\n",
              " 'البلدكله': 30090,\n",
              " 'حتي': 160227,\n",
              " 'يوتيوبر': 443169,\n",
              " 'ستانداب': 187588,\n",
              " 'كوميدي': 246550,\n",
              " 'متل': 289959,\n",
              " 'الخلق': 38008,\n",
              " 'مافي': 279849,\n",
              " 'كلو': 244884,\n",
              " 'عالحرق': 210305,\n",
              " 'وشي': 381637,\n",
              " 'بمز': 111837,\n",
              " 'النفس': 65351,\n",
              " 'التفاهه': 32605,\n",
              " 'وتقل': 369266,\n",
              " 'الدم': 39073,\n",
              " 'يعني': 435830,\n",
              " 'نقدر': 324004,\n",
              " 'نقول': 324366,\n",
              " 'ظروف': 208341,\n",
              " 'المباراه': 57345,\n",
              " 'تعطي': 141713,\n",
              " 'شكل': 197383,\n",
              " 'ممكن': 308512,\n",
              " 'نشكل': 321237,\n",
              " 'المدرب': 59061,\n",
              " 'نسبه': 320259,\n",
              " 'يخلف': 427844,\n",
              " 'اهلك': 74783,\n",
              " 'متصور': 288985,\n",
              " 'ياليل': 419968,\n",
              " 'لاعب': 250496,\n",
              " 'يفكر': 437426,\n",
              " 'نفسه': 323527,\n",
              " 'قديش': 235744,\n",
              " 'ناس': 315745,\n",
              " 'بيكونوا': 124411,\n",
              " 'عارفين': 209231,\n",
              " 'الصح': 45908,\n",
              " 'وبيسكتوا': 364019,\n",
              " 'خوف': 171629,\n",
              " 'المجتمع': 58223,\n",
              " 'والمواجهه': 355122,\n",
              " 'رده': 180405,\n",
              " 'الفعل': 51970,\n",
              " 'هتصير': 333083,\n",
              " 'تكلموا': 145821,\n",
              " 'ضد': 204182,\n",
              " 'الخوف': 38220,\n",
              " 'والسكوت': 350524,\n",
              " 'لانو': 251612,\n",
              " 'نحكي': 318676,\n",
              " 'دكتوري': 174901,\n",
              " 'الخرا': 37538,\n",
              " 'امتحانكم': 68909,\n",
              " 'الاتنين': 24182,\n",
              " 'ولو': 393454,\n",
              " 'فش': 229653,\n",
              " 'اضراب': 18103,\n",
              " 'حيبقي': 166155,\n",
              " 'التلاته': 32924,\n",
              " 'متخلي': 288250,\n",
              " 'سواء': 191558,\n",
              " 'وتنهي': 370338,\n",
              " 'ينعن': 441993,\n",
              " 'عرضك': 214248,\n",
              " 'غباءك': 221253,\n",
              " 'بدل': 99519,\n",
              " 'منتا': 309118,\n",
              " 'خنقنا': 171377,\n",
              " 'ومش': 398312,\n",
              " 'فهمين': 232610,\n",
              " 'بتسمعي': 92901,\n",
              " 'راديو': 178484,\n",
              " 'احييييييكي': 8875,\n",
              " 'سنا': 190903,\n",
              " 'المجد': 58238,\n",
              " 'للراديو': 262018,\n",
              " 'عساهم': 214932,\n",
              " 'بحال': 97450,\n",
              " 'واردا': 342462,\n",
              " 'منه': 311089,\n",
              " 'يوفقك': 443622,\n",
              " 'ويسعدك': 409905,\n",
              " 'وينولك': 413053,\n",
              " 'بالك': 86106,\n",
              " 'صح': 201092,\n",
              " 'حاله': 158731,\n",
              " 'منكتبها': 310902,\n",
              " 'لنرتاح': 267187,\n",
              " 'فكره': 231144,\n",
              " 'عبالنا': 212607,\n",
              " 'ماتعرف': 274572,\n",
              " 'تلايط': 146126,\n",
              " 'دب': 173073,\n",
              " 'الاسود': 25675,\n",
              " 'هلهاش': 335635,\n",
              " 'الضحكه': 46715,\n",
              " 'حق': 163256,\n",
              " 'يوم': 443792,\n",
              " 'تحيا': 132806,\n",
              " 'مصر': 300526,\n",
              " 'بجيشها': 97355,\n",
              " 'ورجاله': 377103,\n",
              " 'الاقوياء': 26471,\n",
              " 'بيخلصونه': 120710,\n",
              " 'الارهابيين': 25077,\n",
              " 'المجرمين': 58274,\n",
              " 'التكفريين': 32874,\n",
              " 'يرزقنا': 429495,\n",
              " 'ربفي': 179420,\n",
              " 'وظاءف': 383937,\n",
              " 'لنساء': 267201,\n",
              " 'الباحه': 28220,\n",
              " 'بلجرشي': 110394,\n",
              " 'وظايفنساييه': 383966,\n",
              " 'تنور': 149326,\n",
              " 'خلو': 170830,\n",
              " 'مرحبا': 295522,\n",
              " 'بك': 109164,\n",
              " 'قال': 234717,\n",
              " 'امفكره': 69685,\n",
              " 'فلهويت': 231672,\n",
              " 'عصره': 215515,\n",
              " 'جد': 153879,\n",
              " 'تلمي': 146675,\n",
              " 'فانز': 226907,\n",
              " 'كنزه': 245724,\n",
              " 'مرسلي': 295722,\n",
              " 'الحقد': 36374,\n",
              " 'والكراهيه': 353194,\n",
              " 'الي': 67772,\n",
              " 'نسخه': 320530,\n",
              " 'ويشتمو': 410167,\n",
              " 'خبر': 168054,\n",
              " 'سهيله': 191536,\n",
              " 'وابتسام': 339539,\n",
              " 'تسكت': 137519,\n",
              " 'عمري': 218452,\n",
              " 'درتها': 173759,\n",
              " 'حياتي': 166030,\n",
              " 'نكون': 324763,\n",
              " 'كاءن': 239438,\n",
              " 'غريب': 221874,\n",
              " 'الواحد': 66778,\n",
              " 'محتاج': 291910,\n",
              " 'الصبح': 45849,\n",
              " 'منشطات': 310093,\n",
              " 'يعرف': 435232,\n",
              " 'يصحصح': 433263,\n",
              " 'وبالليل': 358744,\n",
              " 'مهدءات': 311609,\n",
              " 'ومنومات': 400688,\n",
              " 'ينام': 440951,\n",
              " 'وباقي': 358360,\n",
              " 'اليوم': 68212,\n",
              " 'مخدرات': 293464,\n",
              " 'يستحمل': 430747,\n",
              " 'الناس': 64495,\n",
              " 'بيتعامل': 119340,\n",
              " 'معاهم': 302351,\n",
              " 'اقول': 22650,\n",
              " 'رحو': 180205,\n",
              " 'حرور': 161316,\n",
              " 'جزركم': 154738,\n",
              " 'وبعدين': 361662,\n",
              " 'تفاهمو': 143261,\n",
              " 'المحكمه': 58639,\n",
              " 'بتقاضيكم': 94710,\n",
              " 'القرصنه': 53205,\n",
              " 'ياعيال': 417634,\n",
              " 'دحلان': 173356,\n",
              " 'بالضبط': 84829,\n",
              " 'جابها': 152657,\n",
              " 'الجرح': 34391,\n",
              " 'فعلا': 230472,\n",
              " 'انكم': 73917,\n",
              " 'سفيهين': 189389,\n",
              " 'ولك': 392967,\n",
              " 'يامصدي': 420364,\n",
              " 'يامعفن': 420438,\n",
              " 'يابو': 414706,\n",
              " 'هرمونات': 334385,\n",
              " 'بني': 115484,\n",
              " 'صهيون': 203106,\n",
              " 'وين': 412531,\n",
              " 'مايروح': 284164,\n",
              " 'وغلط': 386424,\n",
              " 'لايجتمع': 251936,\n",
              " 'دينان': 176853,\n",
              " 'الحرمين': 35953,\n",
              " 'انتوا': 71369,\n",
              " 'جمعتوا': 155671,\n",
              " 'الاديان': 24836,\n",
              " 'وخالفتوا': 374322,\n",
              " 'الدين': 39540,\n",
              " 'والرسولص': 349976,\n",
              " 'استح': 13678,\n",
              " 'وجهك': 371942,\n",
              " 'خايف': 167984,\n",
              " 'المشار': 60439,\n",
              " 'ومنت': 400285,\n",
              " 'حوار': 165609,\n",
              " 'بين': 124845,\n",
              " 'اثنين': 6091,\n",
              " 'بالديوانيه': 83625,\n",
              " 'يقوله': 438534,\n",
              " 'المعامله': 61313,\n",
              " 'موراضي': 312725,\n",
              " 'يمشيها': 440469,\n",
              " 'اخوك': 10086,\n",
              " 'يرد': 429411,\n",
              " 'عليه': 217846,\n",
              " 'مافيه': 279898,\n",
              " 'ومتمسك': 396574,\n",
              " 'بالقوانين': 86083,\n",
              " 'ودايما': 375493,\n",
              " 'مسوي': 298781,\n",
              " 'رجل': 179942,\n",
              " 'انظباط': 73087,\n",
              " 'ويطبق': 410672,\n",
              " 'القانون': 52869,\n",
              " 'صج': 201062,\n",
              " 'لي': 269092,\n",
              " 'طلبته': 207083,\n",
              " 'للاسف': 259999,\n",
              " 'اصبح': 17440,\n",
              " 'يحمي': 426998,\n",
              " 'عمله': 218628,\n",
              " 'ووطنه': 406253,\n",
              " 'بتطبيق': 93647,\n",
              " 'القوانين': 53882,\n",
              " 'وعدم': 384727,\n",
              " 'تجاوزها': 130717,\n",
              " 'عرفتوا': 214321,\n",
              " 'الفساد': 51801,\n",
              " 'اصلح': 17855,\n",
              " 'نفسك': 323492,\n",
              " 'اولا': 76054,\n",
              " 'مسخره': 298130,\n",
              " 'وليد': 393652,\n",
              " 'مفيش': 305297,\n",
              " 'هزار': 334463,\n",
              " 'فريق': 229331,\n",
              " 'يمكن': 440564,\n",
              " 'يتحسب': 422778,\n",
              " 'النادي': 64464,\n",
              " 'الاهلي': 27580,\n",
              " 'يشفيه': 432774,\n",
              " 'يقومه': 438604,\n",
              " 'بالسلامه': 84219,\n",
              " 'رب': 179227,\n",
              " 'مبروكين': 286587,\n",
              " 'شاء': 193110,\n",
              " 'دكتور': 174878,\n",
              " 'يوفقها': 443636,\n",
              " 'وتشوفونها': 367778,\n",
              " 'اعلي': 20140,\n",
              " 'المراتب': 59297,\n",
              " 'الهلالبطلالسوبر': 66405,\n",
              " 'احتزم': 7407,\n",
              " 'يارفيقي': 416358,\n",
              " 'وحدد': 372842,\n",
              " 'الاتجاه': 24057,\n",
              " 'واحتزم': 341068,\n",
              " 'فالبساطه': 225257,\n",
              " 'وفاليوم': 386853,\n",
              " 'خذ': 168653,\n",
              " 'لزومك': 256137,\n",
              " 'لاتوصي': 249247,\n",
              " 'ولاتبغي': 390853,\n",
              " 'وصاه': 381894,\n",
              " 'اتكل': 5213,\n",
              " 'بالله': 86518,\n",
              " 'وخله': 375004,\n",
              " 'يصير': 433724,\n",
              " 'اخي': 10144,\n",
              " 'مفهمتوش': 305229,\n",
              " 'ومعرفتش': 399124,\n",
              " 'معناه': 303598,\n",
              " 'ماشي': 278443,\n",
              " 'زعما': 184344,\n",
              " 'مستهزاه': 297922,\n",
              " 'الاسم': 25630,\n",
              " 'اكيد': 23723,\n",
              " 'بالعكس': 85309,\n",
              " 'كيبقا': 246844,\n",
              " 'فيا': 233154,\n",
              " 'اسماء': 15616,\n",
              " 'كنسمعها': 245766,\n",
              " 'كيتعقدو': 246912,\n",
              " 'منها': 311090,\n",
              " 'اصحابها': 17521,\n",
              " 'وهادشي': 404298,\n",
              " 'مسءوليه': 296865,\n",
              " 'الوالدين': 66894,\n",
              " 'مكيختاروش': 307123,\n",
              " 'لاولادهم': 251808,\n",
              " 'ازيد': 13079,\n",
              " 'كلامج': 244160,\n",
              " 'شنو': 198193,\n",
              " 'علاقه': 217069,\n",
              " 'الكليه': 55146,\n",
              " 'بالسنه': 84279,\n",
              " 'كثر': 241848,\n",
              " 'الاجرام': 24303,\n",
              " 'حمروا': 164755,\n",
              " 'العين': 49854,\n",
              " 'وفعلوا': 387482,\n",
              " 'الجميع': 34870,\n",
              " 'وبس': 361055,\n",
              " 'واسطات': 343254,\n",
              " 'مل': 307194,\n",
              " 'دمرت': 175236,\n",
              " 'ديرتنا': 176540,\n",
              " 'هههههه': 336736,\n",
              " 'ديما': 176776,\n",
              " 'النيفو': 65855,\n",
              " 'طالع': 205519,\n",
              " 'يغنيك': 436421,\n",
              " 'يابومنصور': 414906,\n",
              " 'وكل': 390033,\n",
              " 'عام': 212059,\n",
              " 'وانت': 356562,\n",
              " 'بخير': 99025,\n",
              " 'يتقبل': 424197,\n",
              " 'منك': 310887,\n",
              " 'الصيام': 46573,\n",
              " 'والقيام': 353053,\n",
              " 'هلا': 335289,\n",
              " 'معك': 303266,\n",
              " 'تلات': 145989,\n",
              " 'ثواني': 152363,\n",
              " 'نعم': 322762,\n",
              " 'الترفيه': 31819,\n",
              " 'صناعه': 202971,\n",
              " 'مجالها': 290993,\n",
              " 'كبير': 241096,\n",
              " 'جدا': 153880,\n",
              " 'اقتصادها': 22027,\n",
              " 'مبني': 287000,\n",
              " 'السياحه': 43787,\n",
              " 'حكومتنا': 163797,\n",
              " 'هذي': 334245,\n",
              " 'الصناعه': 46419,\n",
              " 'نهني': 325842,\n",
              " 'المملكه': 62999,\n",
              " 'العربيه': 48520,\n",
              " 'السعوديه': 42716,\n",
              " 'اهتمامها': 74533,\n",
              " 'بهذا': 116319,\n",
              " 'المجال': 58182,\n",
              " 'شالله': 193770,\n",
              " 'بتخلق': 91759,\n",
              " 'فرص': 228956,\n",
              " 'تجاريه': 130617,\n",
              " 'وظيفيه': 384112,\n",
              " 'بيستفيد': 121795,\n",
              " 'المواطن': 63772,\n",
              " 'السعودي': 42703,\n",
              " 'بيرد': 121218,\n",
              " 'ساعات': 186406,\n",
              " 'ليه': 270082,\n",
              " 'فاكرني': 224992,\n",
              " 'مضاد': 301041,\n",
              " 'حيوي': 167160,\n",
              " 'ايات': 76602,\n",
              " 'المنافق': 63120,\n",
              " 'هذولا': 334228,\n",
              " 'فيهم': 233963,\n",
              " 'شعب': 196159,\n",
              " 'جنگل': 156337,\n",
              " 'ياحلوه': 415517,\n",
              " 'طير': 208019,\n",
              " 'قلبه': 237870,\n",
              " 'اكل': 23501,\n",
              " 'المسكين': 60301,\n",
              " 'سخافه': 188235,\n",
              " 'برلمانيه': 101213,\n",
              " 'خلصت': 170539,\n",
              " 'مشاكل': 299066,\n",
              " 'الكويت': 55605,\n",
              " 'ومابقي': 394015,\n",
              " 'الا': 23794,\n",
              " 'حجاب': 160267,\n",
              " 'الوزيره': 67205,\n",
              " 'الكدب': 54559,\n",
              " 'والافتراء': 346431,\n",
              " 'اوعي': 75843,\n",
              " 'تفكري': 143881,\n",
              " 'حد': 160484,\n",
              " 'هيصدقكم': 338369,\n",
              " 'خلااااص': 170371,\n",
              " 'اشكالكم': 16957,\n",
              " 'معدتشبيتسمع': 302704,\n",
              " 'لها': 267526,\n",
              " 'كلام': 244145,\n",
              " 'نزل': 319963,\n",
              " 'النهارده': 65603,\n",
              " 'الشغل': 44947,\n",
              " 'بشراب': 103540,\n",
              " 'فرده': 228876,\n",
              " 'وفرده': 387155,\n",
              " 'كحلي': 242041,\n",
              " 'الدنيا': 39141,\n",
              " 'زعلانين': 184281,\n",
              " 'منو': 311214,\n",
              " 'الزعيم': 41345,\n",
              " 'الهلال': 66369,\n",
              " 'الزعيمه': 41350,\n",
              " 'وفكونا': 387574,\n",
              " 'هالامور': 328223,\n",
              " 'اكبر': 22822,\n",
              " 'هالمواضيع': 331253,\n",
              " 'والهلال': 355702,\n",
              " 'يقولك': 438515,\n",
              " 'المثل': 58138,\n",
              " 'النصيحه': 65143,\n",
              " 'بفلوس': 108170,\n",
              " 'والحين': 349104,\n",
              " 'بلاش': 110082,\n",
              " 'اتركو': 3773,\n",
              " 'التخبط': 31563,\n",
              " 'والتلزق': 348067,\n",
              " 'بالاخوان': 80967,\n",
              " 'المفلسون': 62078,\n",
              " 'وارجعو': 342420,\n",
              " 'لربعكم': 255768,\n",
              " 'واهلكم': 357525,\n",
              " 'بالخليج': 83330,\n",
              " 'الشوط': 45467,\n",
              " 'التاني': 31180,\n",
              " 'كله': 244826,\n",
              " 'ليفربول': 269772,\n",
              " 'صلاح': 202718,\n",
              " 'هايسجل': 332064,\n",
              " 'هيووووووووووووو': 339135,\n",
              " 'ريتني': 182986,\n",
              " 'حكيت': 163837,\n",
              " 'زمااان': 184819,\n",
              " 'البطان': 29597,\n",
              " 'الاول': 27841,\n",
              " 'طلع': 207165,\n",
              " 'بلهون': 111063,\n",
              " 'جربوا': 154382,\n",
              " 'سانك': 186977,\n",
              " 'يو': 443065,\n",
              " 'فيري': 233449,\n",
              " 'ماتش': 274129,\n",
              " 'مصطلح': 300757,\n",
              " 'فتوها': 227837,\n",
              " 'صراحه': 201825,\n",
              " 'لحالو': 254483,\n",
              " 'مثير': 290882,\n",
              " 'للقرف': 263732,\n",
              " 'تءاخذنا': 126242,\n",
              " 'صاروخين': 200401,\n",
              " 'ضربينهم': 204325,\n",
              " 'النقب': 65449,\n",
              " 'اظن': 18871,\n",
              " 'الاتحادالاتفاق': 24067,\n",
              " 'عون': 219907,\n",
              " 'الاتحاد': 24064,\n",
              " 'لعيبه': 258015,\n",
              " 'عاهات': 212233,\n",
              " 'بالملعب': 87361,\n",
              " 'صدقت': 201600,\n",
              " 'العامر': 48038,\n",
              " 'بدر': 99297,\n",
              " 'عيال': 220057,\n",
              " 'خاله': 167839,\n",
              " 'المشعان': 60599,\n",
              " 'بشويخ': 104021,\n",
              " 'مشعل': 299628,\n",
              " 'وغازي': 386120,\n",
              " 'ومشهور': 398519,\n",
              " 'بسالكم': 102186,\n",
              " 'سءال': 185923,\n",
              " 'شبكتكم': 194300,\n",
              " 'هالايام': 328280,\n",
              " 'فور': 232828,\n",
              " 'زق': 184571,\n",
              " 'حيزبونه': 166537,\n",
              " 'حقنه': 163427,\n",
              " 'شرجيه': 195440,\n",
              " 'افترضنا': 21059,\n",
              " 'بواب': 116607,\n",
              " 'ابن': 2049,\n",
              " 'فيفي': 233616,\n",
              " 'مهنه': 311900,\n",
              " 'شريفه': 195864,\n",
              " 'بياكل': 118470,\n",
              " 'هز': 334450,\n",
              " 'وسط': 379343,\n",
              " 'امو': 69998,\n",
              " 'واختو': 341663,\n",
              " 'ههههههه': 336773,\n",
              " 'تصدق': 139254,\n",
              " 'خوك': 171661,\n",
              " 'لنه': 267450,\n",
              " 'يبي': 422181,\n",
              " 'يبيض': 422202,\n",
              " 'وجهه': 371970,\n",
              " 'قدامك': 235496,\n",
              " 'وبعد': 361632,\n",
              " 'يومين': 443905,\n",
              " 'حتا': 159537,\n",
              " 'وتساب': 367170,\n",
              " 'سامعات': 186857,\n",
              " 'بعرف': 106155,\n",
              " 'راحوا': 178429,\n",
              " 'بالحب': 82901,\n",
              " 'وهو': 405429,\n",
              " 'حبيبي': 159401,\n",
              " 'وانتي': 356725,\n",
              " 'بعد': 105929,\n",
              " 'شكلج': 197397,\n",
              " 'نسيتي': 320843,\n",
              " 'محلب': 292697,\n",
              " 'يبكلمنا': 421930,\n",
              " 'كده': 242166,\n",
              " 'الزول': 41730,\n",
              " 'غاشينا': 220980,\n",
              " 'هذا': 334115,\n",
              " 'شغلك': 196543,\n",
              " 'بتفهمنا': 94649,\n",
              " 'احني': 8749,\n",
              " 'رشحناكم': 180826,\n",
              " 'ماعاد': 278969,\n",
              " 'فهمنا': 232571,\n",
              " 'شيء': 199263,\n",
              " 'عدل': 213684,\n",
              " 'بالدنيا': 83539,\n",
              " 'كنت': 245549,\n",
              " 'بقلك': 108903,\n",
              " 'ردي': 180452,\n",
              " 'العدل': 48357,\n",
              " 'فقط': 230948,\n",
              " 'غير': 223204,\n",
              " 'وعند': 385770,\n",
              " 'بضيع': 104828,\n",
              " 'تتعرفي': 129594,\n",
              " 'توني': 151065,\n",
              " 'كروس': 242842,\n",
              " 'ينزلون': 441533,\n",
              " 'تميم': 147622,\n",
              " 'ماقول': 280207,\n",
              " 'ياخسي': 415810,\n",
              " 'قولتهم': 238891,\n",
              " 'شان': 193868,\n",
              " 'انام': 70497,\n",
              " 'مبجر': 286369,\n",
              " 'اسمع': 15690,\n",
              " 'صوتج': 203202,\n",
              " 'واتام': 339898,\n",
              " 'ليش': 269570,\n",
              " 'كذه': 242333,\n",
              " 'جننتني': 156210,\n",
              " 'واتعبت': 340288,\n",
              " 'حالي': 158749,\n",
              " 'خلي': 170900,\n",
              " 'خيالك': 171844,\n",
              " 'الاقل': 26439,\n",
              " 'يمر': 440294,\n",
              " 'مرور': 296170,\n",
              " 'عابر': 208838,\n",
              " 'الشيخ': 45575,\n",
              " 'اديني': 11041,\n",
              " 'مورو': 312791,\n",
              " 'بيقولك': 124138,\n",
              " 'المثليه': 58152,\n",
              " 'حريه': 161423,\n",
              " 'ومسءليه': 397892,\n",
              " 'ااااه': 399,\n",
              " 'ياخواات': 415866,\n",
              " 'وياولاد': 407088,\n",
              " 'الوخه': 67071,\n",
              " 'الرقابه': 40534,\n",
              " 'اجودعان': 6881,\n",
              " 'محدش': 292201,\n",
              " 'يقواي': 438465,\n",
              " 'بقي': 109079,\n",
              " 'دين': 176828,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "cv.vocabulary_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GCilTPR5YIdW"
      },
      "outputs": [],
      "source": [
        "x_train_cv = cv.transform(x_train)\n",
        "x_test_cv = cv.transform(x_test) "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Models training"
      ],
      "metadata": {
        "id": "cgU8UV0AYIdX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf_MNB = MultinomialNB()\n",
        "clf_LR = LogisticRegression()\n",
        "clf_SVM=SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3, random_state=42,\n",
        "                      # max_iter=100,\n",
        "                      tol=None)\n",
        "clf_list = [ clf_MNB, clf_LR, clf_SVM]\n",
        "\n",
        "# Compare evaluation of the different classifiers\n",
        "clf_evaluation = {}\n",
        "clf_report = {}\n",
        "\n",
        "for clf in clf_list:\n",
        "    clf_evaluation[clf.__class__.__name__], clf_report[clf.__class__.__name__] = train_predict(clf,\n",
        "                                                                                              x_train_cv,\n",
        "                                                                                              np.argmax(y_train.to_numpy(), axis=1),\n",
        "                                                                                              x_test_cv,\n",
        "                                                                                              np.argmax(y_test.to_numpy(), axis=1)\n",
        "                                                                                              )\n",
        "    print(clf_evaluation )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd38f32c-62ed-4aab-8d3e-897661c74768",
        "id": "M5CBwrSsYIdZ"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'MultinomialNB': {'train_time': 1.45, 'pred_time': 0.52, 'acc_train': 0.64, 'acc_test': 0.49, 'f1_train': 0.59, 'f1_test': 0.4}}\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'MultinomialNB': {'train_time': 1.45, 'pred_time': 0.52, 'acc_train': 0.64, 'acc_test': 0.49, 'f1_train': 0.59, 'f1_test': 0.4}, 'LogisticRegression': {'train_time': 280.66, 'pred_time': 0.46, 'acc_train': 0.72, 'acc_test': 0.52, 'f1_train': 0.71, 'f1_test': 0.5}}\n",
            "{'MultinomialNB': {'train_time': 1.45, 'pred_time': 0.52, 'acc_train': 0.64, 'acc_test': 0.49, 'f1_train': 0.59, 'f1_test': 0.4}, 'LogisticRegression': {'train_time': 280.66, 'pred_time': 0.46, 'acc_train': 0.72, 'acc_test': 0.52, 'f1_train': 0.71, 'f1_test': 0.5}, 'SGDClassifier': {'train_time': 3087.42, 'pred_time': 0.47, 'acc_train': 0.66, 'acc_test': 0.49, 'f1_train': 0.64, 'f1_test': 0.44}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_evaluation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40cfde81-1c7c-4ab0-adf0-7e6bc1f74ed6",
        "id": "b3-90XPKYIdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'LogisticRegression': {'acc_test': 0.52,\n",
              "  'acc_train': 0.72,\n",
              "  'f1_test': 0.5,\n",
              "  'f1_train': 0.71,\n",
              "  'pred_time': 0.46,\n",
              "  'train_time': 280.66},\n",
              " 'MultinomialNB': {'acc_test': 0.49,\n",
              "  'acc_train': 0.64,\n",
              "  'f1_test': 0.4,\n",
              "  'f1_train': 0.59,\n",
              "  'pred_time': 0.52,\n",
              "  'train_time': 1.45},\n",
              " 'SGDClassifier': {'acc_test': 0.49,\n",
              "  'acc_train': 0.66,\n",
              "  'f1_test': 0.44,\n",
              "  'f1_train': 0.64,\n",
              "  'pred_time': 0.47,\n",
              "  'train_time': 3087.42}}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "legend = []\n",
        "for i in range(len(clf_list)):\n",
        "    legend.append(clf_list[i].__class__.__name__)\n",
        "def eval_plot(labels, y_label, title):\n",
        "\n",
        "    clf1 = [clf_evaluation[legend[0]][labels[0]], clf_evaluation[legend[0]][labels[1]]]\n",
        "    clf2 = [clf_evaluation[legend[1]][labels[0]], clf_evaluation[legend[1]][labels[1]]]\n",
        "    clf3 = [clf_evaluation[legend[2]][labels[0]], clf_evaluation[legend[2]][labels[1]]]\n",
        "\n",
        "    x = np.arange(len(labels))  # the label locations\n",
        "    width = 0.2  # the width of the bars\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.bar(x - width/2, clf1, width, label=legend[0])\n",
        "    ax.bar(x + width/2, clf2, width, label=legend[1])\n",
        "    ax.bar(x + width*3/2, clf3, width, label=legend[2])\n",
        "\n",
        "    # Add labels, title, and legend\n",
        "    ax.set_ylabel(y_label)\n",
        "    ax.set_title(title)\n",
        "    ax.set_xticks(x)\n",
        "    ax.set_xticklabels(labels)\n",
        "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "\n",
        "    # Add bars annotation\n",
        "    for i in ax.patches:\n",
        "        # get_x pulls left or right; get_height pushes up or down\n",
        "        ax.text(i.get_x()+(width/2), i.get_height()+0.01,\n",
        "                str(i.get_height()), fontsize=11,\n",
        "                color='black', ha='center')\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "Vt6wWWdLYIdc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['train_time', 'pred_time']\n",
        "y_label = 'Time'\n",
        "title = 'Train/Test Time per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "2bb15820-71f3-4b17-fb10-26b99f15f48d",
        "id": "TOq1qe0aYIdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['acc_train', 'acc_test']\n",
        "y_label = 'Acc_Score'\n",
        "title = 'Train/Test Accuracy per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "3c9025b7-1a22-43c4-ecb0-165601345026",
        "id": "d5A6Eek0YIdy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['f1_train', 'f1_test']\n",
        "y_label = 'F1_Score'\n",
        "title = 'Train/Test F1_Score per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "d3d655cb-054a-4113-addb-c31facbef712",
        "id": "wQj1YFwtYIdz"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##TFIDF"
      ],
      "metadata": {
        "id": "Z8GGkF2TnuH7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9648c78-4ef3-422f-87aa-8b1b536f7c0f",
        "id": "H9Zh2Q4xoa9Q"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vocabulary :  446722\n"
          ]
        }
      ],
      "source": [
        "cv = TfidfVectorizer()\n",
        "cv.fit(x_train)\n",
        "print(\"vocabulary : \",len(cv.vocabulary_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52bf6261-5227-433e-aec9-212f4a17ab7f",
        "id": "V6f-GmeUoa9R"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'عندي': 219190,\n",
              " 'عمل': 218535,\n",
              " 'له': 267524,\n",
              " 'يتواصل': 424836,\n",
              " 'معي': 303792,\n",
              " 'الخاص': 37316,\n",
              " 'بعدين': 106084,\n",
              " 'وش': 380287,\n",
              " 'لقفك': 259092,\n",
              " 'داخل': 172317,\n",
              " 'حسابات': 161596,\n",
              " 'القطريين': 53488,\n",
              " 'تنبش': 147912,\n",
              " 'وراهم': 376899,\n",
              " 'ذابحتكم': 177058,\n",
              " 'الغيره': 50589,\n",
              " 'والقهر': 353016,\n",
              " 'فخريه': 228269,\n",
              " 'البصري': 29544,\n",
              " 'ياسويداني': 416847,\n",
              " 'خلونه': 170865,\n",
              " 'نلعب': 324861,\n",
              " 'سله': 190267,\n",
              " 'بس': 102105,\n",
              " 'دا': 172154,\n",
              " 'متسهوك': 288761,\n",
              " 'قوي': 239173,\n",
              " 'بيقلك': 124092,\n",
              " 'مشاعري': 299031,\n",
              " 'وولاء': 406415,\n",
              " 'غلب': 222509,\n",
              " 'شحاته': 194810,\n",
              " 'ابو': 2184,\n",
              " 'كف': 243662,\n",
              " 'سهرانهانا': 191428,\n",
              " 'من': 308682,\n",
              " 'اكتر': 22972,\n",
              " 'الاغاني': 26165,\n",
              " 'اللي': 56664,\n",
              " 'مفرحني': 304931,\n",
              " 'نجاحها': 317934,\n",
              " 'الكبير': 54398,\n",
              " 'تحديدا': 131782,\n",
              " 'بمصر': 112077,\n",
              " 'ولكل': 392982,\n",
              " 'يسالني': 430466,\n",
              " 'عن': 218918,\n",
              " 'تعاون': 140912,\n",
              " 'مع': 301934,\n",
              " 'الفنان': 52269,\n",
              " 'تامر': 127113,\n",
              " 'عاشور': 209375,\n",
              " 'احنا': 8683,\n",
              " 'سبق': 187381,\n",
              " 'وتعاوننا': 368287,\n",
              " 'بهذه': 116333,\n",
              " 'الاغنيه': 26207,\n",
              " 'وهي': 405517,\n",
              " 'الحانو': 35550,\n",
              " 'وكلمات': 390116,\n",
              " 'وسامصبري': 378919,\n",
              " 'وتوزيع': 370607,\n",
              " 'واتمني': 340516,\n",
              " 'ان': 70265,\n",
              " 'يجمعني': 425447,\n",
              " 'غناءي': 222787,\n",
              " 'معه': 303698,\n",
              " 'يوما': 443793,\n",
              " 'ما': 270574,\n",
              " 'ودي': 376221,\n",
              " 'كحك': 242023,\n",
              " 'ولا': 390588,\n",
              " 'لحمه': 254925,\n",
              " 'حببتي': 159101,\n",
              " 'عقبال': 216574,\n",
              " 'متشوفي': 288902,\n",
              " 'حفيدتك': 163240,\n",
              " 'يارب': 416187,\n",
              " 'قلت': 237943,\n",
              " 'تشك': 138635,\n",
              " 'اني': 74310,\n",
              " 'متجنسه': 287833,\n",
              " 'وربي': 377025,\n",
              " 'متخلف': 288224,\n",
              " 'قطريه': 237247,\n",
              " 'غصب': 222141,\n",
              " 'عنك': 219328,\n",
              " 'جاهل': 153180,\n",
              " 'خوفتي': 171639,\n",
              " 'انتي': 71417,\n",
              " 'المرتزقه': 59428,\n",
              " 'كل': 244036,\n",
              " 'فتاوي': 227513,\n",
              " 'القرضاوي': 53210,\n",
              " 'كانوا': 240615,\n",
              " 'يهاجمونه': 442576,\n",
              " 'فيها': 233861,\n",
              " 'الحين': 37202,\n",
              " 'صاروا': 200395,\n",
              " 'هم': 335687,\n",
              " 'ياخذونها': 415759,\n",
              " 'ليبي': 269220,\n",
              " 'نذل': 319497,\n",
              " 'عايش': 212424,\n",
              " 'في': 233151,\n",
              " 'امريكا': 69302,\n",
              " 'اتصل': 4205,\n",
              " 'بيه': 125525,\n",
              " 'خوووه': 171723,\n",
              " 'قاله': 234823,\n",
              " 'روح': 182330,\n",
              " 'بوكـ': 117877,\n",
              " 'مااات': 270665,\n",
              " 'قصدك': 236791,\n",
              " 'مازلتوو': 277695,\n",
              " 'عايشين': 212456,\n",
              " 'انتو': 71368,\n",
              " 'هههههههههههههه': 336865,\n",
              " 'بعتلنا': 105799,\n",
              " 'مصاري': 300239,\n",
              " 'وما': 393844,\n",
              " 'تجي': 131185,\n",
              " 'نيالوووو': 326606,\n",
              " 'صباح': 200731,\n",
              " 'الورد': 67126,\n",
              " 'يا': 413728,\n",
              " 'حبي': 159316,\n",
              " 'انت': 70722,\n",
              " 'والله': 353554,\n",
              " 'محقق': 292615,\n",
              " 'كونن': 246647,\n",
              " 'شكلك': 197400,\n",
              " 'القاضي': 52819,\n",
              " 'والمحامي': 353958,\n",
              " 'والشهود': 351032,\n",
              " 'والمحكمه': 354008,\n",
              " 'والمتهم': 353885,\n",
              " 'مخابينك': 293168,\n",
              " 'لا': 247660,\n",
              " 'تقذف': 144499,\n",
              " 'جزاك': 154700,\n",
              " 'الله': 56404,\n",
              " 'خير': 171921,\n",
              " 'بدون': 99794,\n",
              " 'دليل': 175112,\n",
              " 'مءكد': 270520,\n",
              " 'قاضي': 234576,\n",
              " 'وادانه': 341991,\n",
              " 'ايون': 78125,\n",
              " 'حبايبي': 159076,\n",
              " 'بحبهم': 97570,\n",
              " 'كلهم': 244866,\n",
              " 'ودابا': 375316,\n",
              " 'دير': 176507,\n",
              " 'معانا': 302285,\n",
              " 'المزيان': 59797,\n",
              " 'وقول': 389146,\n",
              " 'لينا': 270013,\n",
              " 'سر': 188396,\n",
              " 'النجاح': 64745,\n",
              " 'ديالك': 176355,\n",
              " 'وحده': 372859,\n",
              " 'سوالف': 191646,\n",
              " 'النسوان': 64969,\n",
              " 'تفت': 143281,\n",
              " 'القلب': 53608,\n",
              " 'وانا': 356460,\n",
              " 'المحيرني': 58724,\n",
              " 'انك': 73809,\n",
              " 'امس': 69351,\n",
              " 'بتفكر': 94599,\n",
              " 'الموضوع': 64001,\n",
              " 'ده': 175571,\n",
              " 'ببجي': 89208,\n",
              " 'وبلباردو': 362244,\n",
              " 'لودو': 268417,\n",
              " 'رجعنا': 179873,\n",
              " 'للاكل': 260193,\n",
              " 'الصحي': 45975,\n",
              " 'بقوه': 109069,\n",
              " 'لما': 265599,\n",
              " 'نركب': 319779,\n",
              " 'الطاءره': 46929,\n",
              " 'انجوع': 71663,\n",
              " 'تفسير': 143639,\n",
              " 'للموضوع': 265019,\n",
              " 'نبدا': 316575,\n",
              " 'عارفها': 209209,\n",
              " 'بتصير': 93517,\n",
              " 'او': 75024,\n",
              " 'ناكل': 316272,\n",
              " 'قبل': 235195,\n",
              " 'اول': 76052,\n",
              " 'تطلع': 140356,\n",
              " 'نستنا': 320435,\n",
              " 'الوجبه': 66966,\n",
              " 'الحصار': 36194,\n",
              " 'عطي': 215927,\n",
              " 'واحد': 341134,\n",
              " 'وحجمه': 372793,\n",
              " 'دول': 176061,\n",
              " 'ربعهم': 179394,\n",
              " 'باقي': 80694,\n",
              " 'يحطونهم': 426592,\n",
              " 'المتحف': 57624,\n",
              " 'يءرخون': 413698,\n",
              " 'كمجرمين': 245157,\n",
              " 'حرب': 160985,\n",
              " 'اتحاد': 3133,\n",
              " 'الكوره': 55427,\n",
              " 'عزبه': 214652,\n",
              " 'ومعندهم': 399289,\n",
              " 'فكر': 231092,\n",
              " 'تخطيطي': 133502,\n",
              " 'لصالح': 256911,\n",
              " 'المنتخب': 63190,\n",
              " 'بيفكرو': 123748,\n",
              " 'ازاي': 12752,\n",
              " 'يلمو': 439932,\n",
              " 'فلوس': 231698,\n",
              " 'لنفسهم': 267338,\n",
              " 'مكان': 306337,\n",
              " 'فيه': 233860,\n",
              " 'فساد': 229488,\n",
              " 'تستصغرون': 136907,\n",
              " 'مصايب': 300303,\n",
              " 'غيركم': 223308,\n",
              " 'عشان': 215121,\n",
              " 'محد': 292170,\n",
              " 'يزعلكم': 430145,\n",
              " 'بالكلام': 86274,\n",
              " 'اتحداك': 3201,\n",
              " 'اذا': 11065,\n",
              " 'بتقراها': 94799,\n",
              " 'تخربط': 133230,\n",
              " 'قد': 235460,\n",
              " 'الوضع': 67344,\n",
              " 'الاقتصادي': 26384,\n",
              " 'بلبنان': 110341,\n",
              " 'زفت': 184524,\n",
              " 'البنات': 30238,\n",
              " 'عم': 218047,\n",
              " 'يتزو': 423265,\n",
              " 'جوا': 156503,\n",
              " 'حب': 159022,\n",
              " 'زمان': 184842,\n",
              " 'ياهوي': 421008,\n",
              " 'يسلمج': 431796,\n",
              " 'امينه': 70231,\n",
              " 'مره': 296087,\n",
              " 'اخطف': 9804,\n",
              " 'صوبه': 203172,\n",
              " 'يمديني': 440288,\n",
              " 'اخذ': 9458,\n",
              " 'ولين': 393746,\n",
              " 'الحينه': 37210,\n",
              " 'خاطري': 167628,\n",
              " 'ماصارت': 278492,\n",
              " 'هالعهود': 330217,\n",
              " 'العنزي': 49542,\n",
              " 'مليون': 308107,\n",
              " 'منشن': 310122,\n",
              " 'نااااار': 315346,\n",
              " 'وناااار': 401295,\n",
              " 'مش': 298925,\n",
              " 'فاهم': 226986,\n",
              " 'شو': 198595,\n",
              " 'بعمل': 106707,\n",
              " 'مبسوط': 286679,\n",
              " 'ههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههههه': 336963,\n",
              " 'حلوه': 164360,\n",
              " 'يسر': 431400,\n",
              " 'عاشووو': 209385,\n",
              " 'الكل': 54980,\n",
              " 'مو': 311999,\n",
              " 'السنه': 43430,\n",
              " 'طافت': 205391,\n",
              " 'خذنا': 168733,\n",
              " 'تذاكر': 134419,\n",
              " 'ال': 23780,\n",
              " 'اي': 76573,\n",
              " 'بي': 118293,\n",
              " 'ونص': 402885,\n",
              " 'يالسين': 418985,\n",
              " 'داخلين': 172339,\n",
              " 'ببلاش': 89512,\n",
              " 'لانهم': 251607,\n",
              " 'ربع': 179373,\n",
              " 'فلان': 231366,\n",
              " 'وعلان': 385477,\n",
              " 'يدخلون': 428276,\n",
              " 'ويا': 406669,\n",
              " 'اداراه': 10246,\n",
              " 'الفريق': 51754,\n",
              " 'الثاني': 33707,\n",
              " 'مب': 286013,\n",
              " 'الاداره': 24761,\n",
              " 'عندهم': 219168,\n",
              " 'معارف': 302056,\n",
              " 'هو': 337057,\n",
              " 'تويتر': 151197,\n",
              " 'صاير': 200636,\n",
              " 'انستگرام': 72386,\n",
              " 'لو': 268049,\n",
              " 'غلطانه': 222571,\n",
              " 'حاجه': 157847,\n",
              " 'توحشتك': 150318,\n",
              " 'ثاني': 151913,\n",
              " 'ربي': 179525,\n",
              " 'يفرحنا': 437108,\n",
              " 'انا': 70270,\n",
              " 'وياك': 406935,\n",
              " 'كلامك': 244176,\n",
              " 'صحيح': 201356,\n",
              " 'ومفيش': 399550,\n",
              " 'انسان': 72308,\n",
              " 'مهما': 311810,\n",
              " 'كان': 240486,\n",
              " 'بيحب': 120103,\n",
              " 'النكد': 65512,\n",
              " 'يصتعه': 433249,\n",
              " 'لكن': 259587,\n",
              " 'هي': 337489,\n",
              " 'الظروف': 47727,\n",
              " 'وربنا': 377009,\n",
              " 'يسعد': 431524,\n",
              " 'ايامك': 76650,\n",
              " 'تغذينا': 142434,\n",
              " 'سليمه': 190362,\n",
              " 'ومفيده': 399548,\n",
              " 'ماشفنا': 278361,\n",
              " 'شي': 199262,\n",
              " 'حــر': 162969,\n",
              " 'مغــرم': 304142,\n",
              " 'بشوفه': 103983,\n",
              " 'ابلاده': 1919,\n",
              " 'بيشه': 122370,\n",
              " 'واهل': 357520,\n",
              " 'احساسي': 7920,\n",
              " 'ياااحريه': 413955,\n",
              " 'فراس': 228604,\n",
              " 'اسعد': 15260,\n",
              " 'العريضي': 48628,\n",
              " 'مواليد': 312278,\n",
              " 'جرمانا': 154532,\n",
              " 'اعتقلوه': 19321,\n",
              " 'العفاريت': 49040,\n",
              " 'الزرق': 41266,\n",
              " 'بتاريخ': 89822,\n",
              " 'علي': 217609,\n",
              " 'حاجز': 157834,\n",
              " 'قصر': 236813,\n",
              " 'قناعه': 238484,\n",
              " 'محلات': 292679,\n",
              " 'الملابس': 62602,\n",
              " 'الحريمي': 35998,\n",
              " 'لازم': 249950,\n",
              " 'تعمل': 142036,\n",
              " 'قعده': 237432,\n",
              " 'وكراسي': 389735,\n",
              " 'وشيشه': 381685,\n",
              " 'وممكن': 400167,\n",
              " 'عند': 219056,\n",
              " 'الساده': 41912,\n",
              " 'الازواج': 25166,\n",
              " 'الغلابه': 50392,\n",
              " 'جعل': 154947,\n",
              " 'عمررررررررك': 218357,\n",
              " 'طويل': 207849,\n",
              " 'بو': 116591,\n",
              " 'حمد': 164672,\n",
              " 'قرفانيتوا': 236206,\n",
              " 'الاصل': 25871,\n",
              " 'نحط': 318609,\n",
              " 'خطه': 170019,\n",
              " 'بديله': 99935,\n",
              " 'سليم': 190354,\n",
              " 'ماراح': 277249,\n",
              " 'يسوي': 432124,\n",
              " 'تيار': 151290,\n",
              " 'هحلم': 333778,\n",
              " 'وحلمي': 373760,\n",
              " 'مصدقاه': 300461,\n",
              " 'وان': 356458,\n",
              " 'راح': 178352,\n",
              " 'لفين': 258788,\n",
              " 'هفضل': 335095,\n",
              " 'وراهوان': 376908,\n",
              " 'جيتي': 157187,\n",
              " 'دنيا': 175516,\n",
              " 'عليا': 217611,\n",
              " 'طريقي': 206504,\n",
              " 'بردو': 100764,\n",
              " 'مكملاه': 306953,\n",
              " 'يافندم': 417898,\n",
              " 'غلطقصور': 222604,\n",
              " 'ايه': 77951,\n",
              " 'بتتكلم': 90707,\n",
              " 'عليهم': 217922,\n",
              " 'مقر': 305765,\n",
              " 'حكم': 163700,\n",
              " 'بالعالمين': 85091,\n",
              " 'والاداريه': 345997,\n",
              " 'زيهم': 185858,\n",
              " 'زي': 185582,\n",
              " 'مقرين': 305851,\n",
              " 'للنواب': 265217,\n",
              " 'والوزراء': 355892,\n",
              " 'ماشوفتش': 278421,\n",
              " 'النواب': 65655,\n",
              " 'فالاداريه': 225074,\n",
              " 'وهم': 405273,\n",
              " 'ثم': 152257,\n",
              " 'اخدش': 9404,\n",
              " 'لامن': 251318,\n",
              " 'ميزانيه': 314306,\n",
              " 'الدوله': 39361,\n",
              " 'البلدكله': 30090,\n",
              " 'حتي': 160227,\n",
              " 'يوتيوبر': 443169,\n",
              " 'ستانداب': 187588,\n",
              " 'كوميدي': 246550,\n",
              " 'متل': 289959,\n",
              " 'الخلق': 38008,\n",
              " 'مافي': 279849,\n",
              " 'كلو': 244884,\n",
              " 'عالحرق': 210305,\n",
              " 'وشي': 381637,\n",
              " 'بمز': 111837,\n",
              " 'النفس': 65351,\n",
              " 'التفاهه': 32605,\n",
              " 'وتقل': 369266,\n",
              " 'الدم': 39073,\n",
              " 'يعني': 435830,\n",
              " 'نقدر': 324004,\n",
              " 'نقول': 324366,\n",
              " 'ظروف': 208341,\n",
              " 'المباراه': 57345,\n",
              " 'تعطي': 141713,\n",
              " 'شكل': 197383,\n",
              " 'ممكن': 308512,\n",
              " 'نشكل': 321237,\n",
              " 'المدرب': 59061,\n",
              " 'نسبه': 320259,\n",
              " 'يخلف': 427844,\n",
              " 'اهلك': 74783,\n",
              " 'متصور': 288985,\n",
              " 'ياليل': 419968,\n",
              " 'لاعب': 250496,\n",
              " 'يفكر': 437426,\n",
              " 'نفسه': 323527,\n",
              " 'قديش': 235744,\n",
              " 'ناس': 315745,\n",
              " 'بيكونوا': 124411,\n",
              " 'عارفين': 209231,\n",
              " 'الصح': 45908,\n",
              " 'وبيسكتوا': 364019,\n",
              " 'خوف': 171629,\n",
              " 'المجتمع': 58223,\n",
              " 'والمواجهه': 355122,\n",
              " 'رده': 180405,\n",
              " 'الفعل': 51970,\n",
              " 'هتصير': 333083,\n",
              " 'تكلموا': 145821,\n",
              " 'ضد': 204182,\n",
              " 'الخوف': 38220,\n",
              " 'والسكوت': 350524,\n",
              " 'لانو': 251612,\n",
              " 'نحكي': 318676,\n",
              " 'دكتوري': 174901,\n",
              " 'الخرا': 37538,\n",
              " 'امتحانكم': 68909,\n",
              " 'الاتنين': 24182,\n",
              " 'ولو': 393454,\n",
              " 'فش': 229653,\n",
              " 'اضراب': 18103,\n",
              " 'حيبقي': 166155,\n",
              " 'التلاته': 32924,\n",
              " 'متخلي': 288250,\n",
              " 'سواء': 191558,\n",
              " 'وتنهي': 370338,\n",
              " 'ينعن': 441993,\n",
              " 'عرضك': 214248,\n",
              " 'غباءك': 221253,\n",
              " 'بدل': 99519,\n",
              " 'منتا': 309118,\n",
              " 'خنقنا': 171377,\n",
              " 'ومش': 398312,\n",
              " 'فهمين': 232610,\n",
              " 'بتسمعي': 92901,\n",
              " 'راديو': 178484,\n",
              " 'احييييييكي': 8875,\n",
              " 'سنا': 190903,\n",
              " 'المجد': 58238,\n",
              " 'للراديو': 262018,\n",
              " 'عساهم': 214932,\n",
              " 'بحال': 97450,\n",
              " 'واردا': 342462,\n",
              " 'منه': 311089,\n",
              " 'يوفقك': 443622,\n",
              " 'ويسعدك': 409905,\n",
              " 'وينولك': 413053,\n",
              " 'بالك': 86106,\n",
              " 'صح': 201092,\n",
              " 'حاله': 158731,\n",
              " 'منكتبها': 310902,\n",
              " 'لنرتاح': 267187,\n",
              " 'فكره': 231144,\n",
              " 'عبالنا': 212607,\n",
              " 'ماتعرف': 274572,\n",
              " 'تلايط': 146126,\n",
              " 'دب': 173073,\n",
              " 'الاسود': 25675,\n",
              " 'هلهاش': 335635,\n",
              " 'الضحكه': 46715,\n",
              " 'حق': 163256,\n",
              " 'يوم': 443792,\n",
              " 'تحيا': 132806,\n",
              " 'مصر': 300526,\n",
              " 'بجيشها': 97355,\n",
              " 'ورجاله': 377103,\n",
              " 'الاقوياء': 26471,\n",
              " 'بيخلصونه': 120710,\n",
              " 'الارهابيين': 25077,\n",
              " 'المجرمين': 58274,\n",
              " 'التكفريين': 32874,\n",
              " 'يرزقنا': 429495,\n",
              " 'ربفي': 179420,\n",
              " 'وظاءف': 383937,\n",
              " 'لنساء': 267201,\n",
              " 'الباحه': 28220,\n",
              " 'بلجرشي': 110394,\n",
              " 'وظايفنساييه': 383966,\n",
              " 'تنور': 149326,\n",
              " 'خلو': 170830,\n",
              " 'مرحبا': 295522,\n",
              " 'بك': 109164,\n",
              " 'قال': 234717,\n",
              " 'امفكره': 69685,\n",
              " 'فلهويت': 231672,\n",
              " 'عصره': 215515,\n",
              " 'جد': 153879,\n",
              " 'تلمي': 146675,\n",
              " 'فانز': 226907,\n",
              " 'كنزه': 245724,\n",
              " 'مرسلي': 295722,\n",
              " 'الحقد': 36374,\n",
              " 'والكراهيه': 353194,\n",
              " 'الي': 67772,\n",
              " 'نسخه': 320530,\n",
              " 'ويشتمو': 410167,\n",
              " 'خبر': 168054,\n",
              " 'سهيله': 191536,\n",
              " 'وابتسام': 339539,\n",
              " 'تسكت': 137519,\n",
              " 'عمري': 218452,\n",
              " 'درتها': 173759,\n",
              " 'حياتي': 166030,\n",
              " 'نكون': 324763,\n",
              " 'كاءن': 239438,\n",
              " 'غريب': 221874,\n",
              " 'الواحد': 66778,\n",
              " 'محتاج': 291910,\n",
              " 'الصبح': 45849,\n",
              " 'منشطات': 310093,\n",
              " 'يعرف': 435232,\n",
              " 'يصحصح': 433263,\n",
              " 'وبالليل': 358744,\n",
              " 'مهدءات': 311609,\n",
              " 'ومنومات': 400688,\n",
              " 'ينام': 440951,\n",
              " 'وباقي': 358360,\n",
              " 'اليوم': 68212,\n",
              " 'مخدرات': 293464,\n",
              " 'يستحمل': 430747,\n",
              " 'الناس': 64495,\n",
              " 'بيتعامل': 119340,\n",
              " 'معاهم': 302351,\n",
              " 'اقول': 22650,\n",
              " 'رحو': 180205,\n",
              " 'حرور': 161316,\n",
              " 'جزركم': 154738,\n",
              " 'وبعدين': 361662,\n",
              " 'تفاهمو': 143261,\n",
              " 'المحكمه': 58639,\n",
              " 'بتقاضيكم': 94710,\n",
              " 'القرصنه': 53205,\n",
              " 'ياعيال': 417634,\n",
              " 'دحلان': 173356,\n",
              " 'بالضبط': 84829,\n",
              " 'جابها': 152657,\n",
              " 'الجرح': 34391,\n",
              " 'فعلا': 230472,\n",
              " 'انكم': 73917,\n",
              " 'سفيهين': 189389,\n",
              " 'ولك': 392967,\n",
              " 'يامصدي': 420364,\n",
              " 'يامعفن': 420438,\n",
              " 'يابو': 414706,\n",
              " 'هرمونات': 334385,\n",
              " 'بني': 115484,\n",
              " 'صهيون': 203106,\n",
              " 'وين': 412531,\n",
              " 'مايروح': 284164,\n",
              " 'وغلط': 386424,\n",
              " 'لايجتمع': 251936,\n",
              " 'دينان': 176853,\n",
              " 'الحرمين': 35953,\n",
              " 'انتوا': 71369,\n",
              " 'جمعتوا': 155671,\n",
              " 'الاديان': 24836,\n",
              " 'وخالفتوا': 374322,\n",
              " 'الدين': 39540,\n",
              " 'والرسولص': 349976,\n",
              " 'استح': 13678,\n",
              " 'وجهك': 371942,\n",
              " 'خايف': 167984,\n",
              " 'المشار': 60439,\n",
              " 'ومنت': 400285,\n",
              " 'حوار': 165609,\n",
              " 'بين': 124845,\n",
              " 'اثنين': 6091,\n",
              " 'بالديوانيه': 83625,\n",
              " 'يقوله': 438534,\n",
              " 'المعامله': 61313,\n",
              " 'موراضي': 312725,\n",
              " 'يمشيها': 440469,\n",
              " 'اخوك': 10086,\n",
              " 'يرد': 429411,\n",
              " 'عليه': 217846,\n",
              " 'مافيه': 279898,\n",
              " 'ومتمسك': 396574,\n",
              " 'بالقوانين': 86083,\n",
              " 'ودايما': 375493,\n",
              " 'مسوي': 298781,\n",
              " 'رجل': 179942,\n",
              " 'انظباط': 73087,\n",
              " 'ويطبق': 410672,\n",
              " 'القانون': 52869,\n",
              " 'صج': 201062,\n",
              " 'لي': 269092,\n",
              " 'طلبته': 207083,\n",
              " 'للاسف': 259999,\n",
              " 'اصبح': 17440,\n",
              " 'يحمي': 426998,\n",
              " 'عمله': 218628,\n",
              " 'ووطنه': 406253,\n",
              " 'بتطبيق': 93647,\n",
              " 'القوانين': 53882,\n",
              " 'وعدم': 384727,\n",
              " 'تجاوزها': 130717,\n",
              " 'عرفتوا': 214321,\n",
              " 'الفساد': 51801,\n",
              " 'اصلح': 17855,\n",
              " 'نفسك': 323492,\n",
              " 'اولا': 76054,\n",
              " 'مسخره': 298130,\n",
              " 'وليد': 393652,\n",
              " 'مفيش': 305297,\n",
              " 'هزار': 334463,\n",
              " 'فريق': 229331,\n",
              " 'يمكن': 440564,\n",
              " 'يتحسب': 422778,\n",
              " 'النادي': 64464,\n",
              " 'الاهلي': 27580,\n",
              " 'يشفيه': 432774,\n",
              " 'يقومه': 438604,\n",
              " 'بالسلامه': 84219,\n",
              " 'رب': 179227,\n",
              " 'مبروكين': 286587,\n",
              " 'شاء': 193110,\n",
              " 'دكتور': 174878,\n",
              " 'يوفقها': 443636,\n",
              " 'وتشوفونها': 367778,\n",
              " 'اعلي': 20140,\n",
              " 'المراتب': 59297,\n",
              " 'الهلالبطلالسوبر': 66405,\n",
              " 'احتزم': 7407,\n",
              " 'يارفيقي': 416358,\n",
              " 'وحدد': 372842,\n",
              " 'الاتجاه': 24057,\n",
              " 'واحتزم': 341068,\n",
              " 'فالبساطه': 225257,\n",
              " 'وفاليوم': 386853,\n",
              " 'خذ': 168653,\n",
              " 'لزومك': 256137,\n",
              " 'لاتوصي': 249247,\n",
              " 'ولاتبغي': 390853,\n",
              " 'وصاه': 381894,\n",
              " 'اتكل': 5213,\n",
              " 'بالله': 86518,\n",
              " 'وخله': 375004,\n",
              " 'يصير': 433724,\n",
              " 'اخي': 10144,\n",
              " 'مفهمتوش': 305229,\n",
              " 'ومعرفتش': 399124,\n",
              " 'معناه': 303598,\n",
              " 'ماشي': 278443,\n",
              " 'زعما': 184344,\n",
              " 'مستهزاه': 297922,\n",
              " 'الاسم': 25630,\n",
              " 'اكيد': 23723,\n",
              " 'بالعكس': 85309,\n",
              " 'كيبقا': 246844,\n",
              " 'فيا': 233154,\n",
              " 'اسماء': 15616,\n",
              " 'كنسمعها': 245766,\n",
              " 'كيتعقدو': 246912,\n",
              " 'منها': 311090,\n",
              " 'اصحابها': 17521,\n",
              " 'وهادشي': 404298,\n",
              " 'مسءوليه': 296865,\n",
              " 'الوالدين': 66894,\n",
              " 'مكيختاروش': 307123,\n",
              " 'لاولادهم': 251808,\n",
              " 'ازيد': 13079,\n",
              " 'كلامج': 244160,\n",
              " 'شنو': 198193,\n",
              " 'علاقه': 217069,\n",
              " 'الكليه': 55146,\n",
              " 'بالسنه': 84279,\n",
              " 'كثر': 241848,\n",
              " 'الاجرام': 24303,\n",
              " 'حمروا': 164755,\n",
              " 'العين': 49854,\n",
              " 'وفعلوا': 387482,\n",
              " 'الجميع': 34870,\n",
              " 'وبس': 361055,\n",
              " 'واسطات': 343254,\n",
              " 'مل': 307194,\n",
              " 'دمرت': 175236,\n",
              " 'ديرتنا': 176540,\n",
              " 'هههههه': 336736,\n",
              " 'ديما': 176776,\n",
              " 'النيفو': 65855,\n",
              " 'طالع': 205519,\n",
              " 'يغنيك': 436421,\n",
              " 'يابومنصور': 414906,\n",
              " 'وكل': 390033,\n",
              " 'عام': 212059,\n",
              " 'وانت': 356562,\n",
              " 'بخير': 99025,\n",
              " 'يتقبل': 424197,\n",
              " 'منك': 310887,\n",
              " 'الصيام': 46573,\n",
              " 'والقيام': 353053,\n",
              " 'هلا': 335289,\n",
              " 'معك': 303266,\n",
              " 'تلات': 145989,\n",
              " 'ثواني': 152363,\n",
              " 'نعم': 322762,\n",
              " 'الترفيه': 31819,\n",
              " 'صناعه': 202971,\n",
              " 'مجالها': 290993,\n",
              " 'كبير': 241096,\n",
              " 'جدا': 153880,\n",
              " 'اقتصادها': 22027,\n",
              " 'مبني': 287000,\n",
              " 'السياحه': 43787,\n",
              " 'حكومتنا': 163797,\n",
              " 'هذي': 334245,\n",
              " 'الصناعه': 46419,\n",
              " 'نهني': 325842,\n",
              " 'المملكه': 62999,\n",
              " 'العربيه': 48520,\n",
              " 'السعوديه': 42716,\n",
              " 'اهتمامها': 74533,\n",
              " 'بهذا': 116319,\n",
              " 'المجال': 58182,\n",
              " 'شالله': 193770,\n",
              " 'بتخلق': 91759,\n",
              " 'فرص': 228956,\n",
              " 'تجاريه': 130617,\n",
              " 'وظيفيه': 384112,\n",
              " 'بيستفيد': 121795,\n",
              " 'المواطن': 63772,\n",
              " 'السعودي': 42703,\n",
              " 'بيرد': 121218,\n",
              " 'ساعات': 186406,\n",
              " 'ليه': 270082,\n",
              " 'فاكرني': 224992,\n",
              " 'مضاد': 301041,\n",
              " 'حيوي': 167160,\n",
              " 'ايات': 76602,\n",
              " 'المنافق': 63120,\n",
              " 'هذولا': 334228,\n",
              " 'فيهم': 233963,\n",
              " 'شعب': 196159,\n",
              " 'جنگل': 156337,\n",
              " 'ياحلوه': 415517,\n",
              " 'طير': 208019,\n",
              " 'قلبه': 237870,\n",
              " 'اكل': 23501,\n",
              " 'المسكين': 60301,\n",
              " 'سخافه': 188235,\n",
              " 'برلمانيه': 101213,\n",
              " 'خلصت': 170539,\n",
              " 'مشاكل': 299066,\n",
              " 'الكويت': 55605,\n",
              " 'ومابقي': 394015,\n",
              " 'الا': 23794,\n",
              " 'حجاب': 160267,\n",
              " 'الوزيره': 67205,\n",
              " 'الكدب': 54559,\n",
              " 'والافتراء': 346431,\n",
              " 'اوعي': 75843,\n",
              " 'تفكري': 143881,\n",
              " 'حد': 160484,\n",
              " 'هيصدقكم': 338369,\n",
              " 'خلااااص': 170371,\n",
              " 'اشكالكم': 16957,\n",
              " 'معدتشبيتسمع': 302704,\n",
              " 'لها': 267526,\n",
              " 'كلام': 244145,\n",
              " 'نزل': 319963,\n",
              " 'النهارده': 65603,\n",
              " 'الشغل': 44947,\n",
              " 'بشراب': 103540,\n",
              " 'فرده': 228876,\n",
              " 'وفرده': 387155,\n",
              " 'كحلي': 242041,\n",
              " 'الدنيا': 39141,\n",
              " 'زعلانين': 184281,\n",
              " 'منو': 311214,\n",
              " 'الزعيم': 41345,\n",
              " 'الهلال': 66369,\n",
              " 'الزعيمه': 41350,\n",
              " 'وفكونا': 387574,\n",
              " 'هالامور': 328223,\n",
              " 'اكبر': 22822,\n",
              " 'هالمواضيع': 331253,\n",
              " 'والهلال': 355702,\n",
              " 'يقولك': 438515,\n",
              " 'المثل': 58138,\n",
              " 'النصيحه': 65143,\n",
              " 'بفلوس': 108170,\n",
              " 'والحين': 349104,\n",
              " 'بلاش': 110082,\n",
              " 'اتركو': 3773,\n",
              " 'التخبط': 31563,\n",
              " 'والتلزق': 348067,\n",
              " 'بالاخوان': 80967,\n",
              " 'المفلسون': 62078,\n",
              " 'وارجعو': 342420,\n",
              " 'لربعكم': 255768,\n",
              " 'واهلكم': 357525,\n",
              " 'بالخليج': 83330,\n",
              " 'الشوط': 45467,\n",
              " 'التاني': 31180,\n",
              " 'كله': 244826,\n",
              " 'ليفربول': 269772,\n",
              " 'صلاح': 202718,\n",
              " 'هايسجل': 332064,\n",
              " 'هيووووووووووووو': 339135,\n",
              " 'ريتني': 182986,\n",
              " 'حكيت': 163837,\n",
              " 'زمااان': 184819,\n",
              " 'البطان': 29597,\n",
              " 'الاول': 27841,\n",
              " 'طلع': 207165,\n",
              " 'بلهون': 111063,\n",
              " 'جربوا': 154382,\n",
              " 'سانك': 186977,\n",
              " 'يو': 443065,\n",
              " 'فيري': 233449,\n",
              " 'ماتش': 274129,\n",
              " 'مصطلح': 300757,\n",
              " 'فتوها': 227837,\n",
              " 'صراحه': 201825,\n",
              " 'لحالو': 254483,\n",
              " 'مثير': 290882,\n",
              " 'للقرف': 263732,\n",
              " 'تءاخذنا': 126242,\n",
              " 'صاروخين': 200401,\n",
              " 'ضربينهم': 204325,\n",
              " 'النقب': 65449,\n",
              " 'اظن': 18871,\n",
              " 'الاتحادالاتفاق': 24067,\n",
              " 'عون': 219907,\n",
              " 'الاتحاد': 24064,\n",
              " 'لعيبه': 258015,\n",
              " 'عاهات': 212233,\n",
              " 'بالملعب': 87361,\n",
              " 'صدقت': 201600,\n",
              " 'العامر': 48038,\n",
              " 'بدر': 99297,\n",
              " 'عيال': 220057,\n",
              " 'خاله': 167839,\n",
              " 'المشعان': 60599,\n",
              " 'بشويخ': 104021,\n",
              " 'مشعل': 299628,\n",
              " 'وغازي': 386120,\n",
              " 'ومشهور': 398519,\n",
              " 'بسالكم': 102186,\n",
              " 'سءال': 185923,\n",
              " 'شبكتكم': 194300,\n",
              " 'هالايام': 328280,\n",
              " 'فور': 232828,\n",
              " 'زق': 184571,\n",
              " 'حيزبونه': 166537,\n",
              " 'حقنه': 163427,\n",
              " 'شرجيه': 195440,\n",
              " 'افترضنا': 21059,\n",
              " 'بواب': 116607,\n",
              " 'ابن': 2049,\n",
              " 'فيفي': 233616,\n",
              " 'مهنه': 311900,\n",
              " 'شريفه': 195864,\n",
              " 'بياكل': 118470,\n",
              " 'هز': 334450,\n",
              " 'وسط': 379343,\n",
              " 'امو': 69998,\n",
              " 'واختو': 341663,\n",
              " 'ههههههه': 336773,\n",
              " 'تصدق': 139254,\n",
              " 'خوك': 171661,\n",
              " 'لنه': 267450,\n",
              " 'يبي': 422181,\n",
              " 'يبيض': 422202,\n",
              " 'وجهه': 371970,\n",
              " 'قدامك': 235496,\n",
              " 'وبعد': 361632,\n",
              " 'يومين': 443905,\n",
              " 'حتا': 159537,\n",
              " 'وتساب': 367170,\n",
              " 'سامعات': 186857,\n",
              " 'بعرف': 106155,\n",
              " 'راحوا': 178429,\n",
              " 'بالحب': 82901,\n",
              " 'وهو': 405429,\n",
              " 'حبيبي': 159401,\n",
              " 'وانتي': 356725,\n",
              " 'بعد': 105929,\n",
              " 'شكلج': 197397,\n",
              " 'نسيتي': 320843,\n",
              " 'محلب': 292697,\n",
              " 'يبكلمنا': 421930,\n",
              " 'كده': 242166,\n",
              " 'الزول': 41730,\n",
              " 'غاشينا': 220980,\n",
              " 'هذا': 334115,\n",
              " 'شغلك': 196543,\n",
              " 'بتفهمنا': 94649,\n",
              " 'احني': 8749,\n",
              " 'رشحناكم': 180826,\n",
              " 'ماعاد': 278969,\n",
              " 'فهمنا': 232571,\n",
              " 'شيء': 199263,\n",
              " 'عدل': 213684,\n",
              " 'بالدنيا': 83539,\n",
              " 'كنت': 245549,\n",
              " 'بقلك': 108903,\n",
              " 'ردي': 180452,\n",
              " 'العدل': 48357,\n",
              " 'فقط': 230948,\n",
              " 'غير': 223204,\n",
              " 'وعند': 385770,\n",
              " 'بضيع': 104828,\n",
              " 'تتعرفي': 129594,\n",
              " 'توني': 151065,\n",
              " 'كروس': 242842,\n",
              " 'ينزلون': 441533,\n",
              " 'تميم': 147622,\n",
              " 'ماقول': 280207,\n",
              " 'ياخسي': 415810,\n",
              " 'قولتهم': 238891,\n",
              " 'شان': 193868,\n",
              " 'انام': 70497,\n",
              " 'مبجر': 286369,\n",
              " 'اسمع': 15690,\n",
              " 'صوتج': 203202,\n",
              " 'واتام': 339898,\n",
              " 'ليش': 269570,\n",
              " 'كذه': 242333,\n",
              " 'جننتني': 156210,\n",
              " 'واتعبت': 340288,\n",
              " 'حالي': 158749,\n",
              " 'خلي': 170900,\n",
              " 'خيالك': 171844,\n",
              " 'الاقل': 26439,\n",
              " 'يمر': 440294,\n",
              " 'مرور': 296170,\n",
              " 'عابر': 208838,\n",
              " 'الشيخ': 45575,\n",
              " 'اديني': 11041,\n",
              " 'مورو': 312791,\n",
              " 'بيقولك': 124138,\n",
              " 'المثليه': 58152,\n",
              " 'حريه': 161423,\n",
              " 'ومسءليه': 397892,\n",
              " 'ااااه': 399,\n",
              " 'ياخواات': 415866,\n",
              " 'وياولاد': 407088,\n",
              " 'الوخه': 67071,\n",
              " 'الرقابه': 40534,\n",
              " 'اجودعان': 6881,\n",
              " 'محدش': 292201,\n",
              " 'يقواي': 438465,\n",
              " 'بقي': 109079,\n",
              " 'دين': 176828,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "cv.vocabulary_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q1P4FgoZoa9S"
      },
      "outputs": [],
      "source": [
        "x_train_cv = cv.transform(x_train)\n",
        "x_test_cv = cv.transform(x_test) "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Models training"
      ],
      "metadata": {
        "id": "M81Fcquvoa9U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf_MNB = MultinomialNB()\n",
        "clf_LR = LogisticRegression()\n",
        "clf_SVM=SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3, random_state=42,\n",
        "                      # max_iter=100,\n",
        "                      tol=None)\n",
        "clf_list = [ clf_MNB, clf_LR, clf_SVM]\n",
        "\n",
        "# Compare evaluation of the different classifiers\n",
        "clf_evaluation = {}\n",
        "clf_report = {}\n",
        "\n",
        "for clf in clf_list:\n",
        "    clf_evaluation[clf.__class__.__name__], clf_report[clf.__class__.__name__] = train_predict(clf,\n",
        "                                                                                              x_train_cv,\n",
        "                                                                                              np.argmax(y_train.to_numpy(), axis=1),\n",
        "                                                                                              x_test_cv,\n",
        "                                                                                              np.argmax(y_test.to_numpy(), axis=1)\n",
        "                                                                                              )\n",
        "    print(clf_evaluation )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70a42a5f-aa35-40e1-f77b-aa847a427fb0",
        "id": "_Vp0cMQ6oa9U"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'MultinomialNB': {'train_time': 0.85, 'pred_time': 0.43, 'acc_train': 0.49, 'acc_test': 0.4, 'f1_train': 0.37, 'f1_test': 0.26}}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'MultinomialNB': {'train_time': 0.85, 'pred_time': 0.43, 'acc_train': 0.49, 'acc_test': 0.4, 'f1_train': 0.37, 'f1_test': 0.26}, 'LogisticRegression': {'train_time': 291.0, 'pred_time': 0.42, 'acc_train': 0.61, 'acc_test': 0.51, 'f1_train': 0.58, 'f1_test': 0.47}}\n",
            "{'MultinomialNB': {'train_time': 0.85, 'pred_time': 0.43, 'acc_train': 0.49, 'acc_test': 0.4, 'f1_train': 0.37, 'f1_test': 0.26}, 'LogisticRegression': {'train_time': 291.0, 'pred_time': 0.42, 'acc_train': 0.61, 'acc_test': 0.51, 'f1_train': 0.58, 'f1_test': 0.47}, 'SGDClassifier': {'train_time': 3262.03, 'pred_time': 0.52, 'acc_train': 0.74, 'acc_test': 0.49, 'f1_train': 0.74, 'f1_test': 0.45}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_evaluation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e66b46cf-a893-46ef-ac26-4a93818a5015",
        "id": "7IoQhe00oa9V"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'LogisticRegression': {'acc_test': 0.51,\n",
              "  'acc_train': 0.61,\n",
              "  'f1_test': 0.47,\n",
              "  'f1_train': 0.58,\n",
              "  'pred_time': 0.42,\n",
              "  'train_time': 291.0},\n",
              " 'MultinomialNB': {'acc_test': 0.4,\n",
              "  'acc_train': 0.49,\n",
              "  'f1_test': 0.26,\n",
              "  'f1_train': 0.37,\n",
              "  'pred_time': 0.43,\n",
              "  'train_time': 0.85},\n",
              " 'SGDClassifier': {'acc_test': 0.49,\n",
              "  'acc_train': 0.74,\n",
              "  'f1_test': 0.45,\n",
              "  'f1_train': 0.74,\n",
              "  'pred_time': 0.52,\n",
              "  'train_time': 3262.03}}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "legend = []\n",
        "for i in range(len(clf_list)):\n",
        "    legend.append(clf_list[i].__class__.__name__)\n",
        "def eval_plot(labels, y_label, title):\n",
        "\n",
        "    clf1 = [clf_evaluation[legend[0]][labels[0]], clf_evaluation[legend[0]][labels[1]]]\n",
        "    clf2 = [clf_evaluation[legend[1]][labels[0]], clf_evaluation[legend[1]][labels[1]]]\n",
        "    clf3 = [clf_evaluation[legend[2]][labels[0]], clf_evaluation[legend[2]][labels[1]]]\n",
        "\n",
        "    x = np.arange(len(labels))  # the label locations\n",
        "    width = 0.2  # the width of the bars\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.bar(x - width/2, clf1, width, label=legend[0])\n",
        "    ax.bar(x + width/2, clf2, width, label=legend[1])\n",
        "    ax.bar(x + width*3/2, clf3, width, label=legend[2])\n",
        "\n",
        "    # Add labels, title, and legend\n",
        "    ax.set_ylabel(y_label)\n",
        "    ax.set_title(title)\n",
        "    ax.set_xticks(x)\n",
        "    ax.set_xticklabels(labels)\n",
        "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "\n",
        "    # Add bars annotation\n",
        "    for i in ax.patches:\n",
        "        # get_x pulls left or right; get_height pushes up or down\n",
        "        ax.text(i.get_x()+(width/2), i.get_height()+0.01,\n",
        "                str(i.get_height()), fontsize=11,\n",
        "                color='black', ha='center')\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "AXH4ZJRYoa9W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['train_time', 'pred_time']\n",
        "y_label = 'Time'\n",
        "title = 'Train/Test Time per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "a23fe3dc-4dfd-4dbe-bd27-d87998f0856c",
        "id": "-y19Tb5moa9X"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['acc_train', 'acc_test']\n",
        "y_label = 'Acc_Score'\n",
        "title = 'Train/Test Accuracy per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "818fddfb-89bc-4e8d-ad1d-efd22661bbc9",
        "id": "Utw3eLBwoa9Y"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['f1_train', 'f1_test']\n",
        "y_label = 'F1_Score'\n",
        "title = 'Train/Test F1_Score per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "fce31d23-c02f-46c3-ca6f-831d6f059e09",
        "id": "KFgmCJI4oa9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0W_4QqXUF3M"
      },
      "source": [
        "#**Load cleaned data second approach**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "maZ4Bq2FUF3O"
      },
      "outputs": [],
      "source": [
        "clean_df_train=pd.read_csv('/content/drive/MyDrive/Arabic Dialect/cleaned tweets train set 2.csv',lineterminator='\\n')\n",
        "clean_df_test=pd.read_csv('/content/drive/MyDrive/Arabic Dialect/cleaned tweets Test set 2.csv',lineterminator='\\n')\n",
        "\n",
        "clean_df_train=clean_df_train.dropna()\n",
        "clean_df_test=clean_df_test.dropna()\n",
        "\n",
        "x_train=clean_df_train['tweet']\n",
        "y_train=pd.get_dummies(clean_df_train['dialect'])\n",
        "x_test=clean_df_test['tweet']\n",
        "y_test=pd.get_dummies(clean_df_test['dialect'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##ConutVectorizer\n",
        "###ngram_range = (1, 1) \n"
      ],
      "metadata": {
        "id": "Gzdvq0yYUF3d"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d79d358c-c77e-4374-dab1-bb739272e0aa",
        "id": "_RgSBiMWUF3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vocabulary :  228088\n"
          ]
        }
      ],
      "source": [
        "cv = CountVectorizer(binary=False,ngram_range=(1,1),\n",
        "\n",
        "                     )\n",
        "cv.fit(x_train)\n",
        "print(\"vocabulary : \",len(cv.vocabulary_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40f5d820-33f7-4c9a-9dee-613671588fcb",
        "id": "mTaEztWaUF3d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'لكن': 132240,\n",
              " 'ال': 18888,\n",
              " 'نهاي': 178772,\n",
              " 'ينتفض': 225627,\n",
              " 'يغير': 222302,\n",
              " 'يعني': 221833,\n",
              " 'هذا': 186502,\n",
              " 'محسوب': 154229,\n",
              " 'علي': 108904,\n",
              " 'بشر': 34110,\n",
              " 'حي': 71032,\n",
              " 'ون': 201724,\n",
              " 'وحشي': 195123,\n",
              " 'تطلب': 52842,\n",
              " 'من': 165865,\n",
              " 'غرب': 111931,\n",
              " 'يحترمكم': 214705,\n",
              " 'يءمن': 204035,\n",
              " 'دين': 79013,\n",
              " 'كم': 125501,\n",
              " 'ولاينعتكم': 199064,\n",
              " 'إرهاب': 1588,\n",
              " 'مبين': 150006,\n",
              " 'كلام': 124780,\n",
              " 'خليجي': 74456,\n",
              " 'يسلملي': 218980,\n",
              " 'مرور': 157102,\n",
              " 'روح': 82731,\n",
              " 'حلو': 69717,\n",
              " 'وين': 203806,\n",
              " 'هل': 187473,\n",
              " 'غيب': 112952,\n",
              " 'أخ': 815,\n",
              " 'محمد': 154494,\n",
              " 'ياخي': 205438,\n",
              " 'إرهابي': 1589,\n",
              " 'إذ': 1571,\n",
              " 'كان': 122674,\n",
              " 'عراقي': 106652,\n",
              " 'سعودي': 87896,\n",
              " 'فلسطيني': 116387,\n",
              " 'مشكل': 159747,\n",
              " 'باب': 27911,\n",
              " 'فرضخليجي': 115108,\n",
              " 'ماعنده': 142397,\n",
              " 'رحم': 81232,\n",
              " 'مساس': 157740,\n",
              " 'بقدسيتك': 35807,\n",
              " 'اله': 20047,\n",
              " 'يرضي': 217307,\n",
              " 'عن': 109892,\n",
              " 'خلص': 74221,\n",
              " 'مشاكل': 159228,\n",
              " 'عرب': 106682,\n",
              " 'اختلف': 8534,\n",
              " 'نا': 171234,\n",
              " 'بجنسيت': 31378,\n",
              " 'أبو': 695,\n",
              " 'طفل': 100737,\n",
              " 'مغرد': 162902,\n",
              " 'ين': 225345,\n",
              " 'سال': 86032,\n",
              " 'رجح': 81011,\n",
              " 'كارث': 122209,\n",
              " 'الي': 20117,\n",
              " 'وصل': 196620,\n",
              " 'مطلب': 161157,\n",
              " 'يقدم': 223147,\n",
              " 'استقال': 12282,\n",
              " 'وفو': 197622,\n",
              " 'اه': 24733,\n",
              " 'اعتذار': 15593,\n",
              " 'عيون': 111222,\n",
              " 'أنا': 1314,\n",
              " 'ماعندي': 142415,\n",
              " 'شيء': 95526,\n",
              " 'مع': 161425,\n",
              " 'عكس': 108375,\n",
              " 'متابعيني': 150080,\n",
              " 'عتز': 105960,\n",
              " 'في': 117391,\n",
              " 'هم': 187870,\n",
              " 'صافي': 96369,\n",
              " 'بن': 37402,\n",
              " 'يمكن': 225188,\n",
              " 'سءال': 85546,\n",
              " 'فات': 113337,\n",
              " 'كثير': 123549,\n",
              " 'يصور': 220249,\n",
              " 'شنو': 94629,\n",
              " 'موقف': 169528,\n",
              " 'يوثق': 227379,\n",
              " 'طول': 101422,\n",
              " 'مفروض': 163343,\n",
              " 'حتي': 67054,\n",
              " 'مصور': 160648,\n",
              " 'يحال': 214581,\n",
              " 'قضاء': 120202,\n",
              " 'لان': 129666,\n",
              " 'نظر': 176618,\n",
              " 'ات': 3302,\n",
              " 'تستنجد': 50587,\n",
              " 'اتجاه': 3567,\n",
              " 'كاميرا': 122657,\n",
              " 'أول': 1466,\n",
              " 'ان': 21495,\n",
              " 'رد': 81340,\n",
              " 'رجل': 81094,\n",
              " 'جنوبي': 64054,\n",
              " 'والي': 192250,\n",
              " 'ذكر': 79410,\n",
              " 'حجاب': 67090,\n",
              " 'ها': 179687,\n",
              " 'ثاني': 60993,\n",
              " 'أنت': 1326,\n",
              " 'شدعوه': 92468,\n",
              " 'صاير': 96498,\n",
              " 'محامي': 153804,\n",
              " 'كوهين': 126814,\n",
              " 'صحيح': 97007,\n",
              " 'مقول': 164265,\n",
              " 'عراق': 106641,\n",
              " 'بلد': 36682,\n",
              " 'غراءب': 111875,\n",
              " 'عجاءب': 106080,\n",
              " 'موضوع': 169330,\n",
              " 'جد': 62371,\n",
              " 'حساس': 68046,\n",
              " 'يحير': 215477,\n",
              " 'اتفق': 5409,\n",
              " 'بس': 33535,\n",
              " 'اتروح': 4376,\n",
              " 'زايد': 83785,\n",
              " 'عادي': 102290,\n",
              " 'لا': 127681,\n",
              " 'شاء': 90893,\n",
              " 'اخو': 9026,\n",
              " 'يجمعنه': 214326,\n",
              " 'كل': 124693,\n",
              " 'جحيم': 62360,\n",
              " 'يسعد': 218784,\n",
              " 'مساك': 157791,\n",
              " 'سيد': 90443,\n",
              " 'حرف': 67653,\n",
              " 'حزين': 67997,\n",
              " 'عدول': 106466,\n",
              " 'لب': 130383,\n",
              " 'ورم': 195798,\n",
              " 'عدن': 106437,\n",
              " 'خل': 74100,\n",
              " 'اتونس': 6354,\n",
              " 'مارضين': 140466,\n",
              " 'فهاي': 116917,\n",
              " 'سفر': 88075,\n",
              " 'اسم': 13146,\n",
              " 'كيف': 127367,\n",
              " 'روع': 82912,\n",
              " 'تواصل': 59562,\n",
              " 'بنت': 37512,\n",
              " 'عم': 109207,\n",
              " 'ماخذ': 139689,\n",
              " 'اي': 26382,\n",
              " 'وحد': 195022,\n",
              " 'أترك': 714,\n",
              " 'فتر': 114407,\n",
              " 'ماسال': 140763,\n",
              " 'مايستحق': 147233,\n",
              " 'اهتمام': 24822,\n",
              " 'ياكل': 207655,\n",
              " 'خيرن': 75209,\n",
              " 'يهين': 227257,\n",
              " 'موظف': 169386,\n",
              " 'عبال': 105494,\n",
              " 'يرجع': 217046,\n",
              " 'صدام': 97101,\n",
              " 'حسين': 68268,\n",
              " 'حكم': 69299,\n",
              " 'رب': 80603,\n",
              " 'يعطيك': 221625,\n",
              " 'يعطي': 221620,\n",
              " 'صح': 96820,\n",
              " 'قناع': 121168,\n",
              " 'تنصح': 58424,\n",
              " 'ولاتنصح': 198473,\n",
              " 'ملاحظ': 165119,\n",
              " 'تستحق': 50305,\n",
              " 'التفات': 19111,\n",
              " 'ماضي': 141732,\n",
              " 'داءم': 75323,\n",
              " 'ريح': 83304,\n",
              " 'خيان': 75105,\n",
              " 'ياله': 209275,\n",
              " 'بدا': 31997,\n",
              " 'حراك': 67520,\n",
              " 'تطور': 52943,\n",
              " 'جماع': 63487,\n",
              " 'قدر': 119195,\n",
              " 'عقول': 108300,\n",
              " 'مصيب': 160678,\n",
              " 'شعب': 93152,\n",
              " 'يشوف': 219796,\n",
              " 'ابن': 2746,\n",
              " 'مرجع': 156565,\n",
              " 'نبي': 172381,\n",
              " 'دمر': 77659,\n",
              " 'منصور': 167213,\n",
              " 'محمدنه': 154656,\n",
              " 'ياخذ': 205292,\n",
              " 'سل': 88473,\n",
              " 'يا': 204055,\n",
              " 'عمي': 109803,\n",
              " 'خوش': 74973,\n",
              " 'بعد': 35015,\n",
              " 'مت': 150028,\n",
              " 'در': 76378,\n",
              " 'ور': 195604,\n",
              " 'نشوف': 175885,\n",
              " 'نسمع': 175476,\n",
              " 'جاسم': 61667,\n",
              " 'يحب': 214601,\n",
              " 'صور': 98318,\n",
              " 'رايح': 80512,\n",
              " 'ميغلط': 170676,\n",
              " 'حروف': 67781,\n",
              " 'درس': 76572,\n",
              " 'لازم': 129126,\n",
              " 'بصر': 34352,\n",
              " 'دزين': 76799,\n",
              " 'شوي': 95460,\n",
              " 'أستاذ': 944,\n",
              " 'نعم': 176976,\n",
              " 'يزيدني': 217862,\n",
              " 'فخر': 114694,\n",
              " 'تستاهلون': 50269,\n",
              " 'سوي': 90176,\n",
              " 'إيران': 1772,\n",
              " 'مك': 164316,\n",
              " 'يشافيك': 219262,\n",
              " 'مرض': 156795,\n",
              " 'دجل': 76194,\n",
              " 'سبحان': 86544,\n",
              " 'قبل': 118937,\n",
              " 'سن': 89238,\n",
              " 'كن': 125727,\n",
              " 'مجموع': 153582,\n",
              " 'أطفال': 1061,\n",
              " 'هس': 186869,\n",
              " 'صر': 97292,\n",
              " 'تكلم': 56274,\n",
              " 'مذهب': 156167,\n",
              " 'دمرتوه': 77667,\n",
              " 'خلي': 74410,\n",
              " 'عالم': 104510,\n",
              " 'تضحك': 52511,\n",
              " 'شاذ': 91004,\n",
              " 'يتعذر': 212854,\n",
              " 'طيب': 101521,\n",
              " 'مرجعي': 156574,\n",
              " 'هءلاء': 179683,\n",
              " 'أخطر': 829,\n",
              " 'داعش': 75579,\n",
              " 'بق': 35731,\n",
              " 'دون': 78445,\n",
              " 'مبروك': 149440,\n",
              " 'خالو': 72571,\n",
              " 'حضر': 68547,\n",
              " 'عيدي': 110946,\n",
              " 'اجاك': 6558,\n",
              " 'عيد': 110818,\n",
              " 'منو': 167931,\n",
              " 'يخاف': 215526,\n",
              " 'تره': 49563,\n",
              " 'خبلتوها': 72811,\n",
              " 'حنان': 70295,\n",
              " 'اتهمبل': 6174,\n",
              " 'واني': 192471,\n",
              " 'شوف': 95254,\n",
              " 'واضح': 191915,\n",
              " 'ثقاف': 61127,\n",
              " 'بداي': 32031,\n",
              " 'ظاهر': 101712,\n",
              " 'ملتزم': 165293,\n",
              " 'الفتلاوي': 19741,\n",
              " 'شخصي': 92353,\n",
              " 'عام': 105139,\n",
              " 'حق': 69004,\n",
              " 'ينقد': 226557,\n",
              " 'ياري': 205898,\n",
              " 'لو': 133136,\n",
              " 'اتجوزن': 3653,\n",
              " 'الوا': 20085,\n",
              " 'يارب': 205703,\n",
              " 'أمن': 1300,\n",
              " 'امان': 20325,\n",
              " 'مصر': 160282,\n",
              " 'غالي': 111466,\n",
              " 'غريب': 112141,\n",
              " 'سكوت': 88429,\n",
              " 'جناب': 63843,\n",
              " 'ذهب': 79617,\n",
              " 'أعرف': 1091,\n",
              " 'شيحبون': 95648,\n",
              " 'لايريدون': 130048,\n",
              " 'رياض': 83098,\n",
              " 'افن': 17333,\n",
              " 'ولاثقافه': 198495,\n",
              " 'ولاتعليم': 198389,\n",
              " 'ولامطبقين': 198823,\n",
              " 'ملاعب': 165171,\n",
              " 'حوز': 70917,\n",
              " 'حير': 71505,\n",
              " 'هذه': 186584,\n",
              " 'مراسيم': 156284,\n",
              " 'يتبع': 211636,\n",
              " 'مكن': 164868,\n",
              " 'توضيح': 60004,\n",
              " 'صاحب': 96117,\n",
              " 'تغريد': 54246,\n",
              " 'قل': 120683,\n",
              " 'ناس': 171530,\n",
              " 'سذج': 87394,\n",
              " 'ركز': 82276,\n",
              " 'شبيه': 91878,\n",
              " 'شريف': 92941,\n",
              " 'يم': 224813,\n",
              " 'كعب': 124521,\n",
              " 'عرف': 106847,\n",
              " 'هنيالك': 188690,\n",
              " 'ياعراقبعدين': 206890,\n",
              " 'حج': 67088,\n",
              " 'تصوير': 52413,\n",
              " 'أفلام': 1171,\n",
              " 'ناشر': 171645,\n",
              " 'أم': 1270,\n",
              " 'غير': 113014,\n",
              " 'موزون': 169128,\n",
              " 'اوزن': 25714,\n",
              " 'أقوال': 1207,\n",
              " 'أفعال': 1160,\n",
              " 'مانتم': 144573,\n",
              " 'ظل': 101863,\n",
              " 'سوالف': 89770,\n",
              " 'حال': 65736,\n",
              " 'تنسب': 58277,\n",
              " 'يحكمك': 215211,\n",
              " 'احن': 8168,\n",
              " 'عافي': 102583,\n",
              " 'رجال': 80967,\n",
              " 'تحميك': 47390,\n",
              " 'ذل': 79534,\n",
              " 'تليق': 57011,\n",
              " 'بعض': 35219,\n",
              " 'يجيك': 214485,\n",
              " 'بطر': 34642,\n",
              " 'لاتدخل': 128092,\n",
              " 'سياس': 90313,\n",
              " 'هذول': 186593,\n",
              " 'راح': 79988,\n",
              " 'نحر': 173781,\n",
              " 'بي': 40229,\n",
              " 'فلسطين': 116371,\n",
              " 'أمين': 1312,\n",
              " 'ينعاد': 226333,\n",
              " 'تحقيق': 47221,\n",
              " 'ماتمناه': 138403,\n",
              " 'مبارك': 149043,\n",
              " 'سلام': 88515,\n",
              " 'أنتم': 1329,\n",
              " 'ألف': 1244,\n",
              " 'خير': 75146,\n",
              " 'بال': 28671,\n",
              " 'عساك': 107244,\n",
              " 'عواد': 110397,\n",
              " 'ماتمنونه': 138412,\n",
              " 'عسا': 107227,\n",
              " 'اخلاقك': 8951,\n",
              " 'اهلن': 25009,\n",
              " 'ترفل': 49312,\n",
              " 'عز': 107047,\n",
              " 'سعاده': 87745,\n",
              " 'وسط': 196120,\n",
              " 'محب': 153841,\n",
              " 'احباب': 7143,\n",
              " 'فدي': 114803,\n",
              " 'عمر': 109434,\n",
              " 'حبيب': 66249,\n",
              " 'معارض': 161472,\n",
              " 'حقيقي': 69200,\n",
              " 'بقي': 35995,\n",
              " 'سوريا': 89930,\n",
              " 'طلع': 100960,\n",
              " 'خون': 75026,\n",
              " 'باع': 28527,\n",
              " 'مرتزق': 156483,\n",
              " 'ليس': 133861,\n",
              " 'حب': 66042,\n",
              " 'أسد': 954,\n",
              " 'حقيق': 69175,\n",
              " 'مو': 168513,\n",
              " 'اتلومني': 5826,\n",
              " 'اح': 7077,\n",
              " 'دا': 75308,\n",
              " 'يصير': 220320,\n",
              " 'بين': 43765,\n",
              " 'اكو': 18784,\n",
              " 'قسم': 119940,\n",
              " 'إحباط': 1540,\n",
              " 'عند': 110003,\n",
              " 'سمار': 88881,\n",
              " 'يوم': 227840,\n",
              " 'طالعتنه': 99818,\n",
              " 'ملتهين': 165320,\n",
              " 'بيه': 44049,\n",
              " 'ذاك': 79195,\n",
              " 'شيع': 95826,\n",
              " 'رن': 82496,\n",
              " 'تحت': 46642,\n",
              " 'مجهر': 153626,\n",
              " 'بخت': 31830,\n",
              " 'ستار': 86773,\n",
              " 'موج': 168849,\n",
              " 'بحر': 31570,\n",
              " 'عوفي': 110624,\n",
              " 'حمص': 70093,\n",
              " 'سولفينه': 90116,\n",
              " 'اتخوف': 4101,\n",
              " 'رايب': 80500,\n",
              " 'إنجاز': 1729,\n",
              " 'قرار': 119420,\n",
              " 'ايفانكا': 27307,\n",
              " 'تبين': 45921,\n",
              " 'كاسر': 122384,\n",
              " 'خاطر': 72414,\n",
              " 'امسح': 20853,\n",
              " 'علق': 108700,\n",
              " 'خوالك': 74896,\n",
              " 'يموت': 225287,\n",
              " 'آلن': 667,\n",
              " 'شجن': 92155,\n",
              " 'مايقولونه': 148244,\n",
              " 'أحضر': 795,\n",
              " 'زين': 85481,\n",
              " 'نبهتني': 172348,\n",
              " 'لحد': 130948,\n",
              " 'الان': 18970,\n",
              " 'ماعرف': 142118,\n",
              " 'دافع': 75632,\n",
              " 'جواب': 64223,\n",
              " 'طبيعي': 100178,\n",
              " 'مافي': 142844,\n",
              " 'ازعاج': 11115,\n",
              " 'مبدع': 149362,\n",
              " 'محد': 154079,\n",
              " 'داري': 75497,\n",
              " 'بيك': 43417,\n",
              " 'ابد': 2141,\n",
              " 'مامنزله': 144288,\n",
              " 'ايدي': 26823,\n",
              " 'لاشكلي': 129243,\n",
              " 'لاتسوينه': 128287,\n",
              " 'اقتراح': 17550,\n",
              " 'رهيب': 82589,\n",
              " 'كلك': 124987,\n",
              " 'أفكار': 1169,\n",
              " 'مبرور': 149438,\n",
              " 'سعي': 88010,\n",
              " 'مشكور': 159794,\n",
              " 'هو': 188724,\n",
              " 'شخسران': 92315,\n",
              " 'نوري': 179188,\n",
              " 'مالكي': 143893,\n",
              " 'ولد': 199127,\n",
              " 'خايب': 72650,\n",
              " 'يخلي': 216015,\n",
              " 'حطب': 68651,\n",
              " 'فزع': 115429,\n",
              " 'خاف': 72456,\n",
              " 'لس': 131352,\n",
              " 'تسرع': 50768,\n",
              " 'أو': 1400,\n",
              " 'اتخاذ': 3965,\n",
              " 'ماضن': 141720,\n",
              " 'متحتاج': 150501,\n",
              " 'نصيح': 176231,\n",
              " 'مريض': 157179,\n",
              " 'نفس': 177410,\n",
              " 'صعب': 97500,\n",
              " 'حر': 67485,\n",
              " 'ومامقصوده': 200251,\n",
              " 'مقصود': 164057,\n",
              " 'شل': 94068,\n",
              " 'لسان': 131368,\n",
              " 'جهل': 64168,\n",
              " 'اف': 16754,\n",
              " 'تكاليف': 55949,\n",
              " 'حفل': 68950,\n",
              " 'حن': 70262,\n",
              " 'حكوم': 69367,\n",
              " 'علو': 108863,\n",
              " 'مخضر': 155195,\n",
              " 'شاكه': 91277,\n",
              " 'دلاءل': 77446,\n",
              " 'لاتشير': 128333,\n",
              " 'صباح': 96550,\n",
              " 'أسمع': 978,\n",
              " 'نادي': 171421,\n",
              " 'ربك': 80722,\n",
              " 'متاخذغير': 150108,\n",
              " 'قهر': 121300,\n",
              " 'تعب': 53262,\n",
              " 'ضيم': 99532,\n",
              " 'يخلص': 215963,\n",
              " 'تربي': 48922,\n",
              " 'أولاد': 1467,\n",
              " 'مراء': 156200,\n",
              " 'عربي': 106719,\n",
              " 'شلونك': 94228,\n",
              " 'أهل': 1391,\n",
              " 'زن': 84887,\n",
              " 'يستاهل': 218092,\n",
              " 'اعتقد': 15705,\n",
              " 'زوج': 85158,\n",
              " 'جوال': 64290,\n",
              " 'شغل': 93447,\n",
              " 'بير': 41694,\n",
              " 'نسوان': 175520,\n",
              " 'تعمر': 53914,\n",
              " 'بيوت': 44140,\n",
              " 'جاب': 61457,\n",
              " 'نتيج': 173345,\n",
              " 'منطقي': 167296,\n",
              " 'هلو': 187832,\n",
              " 'رغود': 81919,\n",
              " 'منور': 167954,\n",
              " 'استاذي': 11627,\n",
              " 'فاضل': 113703,\n",
              " 'انجمد': 22616,\n",
              " 'باني': 28818,\n",
              " 'شتاء': 91886,\n",
              " 'الب': 18994,\n",
              " 'صيف': 98574,\n",
              " 'مطور': 161275,\n",
              " 'يسولف': 219152,\n",
              " 'رفع': 81997,\n",
              " 'صب': 96536,\n",
              " 'اعتبر': 15567,\n",
              " 'الو': 20084,\n",
              " 'يه': 226877,\n",
              " 'منجز': 166525,\n",
              " 'يرفع': 217357,\n",
              " 'تجاوز': 46088,\n",
              " 'نشاط': 175646,\n",
              " 'رءيس': 79789,\n",
              " 'وزراء': 195924,\n",
              " 'مدن': 155923,\n",
              " 'أصبح': 1022,\n",
              " 'تباري': 45267,\n",
              " 'هندس': 188316,\n",
              " 'جمال': 63517,\n",
              " 'رفاهي': 81949,\n",
              " 'فرحان': 114966,\n",
              " 'طم': 101100,\n",
              " 'يبقي': 211181,\n",
              " 'اقتصاد': 17575,\n",
              " 'متوازن': 152832,\n",
              " 'جهنم': 64184,\n",
              " 'افتهم': 16948,\n",
              " 'عد': 106336,\n",
              " 'شرف': 92773,\n",
              " 'انتماء': 22358,\n",
              " 'عدس': 106391,\n",
              " 'كبسول': 123072,\n",
              " 'ست': 86757,\n",
              " 'ساع': 85873,\n",
              " 'صدق': 97157,\n",
              " 'عزيز': 107195,\n",
              " 'انسوي': 23336,\n",
              " 'ثور': 61339,\n",
              " 'تقبل': 55332,\n",
              " 'أخاف': 816,\n",
              " 'نقود': 178047,\n",
              " 'ايصير': 27156,\n",
              " 'أشكال': 1008,\n",
              " 'شرعي': 92766,\n",
              " 'ثق': 61125,\n",
              " 'وقف': 197796,\n",
              " 'توفيق': 60076,\n",
              " 'جميع': 63792,\n",
              " 'قمر': 121061,\n",
              " 'يكون': 224181,\n",
              " 'احلي': 8025,\n",
              " 'ظرور': 101798,\n",
              " 'ضعيف': 99164,\n",
              " 'عاجز': 102192,\n",
              " 'نزيه': 175012,\n",
              " 'فضل': 115772,\n",
              " 'انسحاب': 23251,\n",
              " 'اعلم': 16210,\n",
              " 'لايوفق': 130368,\n",
              " 'فرض': 115105,\n",
              " 'شيسوي': 95783,\n",
              " 'جان': 61842,\n",
              " 'إنسان': 1735,\n",
              " 'عايش': 105392,\n",
              " 'يمثل': 224896,\n",
              " 'حبوب': 66227,\n",
              " 'لاي': 129843,\n",
              " 'دور': 78180,\n",
              " 'تمون': 57559,\n",
              " 'راقي': 80322,\n",
              " 'يطلع': 220811,\n",
              " 'واحد': 191327,\n",
              " 'ماشي': 141449,\n",
              " 'ماتحب': 136188,\n",
              " 'احترم': 7283,\n",
              " 'بلاص': 36439,\n",
              " 'أخذ': 820,\n",
              " 'سنو': 89531,\n",
              " 'مليون': 165809,\n",
              " 'تغير': 54433,\n",
              " 'سرع': 87517,\n",
              " 'اشوف': 14262,\n",
              " 'جديد': 62526,\n",
              " 'دام': 75771,\n",
              " 'ماكان': 143234,\n",
              " 'خبر': 72713,\n",
              " 'شكر': 93852,\n",
              " 'معلوم': 162426,\n",
              " 'يحق': 215180,\n",
              " 'يزوج': 217803,\n",
              " 'موصح': 169254,\n",
              " 'صدو': 97243,\n",
              " 'دوخ': 78150,\n",
              " 'كيان': 126946,\n",
              " 'ديزيدون': 78804,\n",
              " 'مين': 171005,\n",
              " 'صون': 98467,\n",
              " 'بلاء': 36326,\n",
              " 'خلاص': 74119,\n",
              " 'نستورد': 175333,\n",
              " 'يثور': 214007,\n",
              " 'مامعقوله': 144227,\n",
              " 'ديصير': 78851,\n",
              " 'ساكت': 86000,\n",
              " 'مسوانه': 158989,\n",
              " 'عمل': 109610,\n",
              " 'سحر': 87182,\n",
              " 'مصالح': 160108,\n",
              " 'فقط': 116095,\n",
              " 'صين': 98602,\n",
              " 'محصور': 154297,\n",
              " 'عصاب': 107570,\n",
              " 'مافيا': 142845,\n",
              " 'كلشي': 124955,\n",
              " 'أتوقع': 728,\n",
              " 'سومري': 90135,\n",
              " 'لاتصير': 128380,\n",
              " 'حارو': 65360,\n",
              " 'دقيق': 77332,\n",
              " 'حصر': 68473,\n",
              " 'سلاح': 88478,\n",
              " 'دليل': 77578,\n",
              " 'ماحصل': 139404,\n",
              " 'ناصري': 171691,\n",
              " 'نزاع': 174838,\n",
              " 'عشاءري': 107366,\n",
              " 'بارح': 28214,\n",
              " 'جاك': 61710,\n",
              " 'شأن': 90891,\n",
              " 'مامثل': 144083,\n",
              " 'مثل': 153075,\n",
              " 'لاتعليق': 128500,\n",
              " 'عدم': 106421,\n",
              " 'قال': 118557,\n",
              " 'باطل': 28506,\n",
              " 'أحيان': 813,\n",
              " 'نتمني': 173225,\n",
              " 'زمام': 84744,\n",
              " 'أمر': 1284,\n",
              " 'بيد': 41528,\n",
              " 'تعالى': 53179,\n",
              " 'يطور': 220925,\n",
              " 'خلق': 74326,\n",
              " 'أراد': 871,\n",
              " 'تبهريني': 45841,\n",
              " 'تميز': 57579,\n",
              " 'مساء': 157660,\n",
              " 'ام': 20187,\n",
              " 'انتقل': 22329,\n",
              " 'ارجنتين': 10303,\n",
              " 'قريب': 119804,\n",
              " 'ضل': 99243,\n",
              " 'طيع': 101632,\n",
              " 'عظيم': 107994,\n",
              " 'ضحكتني': 98890,\n",
              " 'ياربي': 205767,\n",
              " 'عف': 108021,\n",
              " 'عجب': 106108,\n",
              " 'زون': 85295,\n",
              " 'جماعتنه': 63490,\n",
              " 'خراب': 73191,\n",
              " 'اظاهر': 15384,\n",
              " 'استنكر': 12514,\n",
              " 'هيج': 189430,\n",
              " 'اندفاع': 22932,\n",
              " 'قوه': 121708,\n",
              " 'طالب': 99781,\n",
              " 'حقوق': 69131,\n",
              " 'حرامي': 67545,\n",
              " 'انتظر': 22229,\n",
              " 'ردود': 81428,\n",
              " 'تجيك': 46461,\n",
              " 'يمعود': 225159,\n",
              " 'خط': 73840,\n",
              " 'أحمر': 805,\n",
              " 'زواج': 85090,\n",
              " 'أسباب': 940,\n",
              " 'تفسير': 54876,\n",
              " 'تكون': 56382,\n",
              " 'مشاعر': 159191,\n",
              " 'سمي': 89191,\n",
              " 'ياشريف': 206416,\n",
              " 'تقصير': 55605,\n",
              " 'ايم': 27553,\n",
              " 'مطلوب': 161198,\n",
              " 'ظروري': 101799,\n",
              " 'يش': 219233,\n",
              " 'شو': 94991,\n",
              " 'وصلتني': 196650,\n",
              " 'أجاب': 745,\n",
              " 'عذر': 106549,\n",
              " 'احراج': 7540,\n",
              " 'دكتور': 77383,\n",
              " 'سليم': 88847,\n",
              " 'ومايجاملني': 200451,\n",
              " 'اطلع': 15238,\n",
              " 'اوال': 25287,\n",
              " 'عنصري': 110157,\n",
              " 'وافي': 192110,\n",
              " 'صريح': 97467,\n",
              " 'راسي': 80170,\n",
              " 'دتخيل': 76139,\n",
              " 'شط': 93048,\n",
              " 'أكيد': 1231,\n",
              " 'الك': 19831,\n",
              " 'ولايتي': 198948,\n",
              " 'أكرم': 1222,\n",
              " 'قت': 119012,\n",
              " 'عشر': 107444,\n",
              " 'نظل': 176654,\n",
              " 'اجداد': 6699,\n",
              " 'كب': 122964,\n",
              " 'قنبل': 121208,\n",
              " 'احرض': 7568,\n",
              " 'يحضروك': 215063,\n",
              " 'خليك': 74496,\n",
              " 'يجون': 214418,\n",
              " 'بابل': 27948,\n",
              " 'قوي': 121721,\n",
              " 'رأي': 79829,\n",
              " 'ماعد': 142083,\n",
              " 'علم': 108746,\n",
              " 'مليش': 165769,\n",
              " 'زرا': 84048,\n",
              " 'ياعراق': 206889,\n",
              " 'مامتابعتك': 144058,\n",
              " 'نور': 179119,\n",
              " 'انوب': 24609,\n",
              " 'اخطب': 8889,\n",
              " 'يبعد': 211102,\n",
              " 'انطيك': 23752,\n",
              " 'شمر': 94342,\n",
              " 'مجتمع': 153346,\n",
              " 'افندي': 17343,\n",
              " 'دشداشه': 76881,\n",
              " 'اجب': 6603,\n",
              " 'لابس': 127736,\n",
              " 'طار': 99646,\n",
              " 'ألبس': 1238,\n",
              " 'حري': 67795,\n",
              " 'انتقص': 22327,\n",
              " 'لبس': 130483,\n",
              " 'فوق': 117285,\n",
              " 'مستوى': 158507,\n",
              " 'ادراك': 9314,\n",
              " 'تفسر': 54863,\n",
              " 'لي': 133662,\n",
              " 'نكه': 178327,\n",
              " 'مدارس': 155485,\n",
              " 'طيني': 101653,\n",
              " 'نماذج': 178513,\n",
              " 'يصيح': 220297,\n",
              " 'مكان': 164380,\n",
              " 'يبو': 211320,\n",
              " 'اختراع': 8426,\n",
              " 'تطوير': 52970,\n",
              " 'اسف': 12935,\n",
              " 'حساف': 68051,\n",
              " 'تفتي': 54667,\n",
              " 'نت': 172429,\n",
              " 'اشوفك': 14265,\n",
              " 'ماشفت': 141335,\n",
              " 'ناسوصافن': 171604,\n",
              " 'افكر': 17277,\n",
              " 'ذهن': 79631,\n",
              " 'شارد': 91026,\n",
              " 'تعيش': 54110,\n",
              " 'هادي': 180100,\n",
              " 'تشلع': 51751,\n",
              " 'لبي': 130631,\n",
              " 'بصم': 34415,\n",
              " 'جاحد': 61561,\n",
              " 'سلملي': 88750,\n",
              " 'ماعدنه': 142099,\n",
              " 'نازل': 171508,\n",
              " 'صدر': 97117,\n",
              " 'يحفظ': 215150,\n",
              " 'مر': 156191,\n",
              " 'احسك': 7692,\n",
              " 'دوم': 78429,\n",
              " 'جمع': 63610,\n",
              " 'يديم': 216729,\n",
              " 'خو': 74840,\n",
              " 'مات': 135847,\n",
              " 'انقتل': 24138,\n",
              " 'قتل': 119031,\n",
              " 'دخل': 76288,\n",
              " 'متابع': 150038,\n",
              " 'أسلوب': 973,\n",
              " 'داخل': 75389,\n",
              " 'تويتر': 60349,\n",
              " 'شسوي': 93023,\n",
              " 'تكره': 56123,\n",
              " 'جدي': 62522,\n",
              " 'شعلينه': 93355,\n",
              " 'منزعج': 166878,\n",
              " 'يسلم': 218969,\n",
              " 'رأس': 79828,\n",
              " 'مرفوع': 156882,\n",
              " 'يكرهوني': 223931,\n",
              " 'التمس': 19129,\n",
              " 'يشهد': 219763,\n",
              " 'هي': 189065,\n",
              " 'متوفي': 152933,\n",
              " 'اثنين': 6485,\n",
              " 'وارد': 191638,\n",
              " 'ماتعودت': 137706,\n",
              " 'حولي': 70991,\n",
              " 'يستعملوها': 218351,\n",
              " 'فتوى': 114503,\n",
              " 'ينهنم': 226782,\n",
              " 'الاسلاميه': 18922,\n",
              " 'سد': 87310,\n",
              " 'بظاعتكم': 34849,\n",
              " 'جوابي': 64228,\n",
              " 'ماشفته': 141348,\n",
              " 'غلط': 112508,\n",
              " 'شفت': 93552,\n",
              " 'ينتحر': 225553,\n",
              " 'خطي': 73988,\n",
              " 'اكتفى': 18349,\n",
              " 'تعليق': 53874,\n",
              " 'طبع': 100062,\n",
              " 'زلمنه': 84672,\n",
              " 'باير': 28912,\n",
              " 'شايف': 91532,\n",
              " 'اشو': 14234,\n",
              " 'خفت': 74039,\n",
              " 'لاوتريد': 129814,\n",
              " 'اكفل': 18663,\n",
              " 'انسحب': 23260,\n",
              " 'مزح': 157329,\n",
              " 'زحم': 83978,\n",
              " 'تراث': 48724,\n",
              " 'عالمي': 104880,\n",
              " 'عادس': 102245,\n",
              " 'عبد': 105534,\n",
              " 'مهدي': 168256,\n",
              " 'ماعتقد': 142044,\n",
              " 'أتمنى': 727,\n",
              " 'لاك': 129489,\n",
              " 'متطفل': 151599,\n",
              " 'متعدي': 151739,\n",
              " 'حدود': 67365,\n",
              " 'حل': 69480,\n",
              " 'ناف': 171785,\n",
              " 'زمان': 84747,\n",
              " 'حضار': 68536,\n",
              " 'زمن': 84844,\n",
              " 'اغبر': 16471,\n",
              " 'قابل': 118222,\n",
              " 'تصنع': 52350,\n",
              " 'ود': 195400,\n",
              " 'مايخبءه': 146787,\n",
              " 'قلب': 120725,\n",
              " 'تفضح': 54929,\n",
              " 'يقال': 223018,\n",
              " 'نسيب': 175562,\n",
              " 'قطع': 120439,\n",
              " 'كبد': 123024,\n",
              " 'يشبه': 219305,\n",
              " 'لهب': 133027,\n",
              " 'ضحك': 98872,\n",
              " 'مظاهر': 161340,\n",
              " 'ضد': 98952,\n",
              " 'غش': 112266,\n",
              " 'تظربون': 53027,\n",
              " 'معترض': 161832,\n",
              " 'شايل': 91611,\n",
              " 'فكر': 116159,\n",
              " 'كت': 123156,\n",
              " 'الون': 20114,\n",
              " 'لايوفقك': 130370,\n",
              " 'خليتونه': 74437,\n",
              " 'مضحك': 160778,\n",
              " 'يسو': 219106,\n",
              " 'ميسو': 170472,\n",
              " 'كفو': 124651,\n",
              " 'لايجيب': 129930,\n",
              " 'زعل': 84256,\n",
              " 'برود': 33164,\n",
              " 'هاو': 184426,\n",
              " 'تكتل': 56031,\n",
              " 'سباح': 86452,\n",
              " 'ترك': 49358,\n",
              " 'صرع': 97385,\n",
              " 'نحل': 173935,\n",
              " 'جرع': 62798,\n",
              " 'مخدر': 155067,\n",
              " 'تعرف': 53545,\n",
              " 'أغلب': 1135,\n",
              " 'أمور': 1306,\n",
              " 'تطرح': 52748,\n",
              " 'مسلي': 158871,\n",
              " 'مجامل': 153259,\n",
              " 'اما': 20190,\n",
              " 'آخر': 658,\n",
              " 'دقاءق': 77266,\n",
              " 'غزل': 112200,\n",
              " 'قيس': 121808,\n",
              " 'ملوح': 165693,\n",
              " 'شرك': 92813,\n",
              " 'سعد': 87773,\n",
              " 'ماصخ': 141535,\n",
              " 'درج': 76537,\n",
              " 'تقيء': 55884,\n",
              " 'تنتقد': 57923,\n",
              " 'يجاوبك': 214077,\n",
              " 'مثقف': 153065,\n",
              " 'هدوء': 186440,\n",
              " 'توازن': 59547,\n",
              " 'هبل': 184903,\n",
              " 'حياك': 71121,\n",
              " 'ألقاب': 1250,\n",
              " 'كون': 126714,\n",
              " 'يسم': 219002,\n",
              " 'تلغي': 56769,\n",
              " 'أمريكا': 1287,\n",
              " 'مقابل': 163620,\n",
              " 'يقود': 223552,\n",
              " 'شف': 93508,\n",
              " 'ميستحق': 170435,\n",
              " 'نقاش': 177737,\n",
              " 'أصل': 1032,\n",
              " 'وفاء': 197398,\n",
              " 'إخلاص': 1559,\n",
              " 'خربان': 73258,\n",
              " 'دمسح': 77686,\n",
              " 'شارب': 91012,\n",
              " 'نساء': 175017,\n",
              " 'معجب': 161892,\n",
              " 'يغار': 222026,\n",
              " 'بحكومتنه': 31718,\n",
              " 'رومانسي': 82978,\n",
              " 'أحسن': 793,\n",
              " 'ظيم': 102024,\n",
              " 'انسجام': 23242,\n",
              " 'غراوي': 111927,\n",
              " 'يشب': 219277,\n",
              " 'فارض': 113514,\n",
              " 'إصرار': 1632,\n",
              " 'معرف': 162032,\n",
              " 'لايقدم': 130247,\n",
              " 'ولاياخر': 198923,\n",
              " 'نسب': 175107,\n",
              " 'أخت': 819,\n",
              " 'مءشر': 134295,\n",
              " 'فليش': 116558,\n",
              " 'ماحبها': 139221,\n",
              " 'ياخير': 205446,\n",
              " 'تجرب': 46166,\n",
              " 'يجوز': 214391,\n",
              " 'نتاهل': 172485,\n",
              " 'تصفي': 52281,\n",
              " 'يد': 216163,\n",
              " 'شيوخ': 96034,\n",
              " 'يرشق': 217274,\n",
              " 'ضمير': 99333,\n",
              " 'ضحال': 98858,\n",
              " 'قليل': 121017,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "cv.vocabulary_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kpSm8Ry3UF3e"
      },
      "outputs": [],
      "source": [
        "x_train_cv = cv.transform(x_train)\n",
        "x_test_cv = cv.transform(x_test) "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Models training"
      ],
      "metadata": {
        "id": "BKVVGhmfUF3e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf_MNB = MultinomialNB()\n",
        "clf_LR = LogisticRegression()\n",
        "clf_SVM=SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3, random_state=42,\n",
        "                      # max_iter=100,\n",
        "                      tol=None)\n",
        "clf_list = [ clf_MNB, clf_LR, clf_SVM]\n",
        "\n",
        "# Compare evaluation of the different classifiers\n",
        "clf_evaluation = {}\n",
        "clf_report = {}\n",
        "\n",
        "for clf in clf_list:\n",
        "    clf_evaluation[clf.__class__.__name__], clf_report[clf.__class__.__name__] = train_predict(clf,\n",
        "                                                                                              x_train_cv,\n",
        "                                                                                              np.argmax(y_train.to_numpy(), axis=1),\n",
        "                                                                                              x_test_cv,\n",
        "                                                                                              np.argmax(y_test.to_numpy(), axis=1)\n",
        "                                                                                              )\n",
        "    print(clf_evaluation )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ac85c44-708c-4170-8960-6db66dace66c",
        "id": "NihT3GR7UF3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'MultinomialNB': {'train_time': 0.51, 'pred_time': 0.33, 'acc_train': 0.56, 'acc_test': 0.39, 'f1_train': 0.49, 'f1_test': 0.37}}\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'MultinomialNB': {'train_time': 0.51, 'pred_time': 0.33, 'acc_train': 0.56, 'acc_test': 0.39, 'f1_train': 0.49, 'f1_test': 0.37}, 'LogisticRegression': {'train_time': 194.04, 'pred_time': 0.35, 'acc_train': 0.53, 'acc_test': 0.45, 'f1_train': 0.49, 'f1_test': 0.45}}\n",
            "{'MultinomialNB': {'train_time': 0.51, 'pred_time': 0.33, 'acc_train': 0.56, 'acc_test': 0.39, 'f1_train': 0.49, 'f1_test': 0.37}, 'LogisticRegression': {'train_time': 194.04, 'pred_time': 0.35, 'acc_train': 0.53, 'acc_test': 0.45, 'f1_train': 0.49, 'f1_test': 0.45}, 'SGDClassifier': {'train_time': 2981.22, 'pred_time': 0.37, 'acc_train': 0.56, 'acc_test': 0.43, 'f1_train': 0.54, 'f1_test': 0.41}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_evaluation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68d0c349-1ab8-4d32-a8aa-326fb2b4466a",
        "id": "553p_lPuUF3g"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'LogisticRegression': {'acc_test': 0.45,\n",
              "  'acc_train': 0.53,\n",
              "  'f1_test': 0.45,\n",
              "  'f1_train': 0.49,\n",
              "  'pred_time': 0.35,\n",
              "  'train_time': 194.04},\n",
              " 'MultinomialNB': {'acc_test': 0.39,\n",
              "  'acc_train': 0.56,\n",
              "  'f1_test': 0.37,\n",
              "  'f1_train': 0.49,\n",
              "  'pred_time': 0.33,\n",
              "  'train_time': 0.51},\n",
              " 'SGDClassifier': {'acc_test': 0.43,\n",
              "  'acc_train': 0.56,\n",
              "  'f1_test': 0.41,\n",
              "  'f1_train': 0.54,\n",
              "  'pred_time': 0.37,\n",
              "  'train_time': 2981.22}}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "legend = []\n",
        "for i in range(len(clf_list)):\n",
        "    legend.append(clf_list[i].__class__.__name__)\n",
        "def eval_plot(labels, y_label, title):\n",
        "\n",
        "    clf1 = [clf_evaluation[legend[0]][labels[0]], clf_evaluation[legend[0]][labels[1]]]\n",
        "    clf2 = [clf_evaluation[legend[1]][labels[0]], clf_evaluation[legend[1]][labels[1]]]\n",
        "    clf3 = [clf_evaluation[legend[2]][labels[0]], clf_evaluation[legend[2]][labels[1]]]\n",
        "\n",
        "    x = np.arange(len(labels))  # the label locations\n",
        "    width = 0.2  # the width of the bars\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.bar(x - width/2, clf1, width, label=legend[0])\n",
        "    ax.bar(x + width/2, clf2, width, label=legend[1])\n",
        "    ax.bar(x + width*3/2, clf3, width, label=legend[2])\n",
        "\n",
        "    # Add labels, title, and legend\n",
        "    ax.set_ylabel(y_label)\n",
        "    ax.set_title(title)\n",
        "    ax.set_xticks(x)\n",
        "    ax.set_xticklabels(labels)\n",
        "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "\n",
        "    # Add bars annotation\n",
        "    for i in ax.patches:\n",
        "        # get_x pulls left or right; get_height pushes up or down\n",
        "        ax.text(i.get_x()+(width/2), i.get_height()+0.01,\n",
        "                str(i.get_height()), fontsize=11,\n",
        "                color='black', ha='center')\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "THZFZE_mUF3g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['train_time', 'pred_time']\n",
        "y_label = 'Time'\n",
        "title = 'Train/Test Time per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "753c65d8-e711-4c53-bd6c-652574c3a8d2",
        "id": "oFHQjCJGUF3g"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhEAAAEJCAYAAAAq83V5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd3wVVf7/8dcnCdIJvQVCWEgIoYgQUSxfGxZQsaGIBeyVr4XFr6zuin1dV9efLCsWiqhg2XVtiGJDsK4ERIEkQJTee0AwkuTz++NOspeQhOSSEKLv5+NxH5k5c+bMZy433E/OnJlj7o6IiIhIeUVVdQAiIiJSPSmJEBERkYgoiRAREZGIKIkQERGRiCiJEBERkYgoiRAREZGIKImQSmdm75nZ0KqO40CZ2U4z+11Vx1HZzOwKM/u8Etvf6/NgZg+a2SYzW2dm8cH7HF1ZxxeRihNT1QHIocnMdoat1gFygLxg/Xp3n1zWtty9XzmPfRiwBtgOtAiKawN7gNxg/WF3f7ic7T4PrHL3PxazLR5IDyuqC+wCCh6k0s/d65XneIcyMzsduBs4AviZ0Lk/7u5vV/axwz8Pwfv+e6Cdu28Iin8177PIr52SCClW+BemmS0DrnH3j4rWM7MYd88tWn6A/geY5+59w47zKfCSu4+r4GMB4O4rCPvyMjMHDnf3rMo43sFiZtHunlekbCAwARgOnA3sAI4HLgMqPYkoIh7YHJZARKySPosiUgpdzpByMbMTzWyVmd1pZuuAiWbWyMymmtlGM9saLLcJ2+dTM7smWL7CzD43s8eCukvNrGhPRX9g2n7iuMrMMoI2pptZu6DczOwJM9tgZtlmNt/MuprZdcClwP8F3eXvRHDubmYdg+XnzeypoGt+p5l9YWYtzez/BTFlmtkRYfu2NrPXg/doqZndUspxnjezp83sQzPbYWYzC84v2J4cbNtiZovM7KIi+441s2lm9hNwUpG2Dfgb8IC7j3P37e6e7+4z3f3aEuJ50sxWBu/nHDM7PmxbbzNLC7atN7O/BeW1zOwlM9tsZtvMbLaZtQi2fWpm15hZX+BDoHXwHj5vZgnB+xwT1I01s/FmttbMVgeXPqKDbVcE7/sTZrYZuLeM/5QiUkGUREgkWgKNgXbAdYQ+RxOD9XhgNzCmlP2PAhYBTYFHgfHBl1uB/sC7Je1sZucAdwHnA82Az4CXg82nEerJSAJigYsI/aX7LDAZeNTd67n72eU435JcBPwxOI8c4CtgbrD+L0Jf1phZFPAO8B0QB5wC3BZcUijJpcADQVvzgtgxs7qEvninAM2Bi4GnzCwlbN9LgIeA+kDRsQ2dgLZBfGU1G+hB6N98CvBPM6sVbHsSeNLdGwAdgNeC8qGE3v+2QBPgBkKfi0JBz1Y/YE3wb3JFMcd+ntAlrI6ELr2cBlwTtv0o4EdCl70eKsc5iUgFUBIhkcgHRrl7jrvvdvfN7v66u+9y9x2E/jM/oZT9l7v7c0E3+ySgFcHYBzPrAMS4+6JS9r8B+LO7ZwTd1w8DPYK/1vcQ+vJMBiyos/ZAT7gEb7j7HHf/GXgD+NndXwjO61VCX3oARwLN3P1+d//F3X8EniOUAJTkXXef5e45hMYu9DGztsBZwDJ3n+juue7+LfA6cGHYvm+5+xdBD8PPRdptEvws83vi7i8F/8a57v44UJNQMgKh97ujmTV1953u/nVYeROgo7vnBe9TdlmPCRD0XPQHbnP3n4JLHk+w9/u2xt3/HsS2u9iGRKTSKImQSGwM/3Iyszpm9oyZLTezbGAW0NBKHmG/rmDB3XcFiwXjEfoD7+3n+O2AJ4Nu8m3AFsCAOHf/hFAvyD+ADWb2rJk1KO8JltH6sOXdxawXnFM7Ql3228Jivov/DhotzsqCBXffSegcWwdtHVWkrUsJ9Q7ts28xNgc/W5VSZy9mNiK4dLQ9OF4soR4SgKsJ9fpkBpcszgrKXwSmA6+Y2Roze9TMapT1mIF2QA1gbdi5PkOoB6ZAaecqIpVMSYREoujUr78n9JfpUUG39v8E5Ub57Xc8BKEvjuvdvWHYq7a7fwng7qPdvReQQugL7o4S4j5YVgJLi8Rb3937l7JP24IFM6tH6FLCmqCtmUXaqufuN4btW9p5LgrauKAsgQfjH/6P0KWbRu7ekNBdMwbg7kvcfTChL/a/AP8ys7ruvsfd73P3FOAYQj0oQ8pyzDArCV0mahp2rg3cvUtYHU1DLFKFlERIRahP6C/vbWbWGBgVSSNmVgfoDczYT9WngT+YWZdgv1gzuzBYPtLMjgr+6v2J0O2L+cF+64GqeM7DN8AOCw1GrW1m0cFgzyNL2ae/mR1nodtdHwC+dveVwFQgycwuN7MawetIM+tclkDc3QndlfEnM7vSzBqYWVRwrGeL2aU+oTEJG4EYM7sHKOzZMbPLzKyZu+cD24LifDM7ycy6Bb1R2YQub+RTDsFlqA+Ax8Pi7GBmpV0qE5GDSEmEVIT/R+g5DpuAr4H3I2znZOCrYq7j78Xd3yD0V+8rweWTBYQG6EHoC+45YCuwnFD3/V+DbeOBlKBr/M0IYyy3YIzEWYQGJy4l9D6NI3RZoCRTCCVjW4BehG6/JBhzchqhcQFrCF0a+guhcQpljedfwCDgqqCN9cCDwFvFVJ9O6N9zMaH382f2voRwBrDQQs8VeRK4OBib0JLQ4M1sIAOYSegSR3kNAQ4j9ByLrUGbZb4UIyKVy0J/mIhUPTN7Cljg7k9VdSxVyUp5KJaIyKFED5uSQ8k8QrdCiohINaAkQg4ZwbMcRESkmtDlDBEREYmIBlaKiIhIRH6VlzOaNm3qCQkJVR2GiEi1MmfOnE3u3qyq45Dq41eZRCQkJJCWllbVYYiIVCtmtryqY5DqRZczREREJCJKIkRERCQiSiIkIps3b6Z///506tSJbt26cf7557Nx40YAJk6cSPfu3enSpQtnn302W7ZsKdxvwoQJdOvWjR49epCamspnn31WuO3SSy+ldevWmBk7d+4s8dg333wzycnJHH744Rx77LGFl67y8/O54IIL6NSpE4cffjinnnoqP/zwQyW9AyIigrv/6l69evVyqVybN2/2GTNmFK6PGDHCr7rqKk9PT/fWrVv7hg0b3N39gQce8Ouvv97d3Tdt2uT169f3devWubv7W2+95Z07dy5s4+OPP/b169c74Dt27Cjx2O+8847/8ssvhcu/+93v3N09Ly/P33rrLc/Ly3N397///e9+8sknV9xJi/zKAWl+CPwfrlf1eVVaT4SZ1TKzb8zsOzNbaGb3BeXtzew/ZpZlZq8GEwxhZjWD9axge0JYW38IyheZ2emVFbOUXePGjTnxxBML148++miWL1/OggUL6NGjB82ahQZ49+/fn8mTJwP/TVh37NgBwLZt22jTpk1hGyeffDLNm4fP8ly8s846ixo1QrNK9+nTh1WrVpGfn09UVBQDBgwgKiqqcNvy5RonJiJSWSrz7owc4GR33xnMqPi5mb1HaAbBJ9z9FTN7GrgaGBv83OruHc3sYkKTCg0ysxRCkw11AVoDH5lZkocmNZJDQH5+PmPHjmXAgAEcfvjhzJ49m6VLl5KQkMCUKVPYuXMnW7ZsoWnTpjzzzDP07NmThg0bkp+fz6effnpAxx4zZgxnnnlmYeJQdNuAAQMOqH0RESlZpSUR7u5AwYXtGsHLCc3UeElQPgm4l1AScU6wDKGZ+saYmQXlr7h7DrDUzLIITRf9VWXFLuXzv//7v9SrV49hw4YRFRXF6NGjGTRoEGbGOeecA0BMTAzZ2dmMGTOG2bNn06lTJ1577TXOO+88vv/+e0L/1OXzyiuvMGXKFGbNmrXPtkcffZSMjAw++eSTAz4/EREpXqUOrDSzaDObB2wAPgR+ALa5e25QZRUQFyzHEUwxHGzfDjQJLy9mH6liI0aMYMmSJbz66quFvQEXX3wx33zzDf/5z3/o27cvcXFxNGjQgA8++ICGDRvSqVMnAC666CJ++OEHNm3aVO7jvvHGG9x9991Mnz6dFi1a7LXt73//O1OmTGHatGnUqVPnwE9SRESKValJhLvnuXsPoA2h3oPkyjqWmV1nZmlmllZwl4BUrrvuuos5c+bw5ptvUrNmzcLydevWAfDzzz8zatQoRowYAUD79u2ZO3cuGzZsAGDGjBk0aNCApk2bluu4U6dOZfjw4UyfPp2iTyZ95plnePbZZ/nwww9p3LjxAZydiIjsz0F5YqW7bzOzGUAfoKGZxQS9DW2A1UG11UBbYJWZxQCxwOaw8gLh+4Qf41ngWYDU1FTNKlbJFi5cyJ///GeSkpJoktwEgBrNatDulnYse3wZezbvwXOd2KNiWRm7kvGTxgNgJxnxPeKxGMNijJZXt6T7C90BWPH3Fez6cRcAjdo2olabWiSMSAAg609ZtBvejhqNapDxvxlYtNHt5G6F8STcmYBFGxk3ZVCjSQ3ie8XTqXEnatasyX/+85+D+M6IiPx2VNosnmbWDNgTJBC1gQ8IDZYcCrweNrDye3d/ysxuBrq5+w3BwMrz3f0iM+sCTCHUk9Ea+BhILG1gZWpqquux1wdPt0nd9l+pCswfOr+qQxCpVsxsjrunVnUcUn1UZk9EK2CSmUUTumzymrtPNbN04BUzexD4Fhgf1B8PvBgMnNxC6I4M3H2hmb0GpAO5wM26M0NERKTqVebdGd8DRxRT/iOhXoWi5T8DF5bQ1kPAQxUdo4iIiEROj70WERGRiCiJEBERkYgoiRAREZGIKIkQERGRiCiJEBERkYgoiRAREZGIKIkQERGRiCiJEBERkYgoiRAREZGIKIkQERGRiCiJEBERkYgoiRAREZGIKIkQERGRiCiJEBERkYgoiRAREZGIKIkQERGRiCiJEBERkYgoiRAREZGIKIkQERGRiCiJEBERkYgoiRAREZGIKIkQERGRiCiJEBERkYgoiRAREZGIKIkQERGRiCiJEBERkYhUWhJhZm3NbIaZpZvZQjO7NSi/18xWm9m84NU/bJ8/mFmWmS0ys9PDys8IyrLMbGRlxSwiIiJlF1OJbecCv3f3uWZWH5hjZh8G255w98fCK5tZCnAx0AVoDXxkZknB5n8ApwKrgNlm9ra7p1di7CIiIrIflZZEuPtaYG2wvMPMMoC4UnY5B3jF3XOApWaWBfQOtmW5+48AZvZKUFdJhIiISBU6KGMizCwBOAL4T1A0zMy+N7MJZtYoKIsDVobttiooK6m86DGuM7M0M0vbuHFjBZ+BiIiIFFXpSYSZ1QNeB25z92xgLNAB6EGop+LxijiOuz/r7qnuntqsWbOKaFJERERKUZljIjCzGoQSiMnu/m8Ad18ftv05YGqwuhpoG7Z7m6CMUspFRESkilTm3RkGjAcy3P1vYeWtwqqdBywIlt8GLjazmmbWHkgEvgFmA4lm1t7MDiM0+PLtyopbREREyqYyeyKOBS4H5pvZvKDsLmCwmfUAHFgGXA/g7gvN7DVCAyZzgZvdPQ/AzIYB04FoYIK7L6zEuEVERKQMKvPujM8BK2bTtFL2eQh4qJjyaaXtJyIiIgefnlgpIiIiEVESISIiIhFREiEiIiIRURIhIiIiEVESISIiIhFREiEiIiIRURIhIiIiEVESISIiIhFREiEiIiIRURIhIiIiEVESISIiIhFREiEiIiIRURIhIiIiEVESISIiIhFREiEiIiIRURIhIiIiEVESISIiIhFREiEiIiIRURIhIiIiEVESISIiIhFREiEiIiIRURIhIiIiEVESISIiIhFREiEiIiIRURIhIiIiEVESISIiIhGptCTCzNqa2QwzSzezhWZ2a1De2Mw+NLMlwc9GQbmZ2WgzyzKz782sZ1hbQ4P6S8xsaGXFLCIiImVXmT0RucDv3T0FOBq42cxSgJHAx+6eCHwcrAP0AxKD13XAWAglHcAo4CigNzCqIPEQERGRqlNpSYS7r3X3ucHyDiADiAPOASYF1SYB5wbL5wAveMjXQEMzawWcDnzo7lvcfSvwIXBGZcUtIiIiZXNQxkSYWQJwBPAfoIW7rw02rQNaBMtxwMqw3VYFZSWVFz3GdWaWZmZpGzdurND4RUREZF+VnkSYWT3gdeA2d88O3+buDnhFHMfdn3X3VHdPbdasWUU0KSIiIqWo1CTCzGoQSiAmu/u/g+L1wWUKgp8bgvLVQNuw3dsEZSWVi4iISBWqzLszDBgPZLj738I2vQ0U3GExFHgrrHxIcJfG0cD24LLHdOA0M2sUDKg8LSgTERGRKhRTiW0fC1wOzDezeUHZXcAjwGtmdjWwHLgo2DYN6A9kAbuAKwHcfYuZPQDMDurd7+5bKjFuERERKYNKSyLc/XPASth8SjH1Hbi5hLYmABMqLjoRERE5UJXZEyEiItXcnDlzmsfExIwDuqKnHP/W5AMLcnNzr+nVq9eG4iooiRARkRLFxMSMa9myZedmzZptjYqKqpC76aR6yM/Pt40bN6asW7duHDCguDrKKkVEpDRdmzVrlq0E4rcnKirKmzVrtp1QL1TxdQ5iPCIiUv1EKYH47Qr+7UvMFZREiIiISEQ0JkJERMosYeS7vSqyvWWPnDlnf3XMrNeAAQO2vPXWW0sB9uzZQ/PmzQ/v0aPHTzNmzMgqbd86deocsWvXrm8XLVp02IwZM+rdcMMNWwBmzZpVZ8KECU2ef/75laXtfyAmT54cu3DhwtoPP/zwupLqjB49uklaWlrdF154YcXw4cNbjx07tkVWVtb8uLi43PD4AaKjo3slJibudneio6P9ySefXHHqqaf+VFnxl4V6IkRE5JBWu3bt/EWLFtXeuXOnAbzxxhsNWrRosac8bSxZsqTmq6++2rhg/X/+5392VWYCAXDppZduLy2BKE7Dhg1zH3zwwRbFbatZs2Z+ZmZm+qJFi9IfeOCB1XfddVebiok0ckoiRETkkNe3b9/t//znPxsCvPzyy40vuOCCwocODh8+vPU999xT+MWbmJjYZdGiRYeF73/33XfHpaWl1UtOTk657777mk+dOrX+SSed1LFg/wsvvDChd+/endq0adPtwQcfbF6w37333tsiMTGxS2JiYpf777+/OcCiRYsOa9++fZcLLrggISEhoeuAAQPav/nmm/V79uyZ3K5du64zZsyoA6FehiFDhsQDTJkyJbZ79+7JnTt3TjnmmGOSVq5cWeyVgMGDB29+++23G69fvz66tPdj+/bt0bGxsbnlfR8rmpIIERE55F1++eVbXn311Ua7du2yjIyMOn369ClXN/5DDz20OjU1dWdmZmb6qFGj9nnmQVZWVq2ZM2cunj17dsZjjz3WOicnxz777LM6U6ZMaTJnzpyMtLS0jBdeeKHZF198URtg5cqVte688871P/zww4Iffvih1uTJk5ukpaVlPvTQQ6seeuihVkXbP/XUU3fOmzcvMyMjI33gwIFb7r///pbFxVmvXr28wYMHb3rkkUf26Y3IycmJSk5OTmnfvn2XW2+9td2oUaPWFtfGwaQkQkREDnlHHXXU7lWrVtV87rnnGvft23d7Rbd/2mmnbatdu7a3atUqt3HjxntWrVoV8+mnn9br37//tgYNGuTHxsbmn3nmmVtnzJhRHyAuLi6nd+/eu6Ojo0lKStp98sknZ0dFRdGzZ89dq1atqlm0/aVLlx52/PHHJyYlJaWMHj26ZWZmZu2SYhk5cuSG1157rcnWrVv3+o4uuJyxdOnShW+88caSK6+8sn1+fn5FvxXloiRCRESqhTPOOGPbqFGj2g4ZMmSv+ZNiYmI8/Ms0JyenpCkXSlSzZs3C21ijo6PJzc0ttY3DDjussH5UVBS1atXygn3z8vL22XfYsGHxN91004bFixenjxkzZnlOTk6J379NmzbNO++887b89a9/bV5Snb59+/60devWmLVr11bpDRJKIkREpFq48cYbN40YMWJN7969d4eXJyQk5MybN68uwOeff15n9erV+/QExMbG5u3cubPUcQZFnXTSSTunTZvWcMeOHVHZ2dlR06ZNa3TSSSftiCT2HTt2RMfHx+8BeP7555vsr/7dd9+9ftKkSc2KS0gAvv3221r5+fm0aNGiSsdF6BZPEREps7LckllZOnTosOePf/zjPuMZhgwZsnXy5MlNOnbs2OWII474qV27dj8XrRNcevBOnTqlXHLJJZt69eq1u2idoo477rhdl1xyyeaePXt2Brj88ss3HnvssbuLDtosi7vvvnvN4MGDO8TGxuYed9xxO1asWLFPohOuVatWuf369ds6fvz4wrERBWMiANydsWPHLouJqdqvcQtNnvnrkpqa6mlpaVUdxm9Gt0ndqjqEYs0fOr+qQxCpVsxsjrunhpd99913yw4//PBNVRWTVL3vvvuu6eGHH55Q3Lb9Xs4wsxZmNt7M3gvWU8zs6gqOUURERKqZsoyJeB6YDrQO1hcDt1VWQCIiIlI9lCWJaOrurxGaVxx3zwXyKjUqEREROeSVJYn4ycyaAA5gZkcDFX6ProiIiFQvZRnWORx4G+hgZl8AzYCBlRqViIiIHPL2m0S4+1wzOwHoBBiwyN3LNfGJiIiI/PrsN4kws2igP5AQ1D/NzHD3v1VybCIicqi5N7ZCpwLn3u37fe5E+HTYkdrf1N+RTBUeFxfXrW7dunkQepjV5MmTlyYlJf1yIHFWlEcffbRZnTp18ocNG7a5Mo9TljER7wBXAE2A+mEvERGRamF/U39HOlX4zJkzFy9evDj9uOOO23HPPffsM/FWeeXn55OXd+D3Lvzf//3fxspOIKBsSUQbdz/f3Ue5+30Fr8oOTEREpCRffvll7cMPPzw5KSkp5dRTT+2wcePGaICZM2fWSUpKSklOTk65/vrr2yQmJnYBCJ/6+913362XnJyckpycnNK5c+eUrVu3RpU2Vfj27dujBg4cmJCUlJSSlJSU8vzzzzcsGs+xxx67c+3atTUA1qxZE3P66ad36Nq1a+euXbt2/uCDD+oWlB9zzDGJHTt27DJo0KB2rVu37rZ27dqYRYsWHZaQkND1vPPOS0hKSuryww8/HPanP/2pRdeuXTsnJSWl3H777a0BsrOzo0488cSOnTp1SklMTOzy3HPPNQK46aab4jp06NAlKSkp5brrrmsDe0+PXtJ71bt370433nhjXLdu3TonJCR0ff/99+uV99+hLEnEe2Z2WnkbFhERqSxXXHFF+4cffnjV4sWL07t06bL7zjvvbA1wzTXXtH/qqaeWZ2ZmpkdHRxf7SObHH3+85ejRo5dnZmamf/3115n16tXLL22q8JEjR7Zq0KBB3uLFi9MXL16cfuaZZ+4zf8a0adNizz777G0A119/fdvhw4evX7BgQcYbb7zxww033JAQtNP6hBNO2JGVlbXwwgsv3Lp27drCx2evWLGi5rBhwzZmZWUtXLBgQa2srKxa33//fUZGRkb6vHnz6rz33nv1/v3vfzdo2bLlnkWLFqUvWbJk4fnnn5+9bt266GnTpjVasmTJwsWLF6c//PDD+0wPXtJ7BZCbm2vz58/P+Mtf/rLy/vvvb1103/0pSxLxNfCGme02s2wz22Fm2eU9kIiISEXYvHlz9I4dO6LPPPPMnQDXXnvt5q+//rrepk2bon/66aeovn37/gQwdOjQLcXtf/TRR+8cMWJE2wcffLD5pk2bomvUqFHq8WbNmtXg9ttvL0wsmjVrVni94YQTTkhq3rx5908++ST26quv3gLwxRdfNLj11lvjk5OTU84+++yOO3fujN6+fXvUN998U68gpoEDB2Y3aNCgsJ1WrVr9csopp/wE8P777zeYNWtWg5SUlJQuXbqk/PDDD7UyMzNr9ezZc/dnn33W4MYbb4x7//336zVp0iSvSZMmeTVr1swfNGhQwqRJkxrWq1dvr7nBS3qvCrZfeOGFWwGOOeaYn1atWlXuOUHKkkT8DegD1HH3Bu5e390b7G8nM5tgZhvMbEFY2b1mttrM5gWv/mHb/mBmWWa2yMxODys/IyjLMrOR5Tw/ERGRvTz88MPrxo0bt3z37t1Rxx9/fPK3335bK9K2Zs6cuXj16tXfd+nSZdcdd9zRGkKTY82dOzcjMzMzPTMzM33Dhg3fx8bG5pfWTp06dQq3uzu33Xbb2oL9V6xYseD222/f1L1795y5c+emd+vWbfef/vSnuBEjRrSqUaMG8+bNyxg4cODWqVOnNjzxxBMTyxN/wRTmMTExxU5hvj9lSSJWAgu8/DN1PQ+cUUz5E+7eI3hNg9B8HMDFQJdgn6fMLDq4M+QfQD8gBRgc1BURkd+oJk2a5DVo0CCv4Br++PHjm/Tp02dn06ZN8+rWrZv/ySef1AV48cUXGxe3/8KFC2v27t1790MPPbSue/fuPy1YsKBWaVOFn3DCCdlPPPFE84L1gjEFBWrUqMFTTz218vXXX2+yfv366OOOOy77z3/+c2H9L7/8sjbAkUceubMgpn//+98NsrOziz1ev379sl988cWm27dvjwJYunRpjdWrV8csW7asRv369fNvuummLcOHD183b968Otu3b4/asmVL9KBBg7Y//fTTKzMzM+uU5b3a33tcVmV52NSPwKfBBFw5BYX7u8XT3WeZWUIZ4zgHeMXdc4ClZpYF9A62Zbn7jwBm9kpQN72M7YqISEUqwy2ZFe3nn3+OatGiRfeC9RtvvHH9xIkTl954443tbrnllqj4+Picl19+eRnAM888s+yGG25oFxUVRZ8+fXbUr19/n1sdHn300eZffvllAzPzTp067R44cOD2qKgoSpoq/M9//vPaK6+8Mj4xMbFLVFSU33XXXWuGDh26LbzNdu3a7RkwYMCWxx57rPmzzz678pprrolPSkpKycvLs6OOOmrHMcccs+KRRx5ZM3DgwN8lJiY26dWr186mTZvuadiwYV52dvZef9Cff/752QsXLqx15JFHJkOol2Ly5MlLMzMza/7hD39oExUVRUxMjD/11FPLt23bFn3WWWd1zMnJMYAHHnhgnztKSnqvKsJ+pwI3s1HFlZflDo0giZjq7l2D9XsJ3S6aDaQBv3f3rWY2Bvja3V8K6o0H3guaOcPdrwnKLweOcrryLQQAABaaSURBVPdhpR1XU4EfXJoKXOTX4dcwFfj27dujCi4d3HXXXS3Xrl1bY+LEifu9VfNg2L17t8XExHiNGjX46KOP6g4bNqxdZmbmIf9HcWlTgZfliZUVeTvnWOABQvNwPAA8DlxVEQ2b2XXAdQDx8fEV0aSIiFQzr732Wuzjjz/eKi8vz+Li4nKmTJmyrKpjKpCVlXXYRRdd1CE/P58aNWr4M888s6yqYzpQJSYRZjbG3YeZ2TsEk2+Fc/cB5T2Yu68Pa/85YGqwuhpoG1a1TVBGKeVF234WeBZCPRHljU1ERKq/a6+9duu11167tarjKE63bt1yMjIyDvmeh/IorSdiCDAMeKyiDmZmrdy94B7W84CCOzfeBqaY2d+A1kAi8A2huToSzaw9oeThYuCSiopHREREIldaEvEDgLvPjKRhM3sZOBFoamargFHAiWbWg1DPxjLg+uAYC83sNUIDJnOBm909L2hnGDAdiAYmuPvCSOIRERGRilVaEtHMzIaXtLEMd2cMLqZ4fCn1HwIeKqZ8GjCttGOJiIjIwVdaEhEN1CN0SUFERERkL6UlEWvd/f6DFomIiBzyuk3qVqFTgc8fOr9Mz5248847W77++utNoqKiPCoqiqeeemr58ccf/9Ptt98e98477zSqXbt2PsA555yz5S9/+cs6gOjo6F6JiYm7c3NzLTo62i+++OLN99xzz/ro6NAznmbMmFHnjjvuaLtp06YatWvXzu/evftP48aNWzlx4sRGaWlpdV944YUVFXGOJ5xwQsfXX399adOmTfMefPDB5hMmTGjWtWvXXYMGDdqycOHC2g8//PC6ijhOVSgtiVAPhIiIVLmPPvqo7vTp0xvOnz8/vXbt2r527dqYnJwcu/XWW+PWr19fIyMjY2GdOnV869atUQ888EDLgv1q1qyZX/AchtWrV8dceOGFv8vOzo5+4okn1qxcuTLm0ksv7fDCCy/8WDDXxsSJExtt27atLE9yLpeZM2dmFSyPHz++2UcffbS4Q4cOe4Ki7WVtZ8+ePexvno+DrbQ365SDFoWIiEgJVq9eXaNx48a5tWvXdoBWrVrlNmnSJG/KlCnNxo0bt6JOnToO0KhRo/y//e1va4prIy4uLnfcuHHLJk6c2Dw/P5/HH3+8+UUXXbS5IIEAuPLKK7e2bds2N3y/KVOmxHbv3j25c+fOKcccc0zSypUrY6D46cSXL19eIzU1tVNycnJKYmJil4JHTcfFxXVbu3ZtzCWXXBK/atWqmv369Uu87777mo8ePbrJkCFD4qHk6cOHDx/e+txzz23fs2fP5PPPP799Zby/B6LEJMLdi539TERE5GA699xzs9esWXNYQkJC18suuyz+3XffrZeenl6zVatWvzRq1KjUia3CpaSk/JKXl8fq1atj0tPTa6empu7a3z6nnnrqznnz5mVmZGSkDxw4cMv999/fEoqfTnzChAmNTznllO2ZmZnpGRkZC4866qi92p8yZcqK5s2b75k5c+biotONlzR9OMCSJUtqzZo1a9E777yztKznerCUZe4MERGRKhMbG5u/YMGC9Pfff7/+xx9/XH/o0KEdbrvttrXhdZ588skmY8eObbFt27aYzz//PKNjx457SmqvPJYuXXrYueee22bjxo01fvnll6i2bdvmwH+nE7/ooou2DB48eGuHDh3yjz766J+uv/76hD179kQNHDhw6zHHHLN7f+0X+OKLLxosWbKkdsF6wfThAGeccca2evXqHZIPUazwaz8iIiIVLSYmhrPOOmvHE088seavf/3rimnTpsWuXbv2sK1bt0YB3HrrrZszMzPT69evn1fSlNbp6emHRUdHExcXl9u5c+fdaWlpdYqrF27YsGHxN91004bFixenjxkzZnlOTk4UFD+deL9+/XbOmjVrUVxc3C9XXXVV+zFjxjQp6/mVNn143bp1y9zbcrApiRARkUPad999V3P+/Pk1C9a//fbb2h07dsy5+OKLN1199dXxu3btMoDc3Fz27NlTbAKxZs2amGuvvbbdlVdeuSEqKooRI0ZseO2115oUTBsOMGnSpIYFYx4K7NixIzo+Pn4PwPPPP1+YFBQ3nfjixYsPa9OmzZ7f//73m4YMGbJx7ty5+01SCpQ0ffihTpczRESkzMp6S2ZFys7Ojr7lllvis7Ozo6Ojoz0hISFn0qRJyxs3bpx3++23t05OTu5St27d/Fq1auUPGjRoU7t27fYA5OTkRCUnJ6cU3OI5aNCgzaNGjVoP0LZt29wXXnjhxzvuuKPN5s2ba0RFRfnRRx+98/zzz88OP/bdd9+9ZvDgwR1iY2NzjzvuuB0rVqyoCcVPJz5u3LjGo0ePbhkTE+N16tTJmzx5cpnHMJQ0fXhFvo+VYb9TgVdHmgr84NJU4CK/Dr+GqcCl4pU2FbguZ4iIiEhElESIiIhIRJREiIhIafLz8/P1BOPfqODfvsS7Q5REiIhIaRZs3LgxVonEb09+fr5t3LgxFlhQUh3dnSEiIiXKzc29Zt26dePWrVvXFf3h+VuTDyzIzc29pqQKSiJERKREvXr12gAMqOo45NCkrFJEREQioiRCREREIqIkQkRERCKiJEJEREQioiRCREREIqIkQkRERCKiJEJEREQioiRCREREIqIkQkRERCKiJEJEREQiUmlJhJlNMLMNZrYgrKyxmX1oZkuCn42CcjOz0WaWZWbfm1nPsH2GBvWXmNnQyopXREREyqcyeyKeB84oUjYS+NjdE4GPg3WAfkBi8LoOGAuhpAMYBRwF9AZGFSQeIiIiUrUqLYlw91nAliLF5wCTguVJwLlh5S94yNdAQzNrBZwOfOjuW9x9K/Ah+yYmIiIiUgUO9piIFu6+NlheB7QIluOAlWH1VgVlJZXvw8yuM7M0M0vbuHFjxUYtIiIi+6iygZXu7oBXYHvPunuqu6c2a9asopoVERGREhzsJGJ9cJmC4OeGoHw10DasXpugrKRyERERqWIHO4l4Gyi4w2Io8FZY+ZDgLo2jge3BZY/pwGlm1igYUHlaUCYiIiJVLKayGjazl4ETgaZmtorQXRaPAK+Z2dXAcuCioPo0oD+QBewCrgRw9y1m9gAwO6h3v7sXHawpIiIiVaDSkgh3H1zCplOKqevAzSW0MwGYUIGhiYiISAXQEytFREQkIkoiREREJCJKIkRERCQiSiJEREQkIkoiREREJCJKIkRERCQiSiJEREQkIkoiREREJCJKIkRERCQiSiJEREQkIkoiREREJCJKIkRERCQiSiJEREQkIkoiREREJCJKIkRERCQiSiJEREQkIkoiREREJCJKIkRERCQiSiJEREQkIkoiREREJCJKIkRERCQiSiJEREQkIkoiREREJCJKIkRERCQiSiJEREQkIkoiREREJCJVkkSY2TIzm29m88wsLShrbGYfmtmS4GejoNzMbLSZZZnZ92bWsypiFhERkb1VZU/ESe7ew91Tg/WRwMfungh8HKwD9AMSg9d1wNiDHqmIiIjs41C6nHEOMClYngScG1b+god8DTQ0s1ZVEaCIiIj8V1UlEQ58YGZzzOy6oKyFu68NltcBLYLlOGBl2L6rgrK9mNl1ZpZmZmkbN26srLhFREQkEFNFxz3O3VebWXPgQzPLDN/o7m5mXp4G3f1Z4FmA1NTUcu0rIiIi5VclPRHuvjr4uQF4A+gNrC+4TBH83BBUXw20Ddu9TVAmIiIiVeigJxFmVtfM6hcsA6cBC4C3gaFBtaHAW8Hy28CQ4C6No4HtYZc9REREpIpUxeWMFsAbZlZw/Cnu/r6ZzQZeM7OrgeXARUH9aUB/IAvYBVx58EMWERGRog56EuHuPwKHF1O+GTilmHIHbj4IoYmIiEg5HEq3eIqIiEg1oiRCREREIqIkQkRERCKiJEJEREQioiRCREREIqIkQkRERCKiJEJEREQioiRCREREIqIkopoZMWIE7du3x8xYsGBBYfm7775Lz5496datGyeccAJLly7dZ9/77rtvn/3C7dq1i0GDBtGxY0eSk5OZOnXqPnU+/fRToqOjGTNmTMWdlIiIVEtKIqqZc889l1mzZtGuXbvCsq1btzJ06FBeeeUV5s+fz7XXXsuNN964135z587l66+/3mu/oh577DEaNGhAVlYW77zzDtdccw07d+4s3L5jxw7uvPNO+vXrV/EnJiIi1Y6SiGrmuOOOo23btnuVZWVl0aJFC5KSkgDo378/06dPZ9OmTQDk5ORw8803M3bs2FLbfvXVV7n++usBSExMJDU1lffee69w+/Dhw7njjjto2rRpRZ6SiIhUU0oifgWSkpJYt24ds2fPBmDy5MkArFixAoB77rmHyy67jISEhFLbWbFixV49FfHx8axcuRKA9957j+3btzNw4MBKOAMREamOqmIWT6lgsbGxvPrqq9x+++38/PPP9OvXj4YNGxITE8NXX31FWloajzzySMTtb9u2jZEjR/Lhhx9WYNQiIlLdqSfiV6Jv3758/vnnpKWlMWzYMHbv3k2HDh2YOXMmGRkZtG/fnoSEBFatWsXpp5/OBx98sE8b8fHxLF++vHB9xYoVtG3blgULFrB27Vp69+5NQkIC//rXvxg1ahT333//wTxFERE5xKgn4ldi3bp1tGzZkvz8fO666y5uuOEG6taty8iRIxk5cmRhvYSEBKZOnUrXrl33aePCCy/kmWeeITU1lSVLljB79mxefvll6tevz4YNGwrrXXHFFaSmpjJs2LCDcm4iInJoUhJRndwbyy3v/cy/M/awbqfT9+juNKljLLypHn98ezdfrMzjlzzntN/F8MgZteDeifu2sW0HPHU0NI8GoMfTO5l2aR1a14/ijl+cK77cTcfG44mOMp7tW5P6j7fZt415u2HDq7Dp7tB6+/hKPGkRETlUmbtXdQwVLjU11dPS0qo6jIp3b2xVR1CsbodoEjF/6PyqDkGkWjGzOe6eWtVxSPWhMREiIiISESURIiIiEhElESIiIhIRJREiIiISESURIiIiEhElERFYvHgxffr0ISkpiT59+rBkyZJ96tx77700b96cHj160KNHD26++ebCbS+99BLdu3cnJiZGs2FKtVGWz/3EiRPp3r07PXr0oFu3bowePbpM26o6ttJ+X6syriFDhhTG1KNHD6Kionj77bcrJDaRiqBbPCNw8sknc9VVV3HZZZfx0ksvMWHCBD755JO96tx7773s3LmTxx57bJ/9FyxYQFRUFI888gi9e/cu+0ObdItnuegWz4pVls99dnY29evXx8zYsWMHXbt25Z133qF79+6lbqvq2Er7fa3KuMJ99913nHzyyaxZs4aaNWtWaJwFdIunlJd6Isppw4YNzJ07l8GDBwMwePBg5s6dy8aNG8vcRteuXUlJSSEqSm+/VA9l/dw3aNAAMwNg165d7Nmzp3C9tG1VHVtlqOi4xo8fz6WXXlppCYRIJPQtVk4rV64kLi6O6OjQEx+jo6Np3bp14WyX4V555RW6d+/OaaedxldffXWwQxWpMOX53L/99tt06dKFdu3acccdd9CtW7cybavq2Cr697Wi4gL45ZdfmDJlClddddUBxyVSkapNEmFmZ5jZIjPLMrOR+9+jat1www0sXbqU77//njvuuINzzjmHzZs3V3VYIpVuwIABLFy4kMWLF/Piiy+yaNGiMm2rytiq+vd1f+/Lm2++SXx8PD169DhoMYmURbVIIswsGvgH0A9IAQabWUpVxNK2bVtWr15NXl4eAHl5eaxZs4a2bdvuVa9ly5bUqFEDgFNPPbVwNkyR6qisn/tw8fHx9O7dm6lTp5ZrW1XEVhm/rxX5nk2YMEG9EHJIqhZJBNAbyHL3H939F+AV4JyqCKRgBPfLL78MwMsvv8wRRxxBs2bN9qq3evXqwuV58+axbNkyOnXqdFBjFakoZf3cZ2RkFC5v2rSJGTNmFHbNl7atqmOrjN/XiogLYNWqVXz22WdceumlBxSPSGWoFndnmNlA4Ax3vyZYvxw4yt2HhdW5DrguWO0EVGY/aS0ggdAsqLnAUiAH6AisAXYF2+sCHrzWANuD/RsDbYDoYFs+sBj4eT/HbQpsqrCzECmfsnzu2wINCH2uIfR5LZhHvrRtpSnL5/5AY0ug5N/XA3GgcQG0BOoAP1ZAPPvTzt2b7b+aSMivZipwd38WeLaq46hMZpam26/kt0afe5FDV3W5nLGaULZeoE1QJiIiIlWkuiQRs4FEM2tvZocBFwN6bJuIiEgVqhaXM9w918yGAdMJjSOY4O4LqzisqvCrvlwjUgJ97kUOUdViYKWIiIgceqrL5QwRERE5xCiJEBERkYgoiRAREZGIKImoAGbW0MxuimC/aWbWMIL9rjCz1mHr46rqMeAiFcnMTjSzEp+FHWw/Jmz9BjMbcnCiE5GilERUjIbAPkmEmZV694u793f3bREc7wqgMIlw92vcPT2CdkQOimD+m4pwIlCYRLj70+7+QgW1LSLlpCSiYjwCdDCzeWY228w+M7O3gXQAM3vTzOaY2cLg8dwE5cvMrKmZJZhZhpk9F9T5wMxqF3eg4BHgqcDk4Hi1zexTM0sNtu80s78G7XxkZr2D7T+a2YCgTnRQZ7aZfW9m11f2GyS/XsHnN9PMJgef43+ZWZ3g8/0XM5sLXGhmp5nZV2Y218z+aWb1gv3PCPafC5xf2nGAG4Dbg8/+8WZ2r5mNCLZ/amZPmFlaEMeRZvZvM1tiZg+GtXOZmX0TtPFMBSY4Ir85SiIqxkjgB3fvAdwB9ARudfekYPtV7t6L0Jf/LWbWpJg2EoF/uHsXYBtwQXEHcvd/AWnApe7ew913F6lSF/gkaGcH8CBwKnAecH9Q52pgu7sfCRwJXGtm7SM5cZFAJ+Apd+8MZPPfnrnN7t4T+Aj4I9A3WE8DhptZLeA54GygF6F5Iorl7suAp4Engs/+Z8VU+yV4RPbTwFvAzUBX4Aoza2JmnYFBwLHB72seoJmtRCJULR42VQ194+5Lw9ZvMbPzguW2hBKGzUX2Weru84LlOYQm7YnEL8D7wfJ8IMfd95jZ/LA2TwO6B70aALFBTOExi5THSnf/Ilh+CbglWH41+Hk0kAJ8YWYAhwFfAcmEPvtLAMzsJf47kV4kCp5kOx9Y6O5rg3Z/JPS7dxyhZGV2EEdtyjYJmIgUQ0lE5fipYMHMTgT6An3cfZeZfUpoZr+icsKW8wj95xaJPf7fJ4jlF7Tr7vlhYzQM+F93nx7hMUSKKvrUuoL1gt8FAz5098HhlcysRwXHUfB7lM/ev1P5hP6/M2CSu/+hgo8r8pukyxkVYwdQv4RtscDWIIFIJvQXWWUeryymAzeaWQ0AM0sys7oVEJf8dsWbWZ9g+RLg8yLbvwaONbOOAGZW18ySgEwgwcw6BPUGU7oD/ex/DAw0s+ZBHI3NrN0BtCfym6YkogK4+2ZC3bQLgL8W2fw+EGNmGYQGYH5dAYd8Hni6YGBlBPuPIzToc24Q8zOoV0oOzCLg5uBz3ggYG77R3TcSuqvoZTP7nuBShrv/TOjyxbvBwMr9XVp4BzivYGBleYMM7mL6I/BBEMeHQKvytiMiIZo7Q0QOSHDXxFR371rFoYjIQaaeCBEREYmIeiIOYWb2D+DYIsVPuvvEqohH5GAxsyuBW4sUf+HuN1dFPCJSPCURIiIiEhFdzhAREZGIKIkQERGRiCiJEBERkYgoiRAREZGI/H9K8nzmIeKyxQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['acc_train', 'acc_test']\n",
        "y_label = 'Acc_Score'\n",
        "title = 'Train/Test Accuracy per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "a8390d25-cca2-4ece-c532-be94328343e9",
        "id": "JSOHu4POUF3h"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['f1_train', 'f1_test']\n",
        "y_label = 'F1_Score'\n",
        "title = 'Train/Test F1_Score per Classifier'\n",
        "eval_plot(labels, y_label, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "ff47ede7-484b-4617-a517-174d9032fa80",
        "id": "0L0Ydd73UF3i"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbVo4bruw8Ri"
      },
      "source": [
        "#Mazajak\n",
        "Didn't work well with me"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GPntbPilFXX",
        "outputId": "7bca1ce2-b019-41c2-95c0-a5c9a623f324"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-11 00:43:00--  http://mazajak.inf.ed.ac.uk:8000/get_sg_100\n",
            "Resolving mazajak.inf.ed.ac.uk (mazajak.inf.ed.ac.uk)... 129.215.216.121, 2001:630:3c1:216:1a66:daff:fe4d:ef10\n",
            "Connecting to mazajak.inf.ed.ac.uk (mazajak.inf.ed.ac.uk)|129.215.216.121|:8000... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5325297114 (5.0G) [application/octet-stream]\n",
            "Saving to: ‘mazajak.bin’\n",
            "\n",
            "mazajak.bin         100%[===================>]   4.96G  31.0MB/s    in 2m 43s  \n",
            "\n",
            "2022-03-11 00:45:44 (31.1 MB/s) - ‘mazajak.bin’ saved [5325297114/5325297114]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget http://mazajak.inf.ed.ac.uk:8000/get_sg_100 -O mazajak.bin"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models import KeyedVectors\n",
        "word2vec=KeyedVectors.load_word2vec_format(\"/content/mazajak.bin\",\n",
        "                                           binary=True, unicode_errors='ignore')\n",
        "print(\"found %s word vectors of word2vec\" % len(word2vec.vocab))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOQxVZshjp1u",
        "outputId": "09811e9b-2291-4d39-cbff-c5a03eb4247b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "found 4347845 word vectors of word2vec\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word2vec.similarity(\"مصر\",\"القاهرة\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MA8K1331mDyv",
        "outputId": "c8a353aa-6863-4f60-d627-3019fee51178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.71990234"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word2vec.vocab"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6z7-fBum72Z",
        "outputId": "b1713d9c-b80d-4554-c10d-da69ac5225b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'</s>': <gensim.models.keyedvectors.Vocab at 0x7fa0511a4cd0>,\n",
              " 'من': <gensim.models.keyedvectors.Vocab at 0x7fa054c60450>,\n",
              " 'في': <gensim.models.keyedvectors.Vocab at 0x7fa054c60710>,\n",
              " 'الله': <gensim.models.keyedvectors.Vocab at 0x7fa062723ad0>,\n",
              " 'و': <gensim.models.keyedvectors.Vocab at 0x7fa05119d4d0>,\n",
              " 'لا': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4310>,\n",
              " 'على': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4090>,\n",
              " 'ما': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4890>,\n",
              " 'كل': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4290>,\n",
              " '️': <gensim.models.keyedvectors.Vocab at 0x7fa05119d510>,\n",
              " 'اللهم': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4210>,\n",
              " 'يا': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4610>,\n",
              " 'اللي': <gensim.models.keyedvectors.Vocab at 0x7fa0511c46d0>,\n",
              " 'ولا': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4450>,\n",
              " 'أن': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4710>,\n",
              " 'عن': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4790>,\n",
              " 'انا': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4a10>,\n",
              " 'ان': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4a50>,\n",
              " 'مع': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4910>,\n",
              " 'بس': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4ad0>,\n",
              " 'لو': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4b10>,\n",
              " 'هو': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4810>,\n",
              " 'لك': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4b50>,\n",
              " 'يارب': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4b90>,\n",
              " 'والله': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4550>,\n",
              " 'هذا': <gensim.models.keyedvectors.Vocab at 0x7fa0511c43d0>,\n",
              " 'كان': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4c10>,\n",
              " 'لي': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4bd0>,\n",
              " 'الناس': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4750>,\n",
              " 'بعد': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4c50>,\n",
              " 'ف': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4510>,\n",
              " 'حتى': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4c90>,\n",
              " 'ب': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4cd0>,\n",
              " 'له': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4350>,\n",
              " 'انت': <gensim.models.keyedvectors.Vocab at 0x7fa0511c48d0>,\n",
              " 'فيه': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4d50>,\n",
              " 'علي': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4410>,\n",
              " 'إن': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4d10>,\n",
              " 'قلبي': <gensim.models.keyedvectors.Vocab at 0x7fa0511c41d0>,\n",
              " 'يوم': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4d90>,\n",
              " 'شي': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4dd0>,\n",
              " 'اليوم': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4e10>,\n",
              " 'شيء': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4e50>,\n",
              " 'الي': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4f10>,\n",
              " 'سبحان': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4e90>,\n",
              " 'الذي': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4ed0>,\n",
              " 'أنا': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4f50>,\n",
              " 'هي': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4fd0>,\n",
              " 'لما': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4a90>,\n",
              " 'ع': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4f90>,\n",
              " 'إلا': <gensim.models.keyedvectors.Vocab at 0x7fa0511c49d0>,\n",
              " 'إلى': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4850>,\n",
              " 'ل': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4590>,\n",
              " 'قبل': <gensim.models.keyedvectors.Vocab at 0x7fa0511c42d0>,\n",
              " 'غير': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4690>,\n",
              " 'خير': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4190>,\n",
              " 'فيها': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4990>,\n",
              " 'اذا': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4490>,\n",
              " 'الا': <gensim.models.keyedvectors.Vocab at 0x7fa0511c4950>,\n",
              " 'عليك': <gensim.models.keyedvectors.Vocab at 0x7fa051159050>,\n",
              " 'ربي': <gensim.models.keyedvectors.Vocab at 0x7fa051159090>,\n",
              " 'ي': <gensim.models.keyedvectors.Vocab at 0x7fa0511590d0>,\n",
              " 'عليه': <gensim.models.keyedvectors.Vocab at 0x7fa051159110>,\n",
              " 'او': <gensim.models.keyedvectors.Vocab at 0x7fa051159150>,\n",
              " 'كنت': <gensim.models.keyedvectors.Vocab at 0x7fa051159190>,\n",
              " 'شخص': <gensim.models.keyedvectors.Vocab at 0x7fa051159210>,\n",
              " 'قال': <gensim.models.keyedvectors.Vocab at 0x7fa051159250>,\n",
              " 'الدنيا': <gensim.models.keyedvectors.Vocab at 0x7fa0511592d0>,\n",
              " 'عشان': <gensim.models.keyedvectors.Vocab at 0x7fa0511591d0>,\n",
              " 'لكن': <gensim.models.keyedvectors.Vocab at 0x7fa051159290>,\n",
              " 'مش': <gensim.models.keyedvectors.Vocab at 0x7fa051159310>,\n",
              " 'واحد': <gensim.models.keyedvectors.Vocab at 0x7fa051159350>,\n",
              " 'أنت': <gensim.models.keyedvectors.Vocab at 0x7fa051159390>,\n",
              " 'م': <gensim.models.keyedvectors.Vocab at 0x7fa0511593d0>,\n",
              " 'الحياة': <gensim.models.keyedvectors.Vocab at 0x7fa051159410>,\n",
              " 'أو': <gensim.models.keyedvectors.Vocab at 0x7fa051159450>,\n",
              " 'يكون': <gensim.models.keyedvectors.Vocab at 0x7fa0511594d0>,\n",
              " 'العظيم': <gensim.models.keyedvectors.Vocab at 0x7fa051159510>,\n",
              " 'مثل': <gensim.models.keyedvectors.Vocab at 0x7fa051159490>,\n",
              " 'الحب': <gensim.models.keyedvectors.Vocab at 0x7fa0511595d0>,\n",
              " 'إله_إلا': <gensim.models.keyedvectors.Vocab at 0x7fa051159550>,\n",
              " 'به': <gensim.models.keyedvectors.Vocab at 0x7fa051159590>,\n",
              " 'قد': <gensim.models.keyedvectors.Vocab at 0x7fa051159610>,\n",
              " 'اي': <gensim.models.keyedvectors.Vocab at 0x7fa051159650>,\n",
              " 'الحمدلله': <gensim.models.keyedvectors.Vocab at 0x7fa051159690>,\n",
              " 'بعض': <gensim.models.keyedvectors.Vocab at 0x7fa0511596d0>,\n",
              " 'لم': <gensim.models.keyedvectors.Vocab at 0x7fa051159710>,\n",
              " 'الى': <gensim.models.keyedvectors.Vocab at 0x7fa051159750>,\n",
              " 'إذا': <gensim.models.keyedvectors.Vocab at 0x7fa0511597d0>,\n",
              " 'يعني': <gensim.models.keyedvectors.Vocab at 0x7fa051159810>,\n",
              " 'كيف': <gensim.models.keyedvectors.Vocab at 0x7fa051159790>,\n",
              " 'ومن': <gensim.models.keyedvectors.Vocab at 0x7fa0511598d0>,\n",
              " 'وانا': <gensim.models.keyedvectors.Vocab at 0x7fa051159850>,\n",
              " 'لنا': <gensim.models.keyedvectors.Vocab at 0x7fa051159890>,\n",
              " 'مو': <gensim.models.keyedvectors.Vocab at 0x7fa051159910>,\n",
              " 'بك': <gensim.models.keyedvectors.Vocab at 0x7fa051159950>,\n",
              " 'تكون': <gensim.models.keyedvectors.Vocab at 0x7fa051159990>,\n",
              " 'بين': <gensim.models.keyedvectors.Vocab at 0x7fa0511599d0>,\n",
              " 'انك': <gensim.models.keyedvectors.Vocab at 0x7fa051159a10>,\n",
              " 'فى': <gensim.models.keyedvectors.Vocab at 0x7fa051159a50>,\n",
              " 'فقط': <gensim.models.keyedvectors.Vocab at 0x7fa051159ad0>,\n",
              " 'لها': <gensim.models.keyedvectors.Vocab at 0x7fa051159b10>,\n",
              " 'التي': <gensim.models.keyedvectors.Vocab at 0x7fa051159a90>,\n",
              " 'أحد': <gensim.models.keyedvectors.Vocab at 0x7fa051159bd0>,\n",
              " 'ليس': <gensim.models.keyedvectors.Vocab at 0x7fa051159b50>,\n",
              " 'اني': <gensim.models.keyedvectors.Vocab at 0x7fa051159b90>,\n",
              " 'احد': <gensim.models.keyedvectors.Vocab at 0x7fa051159c10>,\n",
              " 'يقول': <gensim.models.keyedvectors.Vocab at 0x7fa051159c50>,\n",
              " 'وهو': <gensim.models.keyedvectors.Vocab at 0x7fa051159c90>,\n",
              " 'عند': <gensim.models.keyedvectors.Vocab at 0x7fa051159cd0>,\n",
              " 'انه': <gensim.models.keyedvectors.Vocab at 0x7fa051159d10>,\n",
              " 'منك': <gensim.models.keyedvectors.Vocab at 0x7fa051159d50>,\n",
              " 'ثم': <gensim.models.keyedvectors.Vocab at 0x7fa051159dd0>,\n",
              " 'اكثر': <gensim.models.keyedvectors.Vocab at 0x7fa051159e10>,\n",
              " 'قلبك': <gensim.models.keyedvectors.Vocab at 0x7fa051159d90>,\n",
              " 'رب': <gensim.models.keyedvectors.Vocab at 0x7fa051159ed0>,\n",
              " 'كانت': <gensim.models.keyedvectors.Vocab at 0x7fa051159e50>,\n",
              " 'جدا': <gensim.models.keyedvectors.Vocab at 0x7fa051159e90>,\n",
              " 'هذه': <gensim.models.keyedvectors.Vocab at 0x7fa051159f10>,\n",
              " 'فيك': <gensim.models.keyedvectors.Vocab at 0x7fa051159f50>,\n",
              " 'أكثر': <gensim.models.keyedvectors.Vocab at 0x7fa051159f90>,\n",
              " 'استغفر': <gensim.models.keyedvectors.Vocab at 0x7fa051159fd0>,\n",
              " 'بها': <gensim.models.keyedvectors.Vocab at 0x7fa05115e090>,\n",
              " 'لهم': <gensim.models.keyedvectors.Vocab at 0x7fa05115e0d0>,\n",
              " 'القلب': <gensim.models.keyedvectors.Vocab at 0x7fa05115e050>,\n",
              " 'عندما': <gensim.models.keyedvectors.Vocab at 0x7fa05115e110>,\n",
              " 'حب': <gensim.models.keyedvectors.Vocab at 0x7fa05115e150>,\n",
              " 'ناس': <gensim.models.keyedvectors.Vocab at 0x7fa05115e190>,\n",
              " '3': <gensim.models.keyedvectors.Vocab at 0x7fa05115e1d0>,\n",
              " 'محمد': <gensim.models.keyedvectors.Vocab at 0x7fa05115e210>,\n",
              " 'هنا': <gensim.models.keyedvectors.Vocab at 0x7fa05115e250>,\n",
              " 'ولكن': <gensim.models.keyedvectors.Vocab at 0x7fa05115e290>,\n",
              " 'وما': <gensim.models.keyedvectors.Vocab at 0x7fa05115e2d0>,\n",
              " 'حد': <gensim.models.keyedvectors.Vocab at 0x7fa05115e310>,\n",
              " 'هل': <gensim.models.keyedvectors.Vocab at 0x7fa05115e390>,\n",
              " 'اللهم_صل_وسلم': <gensim.models.keyedvectors.Vocab at 0x7fa05115e3d0>,\n",
              " 'صباح_الخير': <gensim.models.keyedvectors.Vocab at 0x7fa05115e350>,\n",
              " 'انتي': <gensim.models.keyedvectors.Vocab at 0x7fa05115e410>,\n",
              " 'هم': <gensim.models.keyedvectors.Vocab at 0x7fa05115e450>,\n",
              " 'جميل': <gensim.models.keyedvectors.Vocab at 0x7fa05115e490>,\n",
              " 'نفسي': <gensim.models.keyedvectors.Vocab at 0x7fa05115e4d0>,\n",
              " 'نفسك': <gensim.models.keyedvectors.Vocab at 0x7fa05115e510>,\n",
              " 'قلب': <gensim.models.keyedvectors.Vocab at 0x7fa05115e550>,\n",
              " 'أجمل': <gensim.models.keyedvectors.Vocab at 0x7fa05115e590>,\n",
              " '2': <gensim.models.keyedvectors.Vocab at 0x7fa05115e5d0>,\n",
              " '1': <gensim.models.keyedvectors.Vocab at 0x7fa05115e650>,\n",
              " 'هناك': <gensim.models.keyedvectors.Vocab at 0x7fa05115e690>,\n",
              " 'وش': <gensim.models.keyedvectors.Vocab at 0x7fa05115e610>,\n",
              " 'وكل': <gensim.models.keyedvectors.Vocab at 0x7fa05115e6d0>,\n",
              " 'وانت': <gensim.models.keyedvectors.Vocab at 0x7fa05115e710>,\n",
              " 'الكلام': <gensim.models.keyedvectors.Vocab at 0x7fa05115e750>,\n",
              " 'أي': <gensim.models.keyedvectors.Vocab at 0x7fa05115e790>,\n",
              " 'ده': <gensim.models.keyedvectors.Vocab at 0x7fa05115e7d0>,\n",
              " 'راح': <gensim.models.keyedvectors.Vocab at 0x7fa05115e810>,\n",
              " 'منه': <gensim.models.keyedvectors.Vocab at 0x7fa05115e850>,\n",
              " 'دائما': <gensim.models.keyedvectors.Vocab at 0x7fa05115e890>,\n",
              " 'العالم': <gensim.models.keyedvectors.Vocab at 0x7fa05115e8d0>,\n",
              " 'أكبر': <gensim.models.keyedvectors.Vocab at 0x7fa05115e910>,\n",
              " 'ذلك': <gensim.models.keyedvectors.Vocab at 0x7fa05115e950>,\n",
              " 'عليها': <gensim.models.keyedvectors.Vocab at 0x7fa05115e990>,\n",
              " 'بالله': <gensim.models.keyedvectors.Vocab at 0x7fa05115e9d0>,\n",
              " 'شكرا': <gensim.models.keyedvectors.Vocab at 0x7fa05115ea10>,\n",
              " 'حين': <gensim.models.keyedvectors.Vocab at 0x7fa05115ea50>,\n",
              " 'لن': <gensim.models.keyedvectors.Vocab at 0x7fa05115ea90>,\n",
              " 'كم': <gensim.models.keyedvectors.Vocab at 0x7fa05115ead0>,\n",
              " 'أستغفر': <gensim.models.keyedvectors.Vocab at 0x7fa05115eb10>,\n",
              " 'صباح': <gensim.models.keyedvectors.Vocab at 0x7fa05115eb50>,\n",
              " 'ليه': <gensim.models.keyedvectors.Vocab at 0x7fa05115eb90>,\n",
              " 'ايه': <gensim.models.keyedvectors.Vocab at 0x7fa05115ebd0>,\n",
              " 'ممكن': <gensim.models.keyedvectors.Vocab at 0x7fa05115ec10>,\n",
              " 'معك': <gensim.models.keyedvectors.Vocab at 0x7fa05115ec50>,\n",
              " 'لكل': <gensim.models.keyedvectors.Vocab at 0x7fa05115ec90>,\n",
              " 'بل': <gensim.models.keyedvectors.Vocab at 0x7fa05115ecd0>,\n",
              " 'وبحمده_سبحان': <gensim.models.keyedvectors.Vocab at 0x7fa05115ed10>,\n",
              " 'وحده': <gensim.models.keyedvectors.Vocab at 0x7fa05115ed50>,\n",
              " 'عندي': <gensim.models.keyedvectors.Vocab at 0x7fa05115ed90>,\n",
              " 'وفي': <gensim.models.keyedvectors.Vocab at 0x7fa05115edd0>,\n",
              " 'اول': <gensim.models.keyedvectors.Vocab at 0x7fa05115ee10>,\n",
              " 'كلام': <gensim.models.keyedvectors.Vocab at 0x7fa05115ee50>,\n",
              " 'طيب': <gensim.models.keyedvectors.Vocab at 0x7fa05115ee90>,\n",
              " 'ترى': <gensim.models.keyedvectors.Vocab at 0x7fa05115eed0>,\n",
              " 'دون': <gensim.models.keyedvectors.Vocab at 0x7fa05115ef10>,\n",
              " 'حياتي': <gensim.models.keyedvectors.Vocab at 0x7fa05115ef50>,\n",
              " 'مره': <gensim.models.keyedvectors.Vocab at 0x7fa05115ef90>,\n",
              " 'غيرك': <gensim.models.keyedvectors.Vocab at 0x7fa05115efd0>,\n",
              " 'بكل': <gensim.models.keyedvectors.Vocab at 0x7fa051163050>,\n",
              " 'ههههههههههههههههههههههههههههه': <gensim.models.keyedvectors.Vocab at 0x7fa051163090>,\n",
              " 'كله': <gensim.models.keyedvectors.Vocab at 0x7fa0511630d0>,\n",
              " 'لازم': <gensim.models.keyedvectors.Vocab at 0x7fa051163110>,\n",
              " 'الهلال': <gensim.models.keyedvectors.Vocab at 0x7fa051163150>,\n",
              " 'الجنة': <gensim.models.keyedvectors.Vocab at 0x7fa051163190>,\n",
              " 'جديد': <gensim.models.keyedvectors.Vocab at 0x7fa0511631d0>,\n",
              " 'كذا': <gensim.models.keyedvectors.Vocab at 0x7fa051163210>,\n",
              " 'تقول': <gensim.models.keyedvectors.Vocab at 0x7fa051163250>,\n",
              " 'ولو': <gensim.models.keyedvectors.Vocab at 0x7fa051163290>,\n",
              " 'احبك': <gensim.models.keyedvectors.Vocab at 0x7fa0511632d0>,\n",
              " 'نفس': <gensim.models.keyedvectors.Vocab at 0x7fa051163310>,\n",
              " 'اكبر': <gensim.models.keyedvectors.Vocab at 0x7fa051163350>,\n",
              " 'صلى': <gensim.models.keyedvectors.Vocab at 0x7fa051163390>,\n",
              " 'اجمل': <gensim.models.keyedvectors.Vocab at 0x7fa0511633d0>,\n",
              " 'يمكن': <gensim.models.keyedvectors.Vocab at 0x7fa051163410>,\n",
              " 'عنه': <gensim.models.keyedvectors.Vocab at 0x7fa051163450>,\n",
              " 'يالله': <gensim.models.keyedvectors.Vocab at 0x7fa051163490>,\n",
              " 'منها': <gensim.models.keyedvectors.Vocab at 0x7fa0511634d0>,\n",
              " 'قلت': <gensim.models.keyedvectors.Vocab at 0x7fa051163510>,\n",
              " 'علينا': <gensim.models.keyedvectors.Vocab at 0x7fa051163550>,\n",
              " 'كما': <gensim.models.keyedvectors.Vocab at 0x7fa051163590>,\n",
              " 'حاجه': <gensim.models.keyedvectors.Vocab at 0x7fa0511635d0>,\n",
              " 'بدون': <gensim.models.keyedvectors.Vocab at 0x7fa051163610>,\n",
              " 'كثر': <gensim.models.keyedvectors.Vocab at 0x7fa051163650>,\n",
              " 'الخير': <gensim.models.keyedvectors.Vocab at 0x7fa051163690>,\n",
              " 'عمري': <gensim.models.keyedvectors.Vocab at 0x7fa0511636d0>,\n",
              " 'لمن': <gensim.models.keyedvectors.Vocab at 0x7fa051163710>,\n",
              " 'فيني': <gensim.models.keyedvectors.Vocab at 0x7fa051163750>,\n",
              " 'هذي': <gensim.models.keyedvectors.Vocab at 0x7fa051163790>,\n",
              " 'مني': <gensim.models.keyedvectors.Vocab at 0x7fa0511637d0>,\n",
              " 'لكم': <gensim.models.keyedvectors.Vocab at 0x7fa051163810>,\n",
              " 'وقت': <gensim.models.keyedvectors.Vocab at 0x7fa051163850>,\n",
              " 'حلو': <gensim.models.keyedvectors.Vocab at 0x7fa051163890>,\n",
              " 'حاجة': <gensim.models.keyedvectors.Vocab at 0x7fa0511638d0>,\n",
              " 'الوقت': <gensim.models.keyedvectors.Vocab at 0x7fa051163910>,\n",
              " 'كلها': <gensim.models.keyedvectors.Vocab at 0x7fa051163950>,\n",
              " 'شعور': <gensim.models.keyedvectors.Vocab at 0x7fa051163990>,\n",
              " 'الشخص': <gensim.models.keyedvectors.Vocab at 0x7fa0511639d0>,\n",
              " 'أحبك': <gensim.models.keyedvectors.Vocab at 0x7fa051163a10>,\n",
              " 'كثير': <gensim.models.keyedvectors.Vocab at 0x7fa051163a50>,\n",
              " 'شر': <gensim.models.keyedvectors.Vocab at 0x7fa051163a90>,\n",
              " 'عليه_وسلم': <gensim.models.keyedvectors.Vocab at 0x7fa051163ad0>,\n",
              " 'الحمد_لله': <gensim.models.keyedvectors.Vocab at 0x7fa051163b10>,\n",
              " 'حياتك': <gensim.models.keyedvectors.Vocab at 0x7fa051163b50>,\n",
              " 'أحب': <gensim.models.keyedvectors.Vocab at 0x7fa051163b90>,\n",
              " 'صل': <gensim.models.keyedvectors.Vocab at 0x7fa051163bd0>,\n",
              " 'امين': <gensim.models.keyedvectors.Vocab at 0x7fa051163c10>,\n",
              " 'صار': <gensim.models.keyedvectors.Vocab at 0x7fa051163c50>,\n",
              " 'دي': <gensim.models.keyedvectors.Vocab at 0x7fa051163c90>,\n",
              " 'حق': <gensim.models.keyedvectors.Vocab at 0x7fa051163cd0>,\n",
              " 'احب': <gensim.models.keyedvectors.Vocab at 0x7fa051163d10>,\n",
              " 'مصر': <gensim.models.keyedvectors.Vocab at 0x7fa051163d50>,\n",
              " 'وين': <gensim.models.keyedvectors.Vocab at 0x7fa051163d90>,\n",
              " 'عادي': <gensim.models.keyedvectors.Vocab at 0x7fa051163dd0>,\n",
              " 'تحب': <gensim.models.keyedvectors.Vocab at 0x7fa051163e10>,\n",
              " 'نحن': <gensim.models.keyedvectors.Vocab at 0x7fa051163e50>,\n",
              " 'اللهم_إني_أسألك': <gensim.models.keyedvectors.Vocab at 0x7fa051163e90>,\n",
              " 'زي': <gensim.models.keyedvectors.Vocab at 0x7fa051163ed0>,\n",
              " 'نفسه': <gensim.models.keyedvectors.Vocab at 0x7fa051163f10>,\n",
              " 'منهم': <gensim.models.keyedvectors.Vocab at 0x7fa051163f50>,\n",
              " 'حبيبي': <gensim.models.keyedvectors.Vocab at 0x7fa051163f90>,\n",
              " 'عمر': <gensim.models.keyedvectors.Vocab at 0x7fa051163fd0>,\n",
              " '4': <gensim.models.keyedvectors.Vocab at 0x7fa051167050>,\n",
              " 'ليش': <gensim.models.keyedvectors.Vocab at 0x7fa051167090>,\n",
              " 'قل': <gensim.models.keyedvectors.Vocab at 0x7fa0511670d0>,\n",
              " 'وإن': <gensim.models.keyedvectors.Vocab at 0x7fa051167110>,\n",
              " 'الان': <gensim.models.keyedvectors.Vocab at 0x7fa051167150>,\n",
              " 'كده': <gensim.models.keyedvectors.Vocab at 0x7fa051167190>,\n",
              " '_': <gensim.models.keyedvectors.Vocab at 0x7fa0511671d0>,\n",
              " 'أول': <gensim.models.keyedvectors.Vocab at 0x7fa051167210>,\n",
              " 'لأن': <gensim.models.keyedvectors.Vocab at 0x7fa051167250>,\n",
              " 'تعرف': <gensim.models.keyedvectors.Vocab at 0x7fa051167290>,\n",
              " 'وان': <gensim.models.keyedvectors.Vocab at 0x7fa0511672d0>,\n",
              " 'ال': <gensim.models.keyedvectors.Vocab at 0x7fa051167310>,\n",
              " 'لله': <gensim.models.keyedvectors.Vocab at 0x7fa051167350>,\n",
              " 'الآن': <gensim.models.keyedvectors.Vocab at 0x7fa051167390>,\n",
              " 'عليهم': <gensim.models.keyedvectors.Vocab at 0x7fa0511673d0>,\n",
              " 'قال_رسول': <gensim.models.keyedvectors.Vocab at 0x7fa051167410>,\n",
              " 'كلمة': <gensim.models.keyedvectors.Vocab at 0x7fa051167450>,\n",
              " 'ربنا': <gensim.models.keyedvectors.Vocab at 0x7fa051167490>,\n",
              " 'إني': <gensim.models.keyedvectors.Vocab at 0x7fa0511674d0>,\n",
              " 'مكان': <gensim.models.keyedvectors.Vocab at 0x7fa051167510>,\n",
              " 'عنك': <gensim.models.keyedvectors.Vocab at 0x7fa051167550>,\n",
              " 'بخير': <gensim.models.keyedvectors.Vocab at 0x7fa051167590>,\n",
              " 'ابي': <gensim.models.keyedvectors.Vocab at 0x7fa0511675d0>,\n",
              " '5': <gensim.models.keyedvectors.Vocab at 0x7fa051167610>,\n",
              " 'تويتر': <gensim.models.keyedvectors.Vocab at 0x7fa051167650>,\n",
              " 'اللى': <gensim.models.keyedvectors.Vocab at 0x7fa051167690>,\n",
              " 'عيني': <gensim.models.keyedvectors.Vocab at 0x7fa0511676d0>,\n",
              " 'صح': <gensim.models.keyedvectors.Vocab at 0x7fa051167710>,\n",
              " 'حسبي': <gensim.models.keyedvectors.Vocab at 0x7fa051167750>,\n",
              " 'مين': <gensim.models.keyedvectors.Vocab at 0x7fa051167790>,\n",
              " 'فعلا': <gensim.models.keyedvectors.Vocab at 0x7fa0511677d0>,\n",
              " 'حبك': <gensim.models.keyedvectors.Vocab at 0x7fa051167810>,\n",
              " 'وأنا': <gensim.models.keyedvectors.Vocab at 0x7fa051167850>,\n",
              " 'النصر': <gensim.models.keyedvectors.Vocab at 0x7fa051167890>,\n",
              " 'مهما': <gensim.models.keyedvectors.Vocab at 0x7fa0511678d0>,\n",
              " 'فلا': <gensim.models.keyedvectors.Vocab at 0x7fa051167910>,\n",
              " 'وأنت': <gensim.models.keyedvectors.Vocab at 0x7fa051167950>,\n",
              " 'خلاص': <gensim.models.keyedvectors.Vocab at 0x7fa051167990>,\n",
              " 'نبينا_محمد': <gensim.models.keyedvectors.Vocab at 0x7fa0511679d0>,\n",
              " 'الحياه': <gensim.models.keyedvectors.Vocab at 0x7fa051167a10>,\n",
              " 'فوق': <gensim.models.keyedvectors.Vocab at 0x7fa051167a50>,\n",
              " 'آخر': <gensim.models.keyedvectors.Vocab at 0x7fa051167a90>,\n",
              " 'احنا': <gensim.models.keyedvectors.Vocab at 0x7fa051167ad0>,\n",
              " 'أمي': <gensim.models.keyedvectors.Vocab at 0x7fa051167b10>,\n",
              " 'كبير': <gensim.models.keyedvectors.Vocab at 0x7fa051167b50>,\n",
              " 'يبقى': <gensim.models.keyedvectors.Vocab at 0x7fa051167b90>,\n",
              " 'إنك': <gensim.models.keyedvectors.Vocab at 0x7fa051167bd0>,\n",
              " 'متى': <gensim.models.keyedvectors.Vocab at 0x7fa051167c10>,\n",
              " 'كثيرا': <gensim.models.keyedvectors.Vocab at 0x7fa051167c50>,\n",
              " 'انها': <gensim.models.keyedvectors.Vocab at 0x7fa051167c90>,\n",
              " 'العمر': <gensim.models.keyedvectors.Vocab at 0x7fa051167cd0>,\n",
              " 'عندك': <gensim.models.keyedvectors.Vocab at 0x7fa051167d10>,\n",
              " 'السعودية': <gensim.models.keyedvectors.Vocab at 0x7fa051167d50>,\n",
              " 'وهي': <gensim.models.keyedvectors.Vocab at 0x7fa051167d90>,\n",
              " 'الحين': <gensim.models.keyedvectors.Vocab at 0x7fa051167dd0>,\n",
              " 'ﷺ': <gensim.models.keyedvectors.Vocab at 0x7fa051167e10>,\n",
              " 'رغم': <gensim.models.keyedvectors.Vocab at 0x7fa051167e50>,\n",
              " 'حال': <gensim.models.keyedvectors.Vocab at 0x7fa051167e90>,\n",
              " 'حيث': <gensim.models.keyedvectors.Vocab at 0x7fa051167ed0>,\n",
              " 'عاد': <gensim.models.keyedvectors.Vocab at 0x7fa051167f10>,\n",
              " 'كن': <gensim.models.keyedvectors.Vocab at 0x7fa051167f50>,\n",
              " 'الظالمين': <gensim.models.keyedvectors.Vocab at 0x7fa051167f90>,\n",
              " 'أفضل': <gensim.models.keyedvectors.Vocab at 0x7fa051167fd0>,\n",
              " 'عني': <gensim.models.keyedvectors.Vocab at 0x7fa05116c050>,\n",
              " '#': <gensim.models.keyedvectors.Vocab at 0x7fa05116c090>,\n",
              " 'عيونك': <gensim.models.keyedvectors.Vocab at 0x7fa05116c0d0>,\n",
              " 'ترا': <gensim.models.keyedvectors.Vocab at 0x7fa05116c110>,\n",
              " 'باقي': <gensim.models.keyedvectors.Vocab at 0x7fa05116c150>,\n",
              " 'اجعل': <gensim.models.keyedvectors.Vocab at 0x7fa05116c190>,\n",
              " 'مافي': <gensim.models.keyedvectors.Vocab at 0x7fa05116c1d0>,\n",
              " 'الحزن': <gensim.models.keyedvectors.Vocab at 0x7fa05116c210>,\n",
              " 'جميع': <gensim.models.keyedvectors.Vocab at 0x7fa05116c250>,\n",
              " 'يوما': <gensim.models.keyedvectors.Vocab at 0x7fa05116c290>,\n",
              " 'الجنه': <gensim.models.keyedvectors.Vocab at 0x7fa05116c2d0>,\n",
              " 'رتويت': <gensim.models.keyedvectors.Vocab at 0x7fa05116c310>,\n",
              " 'اخر': <gensim.models.keyedvectors.Vocab at 0x7fa05116c350>,\n",
              " 'حساب': <gensim.models.keyedvectors.Vocab at 0x7fa05116c390>,\n",
              " 'السعادة': <gensim.models.keyedvectors.Vocab at 0x7fa05116c3d0>,\n",
              " 'بنت': <gensim.models.keyedvectors.Vocab at 0x7fa05116c410>,\n",
              " 'النار': <gensim.models.keyedvectors.Vocab at 0x7fa05116c450>,\n",
              " 'بأن': <gensim.models.keyedvectors.Vocab at 0x7fa05116c490>,\n",
              " 'لان': <gensim.models.keyedvectors.Vocab at 0x7fa05116c4d0>,\n",
              " 'أنه': <gensim.models.keyedvectors.Vocab at 0x7fa05116c510>,\n",
              " 'ضد': <gensim.models.keyedvectors.Vocab at 0x7fa05116c550>,\n",
              " 'بلا': <gensim.models.keyedvectors.Vocab at 0x7fa05116c590>,\n",
              " 'معي': <gensim.models.keyedvectors.Vocab at 0x7fa05116c5d0>,\n",
              " 'دا': <gensim.models.keyedvectors.Vocab at 0x7fa05116c610>,\n",
              " 'البشر': <gensim.models.keyedvectors.Vocab at 0x7fa05116c650>,\n",
              " 'الموت': <gensim.models.keyedvectors.Vocab at 0x7fa05116c690>,\n",
              " 'تبقى': <gensim.models.keyedvectors.Vocab at 0x7fa05116c6d0>,\n",
              " 'الأرض': <gensim.models.keyedvectors.Vocab at 0x7fa05116c710>,\n",
              " 'الجميع': <gensim.models.keyedvectors.Vocab at 0x7fa05116c750>,\n",
              " '#السعودية': <gensim.models.keyedvectors.Vocab at 0x7fa05116c790>,\n",
              " 'حياة': <gensim.models.keyedvectors.Vocab at 0x7fa05116c7d0>,\n",
              " 'بعيد': <gensim.models.keyedvectors.Vocab at 0x7fa05116c810>,\n",
              " 'يجب': <gensim.models.keyedvectors.Vocab at 0x7fa05116c850>,\n",
              " 'الصلاة': <gensim.models.keyedvectors.Vocab at 0x7fa05116c890>,\n",
              " 'اقول': <gensim.models.keyedvectors.Vocab at 0x7fa05116c8d0>,\n",
              " 'ان_شاء': <gensim.models.keyedvectors.Vocab at 0x7fa05116c910>,\n",
              " 'الليل': <gensim.models.keyedvectors.Vocab at 0x7fa05116c950>,\n",
              " 'معاك': <gensim.models.keyedvectors.Vocab at 0x7fa05116c990>,\n",
              " 'جمال': <gensim.models.keyedvectors.Vocab at 0x7fa05116c9d0>,\n",
              " 'وليس': <gensim.models.keyedvectors.Vocab at 0x7fa05116ca10>,\n",
              " 'نعم': <gensim.models.keyedvectors.Vocab at 0x7fa05116ca50>,\n",
              " 'صوت': <gensim.models.keyedvectors.Vocab at 0x7fa05116ca90>,\n",
              " 'احسن': <gensim.models.keyedvectors.Vocab at 0x7fa05116cad0>,\n",
              " 'خلق': <gensim.models.keyedvectors.Vocab at 0x7fa05116cb10>,\n",
              " 'بما': <gensim.models.keyedvectors.Vocab at 0x7fa05116cb50>,\n",
              " 'امي': <gensim.models.keyedvectors.Vocab at 0x7fa05116cb90>,\n",
              " 'لآ': <gensim.models.keyedvectors.Vocab at 0x7fa05116cbd0>,\n",
              " 'الشوق': <gensim.models.keyedvectors.Vocab at 0x7fa05116cc10>,\n",
              " 'ماهو': <gensim.models.keyedvectors.Vocab at 0x7fa05116cc50>,\n",
              " 'بسبب': <gensim.models.keyedvectors.Vocab at 0x7fa05116cc90>,\n",
              " 'عيوني': <gensim.models.keyedvectors.Vocab at 0x7fa05116ccd0>,\n",
              " 'النوم': <gensim.models.keyedvectors.Vocab at 0x7fa05116cd10>,\n",
              " 'قلوبنا': <gensim.models.keyedvectors.Vocab at 0x7fa05116cd50>,\n",
              " 'رمضان': <gensim.models.keyedvectors.Vocab at 0x7fa05116cd90>,\n",
              " 'الكل': <gensim.models.keyedvectors.Vocab at 0x7fa05116cdd0>,\n",
              " 'بإذن': <gensim.models.keyedvectors.Vocab at 0x7fa05116ce10>,\n",
              " 'صور': <gensim.models.keyedvectors.Vocab at 0x7fa05116ce50>,\n",
              " 'قول': <gensim.models.keyedvectors.Vocab at 0x7fa05116ce90>,\n",
              " 'طول': <gensim.models.keyedvectors.Vocab at 0x7fa05116ced0>,\n",
              " 'لك_الحمد': <gensim.models.keyedvectors.Vocab at 0x7fa05116cf10>,\n",
              " 'أنك': <gensim.models.keyedvectors.Vocab at 0x7fa05116cf50>,\n",
              " 'افضل': <gensim.models.keyedvectors.Vocab at 0x7fa05116cf90>,\n",
              " 'اللهم_إني_أعوذ': <gensim.models.keyedvectors.Vocab at 0x7fa05116cfd0>,\n",
              " 'إليك': <gensim.models.keyedvectors.Vocab at 0x7fa051172050>,\n",
              " 'النبي': <gensim.models.keyedvectors.Vocab at 0x7fa051172090>,\n",
              " 'يعرف': <gensim.models.keyedvectors.Vocab at 0x7fa0511720d0>,\n",
              " 'معه': <gensim.models.keyedvectors.Vocab at 0x7fa051172110>,\n",
              " 'رحمه': <gensim.models.keyedvectors.Vocab at 0x7fa051172150>,\n",
              " 'بسم': <gensim.models.keyedvectors.Vocab at 0x7fa051172190>,\n",
              " 'رجل': <gensim.models.keyedvectors.Vocab at 0x7fa0511721d0>,\n",
              " 'صورة': <gensim.models.keyedvectors.Vocab at 0x7fa051172210>,\n",
              " 'عدد': <gensim.models.keyedvectors.Vocab at 0x7fa051172250>,\n",
              " 'روح': <gensim.models.keyedvectors.Vocab at 0x7fa051172290>,\n",
              " 'بي': <gensim.models.keyedvectors.Vocab at 0x7fa0511722d0>,\n",
              " 'ذا': <gensim.models.keyedvectors.Vocab at 0x7fa051172310>,\n",
              " 'فيديو': <gensim.models.keyedvectors.Vocab at 0x7fa051172350>,\n",
              " 'اله_الا': <gensim.models.keyedvectors.Vocab at 0x7fa051172390>,\n",
              " 'نور': <gensim.models.keyedvectors.Vocab at 0x7fa0511723d0>,\n",
              " 'السماء': <gensim.models.keyedvectors.Vocab at 0x7fa051172410>,\n",
              " 'عام': <gensim.models.keyedvectors.Vocab at 0x7fa051172450>,\n",
              " 'وجه': <gensim.models.keyedvectors.Vocab at 0x7fa051172490>,\n",
              " 'السلام': <gensim.models.keyedvectors.Vocab at 0x7fa0511724d0>,\n",
              " 'تعلم': <gensim.models.keyedvectors.Vocab at 0x7fa051172510>,\n",
              " 'مرة': <gensim.models.keyedvectors.Vocab at 0x7fa051172550>,\n",
              " 'علشان': <gensim.models.keyedvectors.Vocab at 0x7fa051172590>,\n",
              " 'وهذا': <gensim.models.keyedvectors.Vocab at 0x7fa0511725d0>,\n",
              " 'الدعاء': <gensim.models.keyedvectors.Vocab at 0x7fa051172610>,\n",
              " 'شفت': <gensim.models.keyedvectors.Vocab at 0x7fa051172650>,\n",
              " 'اللهم_ارحم': <gensim.models.keyedvectors.Vocab at 0x7fa051172690>,\n",
              " 'آمين': <gensim.models.keyedvectors.Vocab at 0x7fa0511726d0>,\n",
              " 'روحي': <gensim.models.keyedvectors.Vocab at 0x7fa051172710>,\n",
              " 'اصلا': <gensim.models.keyedvectors.Vocab at 0x7fa051172750>,\n",
              " 'ام': <gensim.models.keyedvectors.Vocab at 0x7fa051172790>,\n",
              " 'القلوب': <gensim.models.keyedvectors.Vocab at 0x7fa0511727d0>,\n",
              " 'فإن': <gensim.models.keyedvectors.Vocab at 0x7fa051172810>,\n",
              " 'احلى': <gensim.models.keyedvectors.Vocab at 0x7fa051172850>,\n",
              " 'حول_ولا': <gensim.models.keyedvectors.Vocab at 0x7fa051172890>,\n",
              " 'تم': <gensim.models.keyedvectors.Vocab at 0x7fa0511728d0>,\n",
              " 'خيرا': <gensim.models.keyedvectors.Vocab at 0x7fa051172910>,\n",
              " 'اما': <gensim.models.keyedvectors.Vocab at 0x7fa051172950>,\n",
              " 'طريق': <gensim.models.keyedvectors.Vocab at 0x7fa051172990>,\n",
              " 'صوتك': <gensim.models.keyedvectors.Vocab at 0x7fa0511729d0>,\n",
              " 'غدا': <gensim.models.keyedvectors.Vocab at 0x7fa051172a10>,\n",
              " 'أبدا': <gensim.models.keyedvectors.Vocab at 0x7fa051172a50>,\n",
              " '6': <gensim.models.keyedvectors.Vocab at 0x7fa051172a90>,\n",
              " 'أم': <gensim.models.keyedvectors.Vocab at 0x7fa051172ad0>,\n",
              " 'عنده': <gensim.models.keyedvectors.Vocab at 0x7fa051172b10>,\n",
              " 'العرب': <gensim.models.keyedvectors.Vocab at 0x7fa051172b50>,\n",
              " 'اشوف': <gensim.models.keyedvectors.Vocab at 0x7fa051172b90>,\n",
              " 'دايم': <gensim.models.keyedvectors.Vocab at 0x7fa051172bd0>,\n",
              " 'فقد': <gensim.models.keyedvectors.Vocab at 0x7fa051172c10>,\n",
              " 'حلوه': <gensim.models.keyedvectors.Vocab at 0x7fa051172c50>,\n",
              " 'الكثير': <gensim.models.keyedvectors.Vocab at 0x7fa051172c90>,\n",
              " 'الاهلي': <gensim.models.keyedvectors.Vocab at 0x7fa051172cd0>,\n",
              " 'ء': <gensim.models.keyedvectors.Vocab at 0x7fa051172d10>,\n",
              " 'سبب': <gensim.models.keyedvectors.Vocab at 0x7fa051172d50>,\n",
              " 'يقولون': <gensim.models.keyedvectors.Vocab at 0x7fa051172d90>,\n",
              " 'اعرف': <gensim.models.keyedvectors.Vocab at 0x7fa051172dd0>,\n",
              " 'يحب': <gensim.models.keyedvectors.Vocab at 0x7fa051172e10>,\n",
              " 'بقى': <gensim.models.keyedvectors.Vocab at 0x7fa051172e50>,\n",
              " 'المسلمين': <gensim.models.keyedvectors.Vocab at 0x7fa051172e90>,\n",
              " 'سنة': <gensim.models.keyedvectors.Vocab at 0x7fa051172ed0>,\n",
              " 'الشباب': <gensim.models.keyedvectors.Vocab at 0x7fa051172f10>,\n",
              " 'جميلة': <gensim.models.keyedvectors.Vocab at 0x7fa051172f50>,\n",
              " 'مساء': <gensim.models.keyedvectors.Vocab at 0x7fa051172f90>,\n",
              " '10': <gensim.models.keyedvectors.Vocab at 0x7fa051178050>,\n",
              " 'صحيح': <gensim.models.keyedvectors.Vocab at 0x7fa051178090>,\n",
              " 'يجي': <gensim.models.keyedvectors.Vocab at 0x7fa0511780d0>,\n",
              " 'حول': <gensim.models.keyedvectors.Vocab at 0x7fa051178110>,\n",
              " 'زمان': <gensim.models.keyedvectors.Vocab at 0x7fa051178150>,\n",
              " 'تدري': <gensim.models.keyedvectors.Vocab at 0x7fa051178190>,\n",
              " 'اتمنى': <gensim.models.keyedvectors.Vocab at 0x7fa0511781d0>,\n",
              " 'الاتحاد': <gensim.models.keyedvectors.Vocab at 0x7fa051178210>,\n",
              " 'دايما': <gensim.models.keyedvectors.Vocab at 0x7fa051178250>,\n",
              " 'سبحانك': <gensim.models.keyedvectors.Vocab at 0x7fa051178290>,\n",
              " 'واللي': <gensim.models.keyedvectors.Vocab at 0x7fa0511782d0>,\n",
              " 'يلا': <gensim.models.keyedvectors.Vocab at 0x7fa051178310>,\n",
              " 'سوى': <gensim.models.keyedvectors.Vocab at 0x7fa051178350>,\n",
              " 'يحبك': <gensim.models.keyedvectors.Vocab at 0x7fa051178390>,\n",
              " 'شئ': <gensim.models.keyedvectors.Vocab at 0x7fa0511783d0>,\n",
              " 'رسول': <gensim.models.keyedvectors.Vocab at 0x7fa051178410>,\n",
              " 'شيئا': <gensim.models.keyedvectors.Vocab at 0x7fa051178450>,\n",
              " 'حبيبتي': <gensim.models.keyedvectors.Vocab at 0x7fa051178490>,\n",
              " 'الدين': <gensim.models.keyedvectors.Vocab at 0x7fa0511784d0>,\n",
              " 'فيهم': <gensim.models.keyedvectors.Vocab at 0x7fa051178510>,\n",
              " 'ثاني': <gensim.models.keyedvectors.Vocab at 0x7fa051178550>,\n",
              " 'إليه': <gensim.models.keyedvectors.Vocab at 0x7fa051178590>,\n",
              " 'شاء': <gensim.models.keyedvectors.Vocab at 0x7fa0511785d0>,\n",
              " 'صدري': <gensim.models.keyedvectors.Vocab at 0x7fa051178610>,\n",
              " 'لماذا': <gensim.models.keyedvectors.Vocab at 0x7fa051178650>,\n",
              " 'ذكر': <gensim.models.keyedvectors.Vocab at 0x7fa051178690>,\n",
              " 'قريب': <gensim.models.keyedvectors.Vocab at 0x7fa0511786d0>,\n",
              " 'عمل': <gensim.models.keyedvectors.Vocab at 0x7fa051178710>,\n",
              " 'ليت': <gensim.models.keyedvectors.Vocab at 0x7fa051178750>,\n",
              " 'العظيم_وأتوب_إليه': <gensim.models.keyedvectors.Vocab at 0x7fa051178790>,\n",
              " 'اكيد': <gensim.models.keyedvectors.Vocab at 0x7fa0511787d0>,\n",
              " 'كل_شيء_قدير': <gensim.models.keyedvectors.Vocab at 0x7fa051178810>,\n",
              " 'تعالى': <gensim.models.keyedvectors.Vocab at 0x7fa051178850>,\n",
              " 'قوة_إلا': <gensim.models.keyedvectors.Vocab at 0x7fa051178890>,\n",
              " 'عسى': <gensim.models.keyedvectors.Vocab at 0x7fa0511788d0>,\n",
              " 'الرجل': <gensim.models.keyedvectors.Vocab at 0x7fa051178910>,\n",
              " 'شوي': <gensim.models.keyedvectors.Vocab at 0x7fa051178950>,\n",
              " 'ليست': <gensim.models.keyedvectors.Vocab at 0x7fa051178990>,\n",
              " '#الهلال': <gensim.models.keyedvectors.Vocab at 0x7fa0511789d0>,\n",
              " 'جعل': <gensim.models.keyedvectors.Vocab at 0x7fa051178a10>,\n",
              " 'مباراة': <gensim.models.keyedvectors.Vocab at 0x7fa051178a50>,\n",
              " 'خلال': <gensim.models.keyedvectors.Vocab at 0x7fa051178a90>,\n",
              " 'قلبه': <gensim.models.keyedvectors.Vocab at 0x7fa051178ad0>,\n",
              " 'منا': <gensim.models.keyedvectors.Vocab at 0x7fa051178b10>,\n",
              " 'وعلى': <gensim.models.keyedvectors.Vocab at 0x7fa051178b50>,\n",
              " 'كمان': <gensim.models.keyedvectors.Vocab at 0x7fa051178b90>,\n",
              " 'مساء_الخير': <gensim.models.keyedvectors.Vocab at 0x7fa051178bd0>,\n",
              " 'الروح': <gensim.models.keyedvectors.Vocab at 0x7fa051178c10>,\n",
              " 'الوطن': <gensim.models.keyedvectors.Vocab at 0x7fa051178c50>,\n",
              " 'طبعا': <gensim.models.keyedvectors.Vocab at 0x7fa051178c90>,\n",
              " 'وهم': <gensim.models.keyedvectors.Vocab at 0x7fa051178cd0>,\n",
              " 'لين': <gensim.models.keyedvectors.Vocab at 0x7fa051178d10>,\n",
              " 'الصباح': <gensim.models.keyedvectors.Vocab at 0x7fa051178d50>,\n",
              " 'اكتر': <gensim.models.keyedvectors.Vocab at 0x7fa051178d90>,\n",
              " 'الصبر': <gensim.models.keyedvectors.Vocab at 0x7fa051178dd0>,\n",
              " 'العين': <gensim.models.keyedvectors.Vocab at 0x7fa051178e10>,\n",
              " 'هدف': <gensim.models.keyedvectors.Vocab at 0x7fa051178e50>,\n",
              " 'حي': <gensim.models.keyedvectors.Vocab at 0x7fa051178e90>,\n",
              " 'ودي': <gensim.models.keyedvectors.Vocab at 0x7fa051178ed0>,\n",
              " 'ك': <gensim.models.keyedvectors.Vocab at 0x7fa051178f10>,\n",
              " 'فهو': <gensim.models.keyedvectors.Vocab at 0x7fa051178f50>,\n",
              " 'يصير': <gensim.models.keyedvectors.Vocab at 0x7fa051178f90>,\n",
              " 'كلهم': <gensim.models.keyedvectors.Vocab at 0x7fa051178fd0>,\n",
              " 'عنا': <gensim.models.keyedvectors.Vocab at 0x7fa05117c050>,\n",
              " 'الكويت': <gensim.models.keyedvectors.Vocab at 0x7fa05117c090>,\n",
              " 'وجهك': <gensim.models.keyedvectors.Vocab at 0x7fa05117c0d0>,\n",
              " 'ابدا': <gensim.models.keyedvectors.Vocab at 0x7fa05117c110>,\n",
              " 'عليكم': <gensim.models.keyedvectors.Vocab at 0x7fa05117c150>,\n",
              " 'كتير': <gensim.models.keyedvectors.Vocab at 0x7fa05117c190>,\n",
              " 'احس': <gensim.models.keyedvectors.Vocab at 0x7fa05117c1d0>,\n",
              " 'داخل': <gensim.models.keyedvectors.Vocab at 0x7fa05117c210>,\n",
              " 'الفرح': <gensim.models.keyedvectors.Vocab at 0x7fa05117c250>,\n",
              " 'أحيانا': <gensim.models.keyedvectors.Vocab at 0x7fa05117c290>,\n",
              " 'عنها': <gensim.models.keyedvectors.Vocab at 0x7fa05117c2d0>,\n",
              " 'المهم': <gensim.models.keyedvectors.Vocab at 0x7fa05117c310>,\n",
              " 'غيره': <gensim.models.keyedvectors.Vocab at 0x7fa05117c350>,\n",
              " 'الحق': <gensim.models.keyedvectors.Vocab at 0x7fa05117c390>,\n",
              " 'لاعب': <gensim.models.keyedvectors.Vocab at 0x7fa05117c3d0>,\n",
              " 'الإنسان': <gensim.models.keyedvectors.Vocab at 0x7fa05117c410>,\n",
              " 'يوجد': <gensim.models.keyedvectors.Vocab at 0x7fa05117c450>,\n",
              " 'انسان': <gensim.models.keyedvectors.Vocab at 0x7fa05117c490>,\n",
              " '7': <gensim.models.keyedvectors.Vocab at 0x7fa05117c4d0>,\n",
              " 'حزن': <gensim.models.keyedvectors.Vocab at 0x7fa05117c510>,\n",
              " 'الواحد': <gensim.models.keyedvectors.Vocab at 0x7fa05117c550>,\n",
              " 'وأن': <gensim.models.keyedvectors.Vocab at 0x7fa05117c590>,\n",
              " 'يعلم': <gensim.models.keyedvectors.Vocab at 0x7fa05117c5d0>,\n",
              " 'ريتويت': <gensim.models.keyedvectors.Vocab at 0x7fa05117c610>,\n",
              " 'بعدك': <gensim.models.keyedvectors.Vocab at 0x7fa05117c650>,\n",
              " 'البنت': <gensim.models.keyedvectors.Vocab at 0x7fa05117c690>,\n",
              " 'دعاء': <gensim.models.keyedvectors.Vocab at 0x7fa05117c6d0>,\n",
              " 'عين': <gensim.models.keyedvectors.Vocab at 0x7fa05117c710>,\n",
              " 'مجرد': <gensim.models.keyedvectors.Vocab at 0x7fa05117c750>,\n",
              " 'عندنا': <gensim.models.keyedvectors.Vocab at 0x7fa05117c790>,\n",
              " 'مآ': <gensim.models.keyedvectors.Vocab at 0x7fa05117c7d0>,\n",
              " '#StaracArabia': <gensim.models.keyedvectors.Vocab at 0x7fa05117c850>,\n",
              " 'فريق': <gensim.models.keyedvectors.Vocab at 0x7fa05117c890>,\n",
              " 'كلنا': <gensim.models.keyedvectors.Vocab at 0x7fa05117c8d0>,\n",
              " 'بقي': <gensim.models.keyedvectors.Vocab at 0x7fa05117c910>,\n",
              " 'القرآن': <gensim.models.keyedvectors.Vocab at 0x7fa05117c950>,\n",
              " 'لحظة': <gensim.models.keyedvectors.Vocab at 0x7fa05117c990>,\n",
              " 'غيابك': <gensim.models.keyedvectors.Vocab at 0x7fa05117c9d0>,\n",
              " 'ألف': <gensim.models.keyedvectors.Vocab at 0x7fa05117ca10>,\n",
              " 'صاحب': <gensim.models.keyedvectors.Vocab at 0x7fa05117ca50>,\n",
              " 'أبي': <gensim.models.keyedvectors.Vocab at 0x7fa05117ca90>,\n",
              " 'وملائكته_يصلون': <gensim.models.keyedvectors.Vocab at 0x7fa05117cad0>,\n",
              " 'مافيه': <gensim.models.keyedvectors.Vocab at 0x7fa05117cb10>,\n",
              " 'مبروك': <gensim.models.keyedvectors.Vocab at 0x7fa05117cb50>,\n",
              " 'الرجال': <gensim.models.keyedvectors.Vocab at 0x7fa05117cb90>,\n",
              " 'الجمال': <gensim.models.keyedvectors.Vocab at 0x7fa05117cbd0>,\n",
              " 'إنه': <gensim.models.keyedvectors.Vocab at 0x7fa05117cc10>,\n",
              " 'أهل': <gensim.models.keyedvectors.Vocab at 0x7fa05117cc50>,\n",
              " 'جميله': <gensim.models.keyedvectors.Vocab at 0x7fa05117cc90>,\n",
              " 'بعده': <gensim.models.keyedvectors.Vocab at 0x7fa05117ccd0>,\n",
              " 'صدق': <gensim.models.keyedvectors.Vocab at 0x7fa05117cd10>,\n",
              " '0': <gensim.models.keyedvectors.Vocab at 0x7fa05117cd50>,\n",
              " 'الشيخ': <gensim.models.keyedvectors.Vocab at 0x7fa05117cd90>,\n",
              " 'النفس': <gensim.models.keyedvectors.Vocab at 0x7fa05117cdd0>,\n",
              " 'باب': <gensim.models.keyedvectors.Vocab at 0x7fa05117ce10>,\n",
              " 'بكره': <gensim.models.keyedvectors.Vocab at 0x7fa05117ce50>,\n",
              " 'يريد': <gensim.models.keyedvectors.Vocab at 0x7fa05117ce90>,\n",
              " 'اجل': <gensim.models.keyedvectors.Vocab at 0x7fa05117ced0>,\n",
              " 'سوريا': <gensim.models.keyedvectors.Vocab at 0x7fa05117cf10>,\n",
              " 'زين': <gensim.models.keyedvectors.Vocab at 0x7fa05117cf50>,\n",
              " 'حبيب': <gensim.models.keyedvectors.Vocab at 0x7fa05117cf90>,\n",
              " 'صرت': <gensim.models.keyedvectors.Vocab at 0x7fa05117cfd0>,\n",
              " '2016': <gensim.models.keyedvectors.Vocab at 0x7fa051182090>,\n",
              " 'تجعل': <gensim.models.keyedvectors.Vocab at 0x7fa0511820d0>,\n",
              " 'دام': <gensim.models.keyedvectors.Vocab at 0x7fa051182110>,\n",
              " 'رجال': <gensim.models.keyedvectors.Vocab at 0x7fa051182150>,\n",
              " 'البعض': <gensim.models.keyedvectors.Vocab at 0x7fa051182190>,\n",
              " 'مات': <gensim.models.keyedvectors.Vocab at 0x7fa0511821d0>,\n",
              " 'كدا': <gensim.models.keyedvectors.Vocab at 0x7fa051182210>,\n",
              " 'وسط': <gensim.models.keyedvectors.Vocab at 0x7fa051182250>,\n",
              " 'تحت': <gensim.models.keyedvectors.Vocab at 0x7fa051182290>,\n",
              " 'أني': <gensim.models.keyedvectors.Vocab at 0x7fa0511822d0>,\n",
              " 'صعب': <gensim.models.keyedvectors.Vocab at 0x7fa051182310>,\n",
              " 'الطيب': <gensim.models.keyedvectors.Vocab at 0x7fa051182350>,\n",
              " 'يأتي': <gensim.models.keyedvectors.Vocab at 0x7fa051182390>,\n",
              " 'ولم': <gensim.models.keyedvectors.Vocab at 0x7fa0511823d0>,\n",
              " 'فينا': <gensim.models.keyedvectors.Vocab at 0x7fa051182410>,\n",
              " 'والحمد_لله': <gensim.models.keyedvectors.Vocab at 0x7fa051182450>,\n",
              " 'يحدث': <gensim.models.keyedvectors.Vocab at 0x7fa051182490>,\n",
              " 'لحد': <gensim.models.keyedvectors.Vocab at 0x7fa0511824d0>,\n",
              " 'كريم': <gensim.models.keyedvectors.Vocab at 0x7fa051182510>,\n",
              " 'رحمة': <gensim.models.keyedvectors.Vocab at 0x7fa051182550>,\n",
              " 'الف': <gensim.models.keyedvectors.Vocab at 0x7fa051182590>,\n",
              " 'ايام': <gensim.models.keyedvectors.Vocab at 0x7fa0511825d0>,\n",
              " 'الرياض': <gensim.models.keyedvectors.Vocab at 0x7fa051182610>,\n",
              " 'عظيم': <gensim.models.keyedvectors.Vocab at 0x7fa051182650>,\n",
              " 'حتي': <gensim.models.keyedvectors.Vocab at 0x7fa051182690>,\n",
              " 'اه': <gensim.models.keyedvectors.Vocab at 0x7fa0511826d0>,\n",
              " 'الشعور': <gensim.models.keyedvectors.Vocab at 0x7fa051182710>,\n",
              " 'مدري': <gensim.models.keyedvectors.Vocab at 0x7fa051182750>,\n",
              " 'كتاب': <gensim.models.keyedvectors.Vocab at 0x7fa051182790>,\n",
              " 'الزمن': <gensim.models.keyedvectors.Vocab at 0x7fa0511827d0>,\n",
              " 'قالت': <gensim.models.keyedvectors.Vocab at 0x7fa051182810>,\n",
              " 'سلام': <gensim.models.keyedvectors.Vocab at 0x7fa051182850>,\n",
              " 'يكتب': <gensim.models.keyedvectors.Vocab at 0x7fa051182890>,\n",
              " 'العمل': <gensim.models.keyedvectors.Vocab at 0x7fa0511828d0>,\n",
              " 'داعش': <gensim.models.keyedvectors.Vocab at 0x7fa051182910>,\n",
              " 'مستحيل': <gensim.models.keyedvectors.Vocab at 0x7fa051182950>,\n",
              " 'إني_كنت': <gensim.models.keyedvectors.Vocab at 0x7fa051182990>,\n",
              " 'الصمت': <gensim.models.keyedvectors.Vocab at 0x7fa0511829d0>,\n",
              " 'يستحق': <gensim.models.keyedvectors.Vocab at 0x7fa051182a10>,\n",
              " 'إله_إلا_أنت_سبحانك': <gensim.models.keyedvectors.Vocab at 0x7fa051182a50>,\n",
              " 'صباحكم': <gensim.models.keyedvectors.Vocab at 0x7fa051182a90>,\n",
              " 'ابو': <gensim.models.keyedvectors.Vocab at 0x7fa051182ad0>,\n",
              " 'فرح': <gensim.models.keyedvectors.Vocab at 0x7fa051182b10>,\n",
              " 'ابن': <gensim.models.keyedvectors.Vocab at 0x7fa051182b50>,\n",
              " 'تبي': <gensim.models.keyedvectors.Vocab at 0x7fa051182b90>,\n",
              " 'أجل': <gensim.models.keyedvectors.Vocab at 0x7fa051182bd0>,\n",
              " 'حرام': <gensim.models.keyedvectors.Vocab at 0x7fa051182c10>,\n",
              " 'الارض': <gensim.models.keyedvectors.Vocab at 0x7fa051182c50>,\n",
              " 'يسعدك': <gensim.models.keyedvectors.Vocab at 0x7fa051182c90>,\n",
              " 'ياخي': <gensim.models.keyedvectors.Vocab at 0x7fa051182cd0>,\n",
              " 'استغفرالله': <gensim.models.keyedvectors.Vocab at 0x7fa051182d10>,\n",
              " 'والله_أكبر': <gensim.models.keyedvectors.Vocab at 0x7fa051182d50>,\n",
              " 'شاهد': <gensim.models.keyedvectors.Vocab at 0x7fa051182d90>,\n",
              " 'يستاهل': <gensim.models.keyedvectors.Vocab at 0x7fa051182dd0>,\n",
              " 'العراق': <gensim.models.keyedvectors.Vocab at 0x7fa051182e10>,\n",
              " 'والا': <gensim.models.keyedvectors.Vocab at 0x7fa051182e50>,\n",
              " '#صباح_الخير': <gensim.models.keyedvectors.Vocab at 0x7fa051182e90>,\n",
              " 'تستاهل': <gensim.models.keyedvectors.Vocab at 0x7fa051182ed0>,\n",
              " '#النصر': <gensim.models.keyedvectors.Vocab at 0x7fa051182f10>,\n",
              " 'مر': <gensim.models.keyedvectors.Vocab at 0x7fa051182f50>,\n",
              " 'البيت': <gensim.models.keyedvectors.Vocab at 0x7fa051182f90>,\n",
              " 'أما': <gensim.models.keyedvectors.Vocab at 0x7fa051182fd0>,\n",
              " 'بنات': <gensim.models.keyedvectors.Vocab at 0x7fa051188050>,\n",
              " 'اليمن': <gensim.models.keyedvectors.Vocab at 0x7fa051188090>,\n",
              " 'الايام': <gensim.models.keyedvectors.Vocab at 0x7fa0511880d0>,\n",
              " 'الشعب': <gensim.models.keyedvectors.Vocab at 0x7fa051188110>,\n",
              " '8': <gensim.models.keyedvectors.Vocab at 0x7fa051188150>,\n",
              " 'لانه': <gensim.models.keyedvectors.Vocab at 0x7fa051188190>,\n",
              " 'محد': <gensim.models.keyedvectors.Vocab at 0x7fa0511881d0>,\n",
              " 'شريك_له_له': <gensim.models.keyedvectors.Vocab at 0x7fa051188210>,\n",
              " 'الصبح': <gensim.models.keyedvectors.Vocab at 0x7fa051188250>,\n",
              " 'الثاني': <gensim.models.keyedvectors.Vocab at 0x7fa051188290>,\n",
              " 'سنه': <gensim.models.keyedvectors.Vocab at 0x7fa0511882d0>,\n",
              " 'تلك': <gensim.models.keyedvectors.Vocab at 0x7fa051188310>,\n",
              " 'قالوا': <gensim.models.keyedvectors.Vocab at 0x7fa051188350>,\n",
              " 'د': <gensim.models.keyedvectors.Vocab at 0x7fa051188390>,\n",
              " 'قلوب': <gensim.models.keyedvectors.Vocab at 0x7fa0511883d0>,\n",
              " 'حبي': <gensim.models.keyedvectors.Vocab at 0x7fa051188410>,\n",
              " 'موجود': <gensim.models.keyedvectors.Vocab at 0x7fa051188450>,\n",
              " 'الاول': <gensim.models.keyedvectors.Vocab at 0x7fa051188490>,\n",
              " 'البنات': <gensim.models.keyedvectors.Vocab at 0x7fa0511884d0>,\n",
              " 'بالي': <gensim.models.keyedvectors.Vocab at 0x7fa051188510>,\n",
              " 'معنى': <gensim.models.keyedvectors.Vocab at 0x7fa051188550>,\n",
              " 'لأنه': <gensim.models.keyedvectors.Vocab at 0x7fa051188590>,\n",
              " 'ونعم_الوكيل': <gensim.models.keyedvectors.Vocab at 0x7fa0511885d0>,\n",
              " 'ماذا': <gensim.models.keyedvectors.Vocab at 0x7fa051188610>,\n",
              " 'بجد': <gensim.models.keyedvectors.Vocab at 0x7fa051188650>,\n",
              " 'طب': <gensim.models.keyedvectors.Vocab at 0x7fa051188690>,\n",
              " 'قدر': <gensim.models.keyedvectors.Vocab at 0x7fa0511886d0>,\n",
              " 'برنامج': <gensim.models.keyedvectors.Vocab at 0x7fa051188710>,\n",
              " 'تحس': <gensim.models.keyedvectors.Vocab at 0x7fa051188750>,\n",
              " 'يبي': <gensim.models.keyedvectors.Vocab at 0x7fa051188790>,\n",
              " 'تنام': <gensim.models.keyedvectors.Vocab at 0x7fa0511887d0>,\n",
              " 'بعدها': <gensim.models.keyedvectors.Vocab at 0x7fa051188810>,\n",
              " 'يا_أيها_الذين': <gensim.models.keyedvectors.Vocab at 0x7fa051188850>,\n",
              " 'هكذا': <gensim.models.keyedvectors.Vocab at 0x7fa051188890>,\n",
              " 'وقد': <gensim.models.keyedvectors.Vocab at 0x7fa0511888d0>,\n",
              " 'عاجل': <gensim.models.keyedvectors.Vocab at 0x7fa051188910>,\n",
              " 'مليون': <gensim.models.keyedvectors.Vocab at 0x7fa051188950>,\n",
              " 'جميعا': <gensim.models.keyedvectors.Vocab at 0x7fa051188990>,\n",
              " 'المرأة': <gensim.models.keyedvectors.Vocab at 0x7fa0511889d0>,\n",
              " 'جد': <gensim.models.keyedvectors.Vocab at 0x7fa051188a10>,\n",
              " 'تريد': <gensim.models.keyedvectors.Vocab at 0x7fa051188a50>,\n",
              " 'حلم': <gensim.models.keyedvectors.Vocab at 0x7fa051188a90>,\n",
              " 'كتب': <gensim.models.keyedvectors.Vocab at 0x7fa051188ad0>,\n",
              " 'فكرة': <gensim.models.keyedvectors.Vocab at 0x7fa051188b10>,\n",
              " 'ٰ': <gensim.models.keyedvectors.Vocab at 0x7fa051188b50>,\n",
              " 'المفروض': <gensim.models.keyedvectors.Vocab at 0x7fa051188b90>,\n",
              " 'يموت': <gensim.models.keyedvectors.Vocab at 0x7fa051188bd0>,\n",
              " 'حلوة': <gensim.models.keyedvectors.Vocab at 0x7fa051188c10>,\n",
              " 'الأيام': <gensim.models.keyedvectors.Vocab at 0x7fa051188c50>,\n",
              " 'بقلبي': <gensim.models.keyedvectors.Vocab at 0x7fa051188c90>,\n",
              " 'الذين': <gensim.models.keyedvectors.Vocab at 0x7fa051188cd0>,\n",
              " 'نعمة': <gensim.models.keyedvectors.Vocab at 0x7fa051188d10>,\n",
              " 'البعد': <gensim.models.keyedvectors.Vocab at 0x7fa051188d50>,\n",
              " 'غيري': <gensim.models.keyedvectors.Vocab at 0x7fa051188d90>,\n",
              " 'عارف': <gensim.models.keyedvectors.Vocab at 0x7fa051188dd0>,\n",
              " 'غلط': <gensim.models.keyedvectors.Vocab at 0x7fa051188e10>,\n",
              " 'ربما': <gensim.models.keyedvectors.Vocab at 0x7fa051188e50>,\n",
              " 'اسمه': <gensim.models.keyedvectors.Vocab at 0x7fa051188e90>,\n",
              " 'الفريق': <gensim.models.keyedvectors.Vocab at 0x7fa051188ed0>,\n",
              " 'ربك': <gensim.models.keyedvectors.Vocab at 0x7fa051188f10>,\n",
              " 'اسم': <gensim.models.keyedvectors.Vocab at 0x7fa051188f50>,\n",
              " 'إنسان': <gensim.models.keyedvectors.Vocab at 0x7fa051188f90>,\n",
              " 'تذكر': <gensim.models.keyedvectors.Vocab at 0x7fa051188fd0>,\n",
              " 'الدوري': <gensim.models.keyedvectors.Vocab at 0x7fa05118e050>,\n",
              " 'علم': <gensim.models.keyedvectors.Vocab at 0x7fa05118e090>,\n",
              " 'إنت': <gensim.models.keyedvectors.Vocab at 0x7fa05118e0d0>,\n",
              " 'رسالة': <gensim.models.keyedvectors.Vocab at 0x7fa05118e110>,\n",
              " 'خاص': <gensim.models.keyedvectors.Vocab at 0x7fa05118e150>,\n",
              " 'عيون': <gensim.models.keyedvectors.Vocab at 0x7fa05118e190>,\n",
              " 'سعادة': <gensim.models.keyedvectors.Vocab at 0x7fa05118e1d0>,\n",
              " 'وبارك': <gensim.models.keyedvectors.Vocab at 0x7fa05118e210>,\n",
              " 'أعلم': <gensim.models.keyedvectors.Vocab at 0x7fa05118e250>,\n",
              " 'الجميل': <gensim.models.keyedvectors.Vocab at 0x7fa05118e290>,\n",
              " 'كي': <gensim.models.keyedvectors.Vocab at 0x7fa05118e2d0>,\n",
              " 'تعيش': <gensim.models.keyedvectors.Vocab at 0x7fa05118e310>,\n",
              " 'نفسه_وزنة_عرشه_ومداد': <gensim.models.keyedvectors.Vocab at 0x7fa05118e350>,\n",
              " 'نهاية': <gensim.models.keyedvectors.Vocab at 0x7fa05118e390>,\n",
              " 'يكفي': <gensim.models.keyedvectors.Vocab at 0x7fa05118e3d0>,\n",
              " 'اكون': <gensim.models.keyedvectors.Vocab at 0x7fa05118e410>,\n",
              " 'رحم': <gensim.models.keyedvectors.Vocab at 0x7fa05118e450>,\n",
              " 'بداية': <gensim.models.keyedvectors.Vocab at 0x7fa05118e490>,\n",
              " 'الطريق': <gensim.models.keyedvectors.Vocab at 0x7fa05118e4d0>,\n",
              " 'عيب': <gensim.models.keyedvectors.Vocab at 0x7fa05118e510>,\n",
              " 'سوف': <gensim.models.keyedvectors.Vocab at 0x7fa05118e550>,\n",
              " 'غالي': <gensim.models.keyedvectors.Vocab at 0x7fa05118e590>,\n",
              " 'ذي': <gensim.models.keyedvectors.Vocab at 0x7fa05118e5d0>,\n",
              " 'سلم': <gensim.models.keyedvectors.Vocab at 0x7fa05118e610>,\n",
              " '#سوريا': <gensim.models.keyedvectors.Vocab at 0x7fa05118e650>,\n",
              " 'انهم': <gensim.models.keyedvectors.Vocab at 0x7fa05118e690>,\n",
              " 'واذا': <gensim.models.keyedvectors.Vocab at 0x7fa05118e6d0>,\n",
              " 'تغير': <gensim.models.keyedvectors.Vocab at 0x7fa05118e710>,\n",
              " 'إله_إلا_هو_الحي': <gensim.models.keyedvectors.Vocab at 0x7fa05118e750>,\n",
              " 'عرفت': <gensim.models.keyedvectors.Vocab at 0x7fa05118e790>,\n",
              " 'حنا': <gensim.models.keyedvectors.Vocab at 0x7fa05118e7d0>,\n",
              " 'قليل': <gensim.models.keyedvectors.Vocab at 0x7fa05118e810>,\n",
              " 'المشاعر': <gensim.models.keyedvectors.Vocab at 0x7fa05118e850>,\n",
              " 'اعلم': <gensim.models.keyedvectors.Vocab at 0x7fa05118e890>,\n",
              " 'اكتب': <gensim.models.keyedvectors.Vocab at 0x7fa05118e8d0>,\n",
              " '9': <gensim.models.keyedvectors.Vocab at 0x7fa05118e910>,\n",
              " 'بيت': <gensim.models.keyedvectors.Vocab at 0x7fa05118e950>,\n",
              " 'واضح': <gensim.models.keyedvectors.Vocab at 0x7fa05118e990>,\n",
              " 'وطن': <gensim.models.keyedvectors.Vocab at 0x7fa05118e9d0>,\n",
              " 'لذلك': <gensim.models.keyedvectors.Vocab at 0x7fa05118ea10>,\n",
              " 'كلمات': <gensim.models.keyedvectors.Vocab at 0x7fa05118ea50>,\n",
              " 'ليا': <gensim.models.keyedvectors.Vocab at 0x7fa05118ea90>,\n",
              " 'أقول': <gensim.models.keyedvectors.Vocab at 0x7fa05118ead0>,\n",
              " 'الرضا': <gensim.models.keyedvectors.Vocab at 0x7fa05118eb10>,\n",
              " 'أسأل': <gensim.models.keyedvectors.Vocab at 0x7fa05118eb50>,\n",
              " 'يارب_العالمين': <gensim.models.keyedvectors.Vocab at 0x7fa05118eb90>,\n",
              " 'معاه': <gensim.models.keyedvectors.Vocab at 0x7fa05118ebd0>,\n",
              " 'بن': <gensim.models.keyedvectors.Vocab at 0x7fa05118ec10>,\n",
              " 'أتمنى': <gensim.models.keyedvectors.Vocab at 0x7fa05118ec50>,\n",
              " 'تعال': <gensim.models.keyedvectors.Vocab at 0x7fa05118ec90>,\n",
              " 'الوحيد': <gensim.models.keyedvectors.Vocab at 0x7fa05118ecd0>,\n",
              " 'قوة': <gensim.models.keyedvectors.Vocab at 0x7fa05118ed10>,\n",
              " 'نحب': <gensim.models.keyedvectors.Vocab at 0x7fa05118ed50>,\n",
              " 'محتاج': <gensim.models.keyedvectors.Vocab at 0x7fa05118ed90>,\n",
              " 'بالخير': <gensim.models.keyedvectors.Vocab at 0x7fa05118edd0>,\n",
              " 'سؤال': <gensim.models.keyedvectors.Vocab at 0x7fa05118ee10>,\n",
              " 'الحنين': <gensim.models.keyedvectors.Vocab at 0x7fa05118ee50>,\n",
              " 'رد': <gensim.models.keyedvectors.Vocab at 0x7fa05118ee90>,\n",
              " 'الملك_وله_الحمد': <gensim.models.keyedvectors.Vocab at 0x7fa05118eed0>,\n",
              " 'ماشاء': <gensim.models.keyedvectors.Vocab at 0x7fa05118ef10>,\n",
              " 'صارت': <gensim.models.keyedvectors.Vocab at 0x7fa05118ef50>,\n",
              " 'يطلع': <gensim.models.keyedvectors.Vocab at 0x7fa05118ef90>,\n",
              " 'واحدة': <gensim.models.keyedvectors.Vocab at 0x7fa05118efd0>,\n",
              " 'تجي': <gensim.models.keyedvectors.Vocab at 0x7fa051192050>,\n",
              " 'أمام': <gensim.models.keyedvectors.Vocab at 0x7fa051192090>,\n",
              " 'صاحبي': <gensim.models.keyedvectors.Vocab at 0x7fa0511920d0>,\n",
              " 'ادري': <gensim.models.keyedvectors.Vocab at 0x7fa051192110>,\n",
              " 'الموضوع': <gensim.models.keyedvectors.Vocab at 0x7fa051192150>,\n",
              " 'وصل': <gensim.models.keyedvectors.Vocab at 0x7fa051192190>,\n",
              " 'جيت': <gensim.models.keyedvectors.Vocab at 0x7fa0511921d0>,\n",
              " 'المطر': <gensim.models.keyedvectors.Vocab at 0x7fa051192210>,\n",
              " 'حياتنا': <gensim.models.keyedvectors.Vocab at 0x7fa051192250>,\n",
              " 'أكون': <gensim.models.keyedvectors.Vocab at 0x7fa051192290>,\n",
              " 'ميت': <gensim.models.keyedvectors.Vocab at 0x7fa0511922d0>,\n",
              " 'لكي': <gensim.models.keyedvectors.Vocab at 0x7fa051192310>,\n",
              " 'تاني': <gensim.models.keyedvectors.Vocab at 0x7fa051192350>,\n",
              " 'الكون': <gensim.models.keyedvectors.Vocab at 0x7fa051192390>,\n",
              " 'وكان': <gensim.models.keyedvectors.Vocab at 0x7fa0511923d0>,\n",
              " 'الأول': <gensim.models.keyedvectors.Vocab at 0x7fa051192410>,\n",
              " 'لقاء': <gensim.models.keyedvectors.Vocab at 0x7fa051192450>,\n",
              " 'أيام': <gensim.models.keyedvectors.Vocab at 0x7fa051192490>,\n",
              " 'ياربي': <gensim.models.keyedvectors.Vocab at 0x7fa0511924d0>,\n",
              " 'تشوف': <gensim.models.keyedvectors.Vocab at 0x7fa051192510>,\n",
              " 'عبدالله': <gensim.models.keyedvectors.Vocab at 0x7fa051192550>,\n",
              " 'أعظم': <gensim.models.keyedvectors.Vocab at 0x7fa051192590>,\n",
              " 'حقيقة': <gensim.models.keyedvectors.Vocab at 0x7fa0511925d0>,\n",
              " 'صديق': <gensim.models.keyedvectors.Vocab at 0x7fa051192610>,\n",
              " 'عبر': <gensim.models.keyedvectors.Vocab at 0x7fa051192650>,\n",
              " 'D': <gensim.models.keyedvectors.Vocab at 0x7fa051192690>,\n",
              " 'طلع': <gensim.models.keyedvectors.Vocab at 0x7fa0511926d0>,\n",
              " 'دعم': <gensim.models.keyedvectors.Vocab at 0x7fa051192710>,\n",
              " 'انتم': <gensim.models.keyedvectors.Vocab at 0x7fa051192750>,\n",
              " 'الأمل': <gensim.models.keyedvectors.Vocab at 0x7fa051192790>,\n",
              " 'السعاده': <gensim.models.keyedvectors.Vocab at 0x7fa0511927d0>,\n",
              " 'غريب': <gensim.models.keyedvectors.Vocab at 0x7fa051192810>,\n",
              " 'إلي': <gensim.models.keyedvectors.Vocab at 0x7fa051192850>,\n",
              " 'هو_عليه_توكلت': <gensim.models.keyedvectors.Vocab at 0x7fa051192890>,\n",
              " 'تمر': <gensim.models.keyedvectors.Vocab at 0x7fa0511928d0>,\n",
              " 'وهو_رب_العرش': <gensim.models.keyedvectors.Vocab at 0x7fa051192910>,\n",
              " 'وبحمده_عدد_خلقه_ورضا': <gensim.models.keyedvectors.Vocab at 0x7fa051192950>,\n",
              " 'ايش': <gensim.models.keyedvectors.Vocab at 0x7fa051192990>,\n",
              " 'الآخرين': <gensim.models.keyedvectors.Vocab at 0x7fa0511929d0>,\n",
              " 'أمر': <gensim.models.keyedvectors.Vocab at 0x7fa051192a10>,\n",
              " 'عندهم': <gensim.models.keyedvectors.Vocab at 0x7fa051192a50>,\n",
              " 'الهوى': <gensim.models.keyedvectors.Vocab at 0x7fa051192a90>,\n",
              " 'كلماته': <gensim.models.keyedvectors.Vocab at 0x7fa051192ad0>,\n",
              " 'عدم': <gensim.models.keyedvectors.Vocab at 0x7fa051192b10>,\n",
              " 'سعيد': <gensim.models.keyedvectors.Vocab at 0x7fa051192b50>,\n",
              " 'حاليا': <gensim.models.keyedvectors.Vocab at 0x7fa051192b90>,\n",
              " 'وجود': <gensim.models.keyedvectors.Vocab at 0x7fa051192bd0>,\n",
              " 'ايران': <gensim.models.keyedvectors.Vocab at 0x7fa051192c10>,\n",
              " 'هما': <gensim.models.keyedvectors.Vocab at 0x7fa051192c50>,\n",
              " 'اهل': <gensim.models.keyedvectors.Vocab at 0x7fa051192c90>,\n",
              " 'سنين': <gensim.models.keyedvectors.Vocab at 0x7fa051192cd0>,\n",
              " 'كامل': <gensim.models.keyedvectors.Vocab at 0x7fa051192d10>,\n",
              " 'معهم': <gensim.models.keyedvectors.Vocab at 0x7fa051192d50>,\n",
              " 'الحمد': <gensim.models.keyedvectors.Vocab at 0x7fa051192d90>,\n",
              " 'الانسان': <gensim.models.keyedvectors.Vocab at 0x7fa051192dd0>,\n",
              " 'بعدين': <gensim.models.keyedvectors.Vocab at 0x7fa051192e10>,\n",
              " 'ساعة': <gensim.models.keyedvectors.Vocab at 0x7fa051192e50>,\n",
              " '12': <gensim.models.keyedvectors.Vocab at 0x7fa051192ed0>,\n",
              " 'تجد': <gensim.models.keyedvectors.Vocab at 0x7fa051192f10>,\n",
              " 'احبه': <gensim.models.keyedvectors.Vocab at 0x7fa051192f50>,\n",
              " 'شهر': <gensim.models.keyedvectors.Vocab at 0x7fa051192f90>,\n",
              " 'معنا': <gensim.models.keyedvectors.Vocab at 0x7fa051192fd0>,\n",
              " 'ليلة': <gensim.models.keyedvectors.Vocab at 0x7fa051118050>,\n",
              " 'حصل': <gensim.models.keyedvectors.Vocab at 0x7fa051118090>,\n",
              " '15': <gensim.models.keyedvectors.Vocab at 0x7fa051118110>,\n",
              " 'أريد': <gensim.models.keyedvectors.Vocab at 0x7fa051118150>,\n",
              " 'جزء': <gensim.models.keyedvectors.Vocab at 0x7fa051118190>,\n",
              " 'باذن': <gensim.models.keyedvectors.Vocab at 0x7fa0511181d0>,\n",
              " 'يقال': <gensim.models.keyedvectors.Vocab at 0x7fa051118210>,\n",
              " 'دى': <gensim.models.keyedvectors.Vocab at 0x7fa051118250>,\n",
              " 'هههههه': <gensim.models.keyedvectors.Vocab at 0x7fa051118290>,\n",
              " 'فأنت': <gensim.models.keyedvectors.Vocab at 0x7fa0511182d0>,\n",
              " 'شوف': <gensim.models.keyedvectors.Vocab at 0x7fa051118310>,\n",
              " 'مفيش': <gensim.models.keyedvectors.Vocab at 0x7fa051118350>,\n",
              " 'كلمه': <gensim.models.keyedvectors.Vocab at 0x7fa051118390>,\n",
              " 'الأشياء': <gensim.models.keyedvectors.Vocab at 0x7fa0511183d0>,\n",
              " 'كنا': <gensim.models.keyedvectors.Vocab at 0x7fa051118410>,\n",
              " 'آمنوا_صلوا_عليه_وسلموا': <gensim.models.keyedvectors.Vocab at 0x7fa051118450>,\n",
              " 'تاريخ': <gensim.models.keyedvectors.Vocab at 0x7fa051118490>,\n",
              " 'شو': <gensim.models.keyedvectors.Vocab at 0x7fa0511184d0>,\n",
              " 'شنو': <gensim.models.keyedvectors.Vocab at 0x7fa051118510>,\n",
              " 'ماهي': <gensim.models.keyedvectors.Vocab at 0x7fa051118550>,\n",
              " 'قادر': <gensim.models.keyedvectors.Vocab at 0x7fa051118590>,\n",
              " 'أنها': <gensim.models.keyedvectors.Vocab at 0x7fa0511185d0>,\n",
              " '#الاتحاد': <gensim.models.keyedvectors.Vocab at 0x7fa051118610>,\n",
              " 'انو': <gensim.models.keyedvectors.Vocab at 0x7fa051118650>,\n",
              " 'جديدة': <gensim.models.keyedvectors.Vocab at 0x7fa051118690>,\n",
              " '20': <gensim.models.keyedvectors.Vocab at 0x7fa051118710>,\n",
              " 'مالك': <gensim.models.keyedvectors.Vocab at 0x7fa051118750>,\n",
              " 'عمره': <gensim.models.keyedvectors.Vocab at 0x7fa051118790>,\n",
              " 'شباب': <gensim.models.keyedvectors.Vocab at 0x7fa0511187d0>,\n",
              " 'إيران': <gensim.models.keyedvectors.Vocab at 0x7fa051118810>,\n",
              " 'تموت': <gensim.models.keyedvectors.Vocab at 0x7fa051118850>,\n",
              " 'وانتي': <gensim.models.keyedvectors.Vocab at 0x7fa051118890>,\n",
              " 'الحقيقة': <gensim.models.keyedvectors.Vocab at 0x7fa0511188d0>,\n",
              " 'الغياب': <gensim.models.keyedvectors.Vocab at 0x7fa051118910>,\n",
              " 'حنان': <gensim.models.keyedvectors.Vocab at 0x7fa051118950>,\n",
              " 'يمر': <gensim.models.keyedvectors.Vocab at 0x7fa051118990>,\n",
              " 'الجديد': <gensim.models.keyedvectors.Vocab at 0x7fa0511189d0>,\n",
              " 'خالد': <gensim.models.keyedvectors.Vocab at 0x7fa051118a10>,\n",
              " 'منو': <gensim.models.keyedvectors.Vocab at 0x7fa051118a50>,\n",
              " 'تنسى': <gensim.models.keyedvectors.Vocab at 0x7fa051118a90>,\n",
              " 'مسلم': <gensim.models.keyedvectors.Vocab at 0x7fa051118ad0>,\n",
              " 'خل': <gensim.models.keyedvectors.Vocab at 0x7fa051118b10>,\n",
              " 'السبب': <gensim.models.keyedvectors.Vocab at 0x7fa051118b50>,\n",
              " 'اللهم_إنا_نسألك': <gensim.models.keyedvectors.Vocab at 0x7fa051118b90>,\n",
              " 'تحبه': <gensim.models.keyedvectors.Vocab at 0x7fa051118bd0>,\n",
              " 'ههههههه': <gensim.models.keyedvectors.Vocab at 0x7fa051118c10>,\n",
              " '11': <gensim.models.keyedvectors.Vocab at 0x7fa051118c90>,\n",
              " 'يجعل': <gensim.models.keyedvectors.Vocab at 0x7fa051118cd0>,\n",
              " 'عالم': <gensim.models.keyedvectors.Vocab at 0x7fa051118d10>,\n",
              " 'احيانا': <gensim.models.keyedvectors.Vocab at 0x7fa051118d50>,\n",
              " 'العام': <gensim.models.keyedvectors.Vocab at 0x7fa051118d90>,\n",
              " 'سبيل': <gensim.models.keyedvectors.Vocab at 0x7fa051118dd0>,\n",
              " 'للأسف': <gensim.models.keyedvectors.Vocab at 0x7fa051118e10>,\n",
              " 'القيوم_وأتوب_إليه': <gensim.models.keyedvectors.Vocab at 0x7fa051118e50>,\n",
              " 'كانوا': <gensim.models.keyedvectors.Vocab at 0x7fa051118e90>,\n",
              " 'جاء': <gensim.models.keyedvectors.Vocab at 0x7fa051118ed0>,\n",
              " 'أمل': <gensim.models.keyedvectors.Vocab at 0x7fa051118f10>,\n",
              " 'أسعد': <gensim.models.keyedvectors.Vocab at 0x7fa051118f50>,\n",
              " 'التاريخ': <gensim.models.keyedvectors.Vocab at 0x7fa051118f90>,\n",
              " 'سيدنا_محمد': <gensim.models.keyedvectors.Vocab at 0x7fa051118fd0>,\n",
              " 'العلم': <gensim.models.keyedvectors.Vocab at 0x7fa05111e050>,\n",
              " 'مشكلة': <gensim.models.keyedvectors.Vocab at 0x7fa05111e090>,\n",
              " 'يحتاج': <gensim.models.keyedvectors.Vocab at 0x7fa05111e0d0>,\n",
              " 'النساء': <gensim.models.keyedvectors.Vocab at 0x7fa05111e110>,\n",
              " 'اله_الا_انت': <gensim.models.keyedvectors.Vocab at 0x7fa05111e150>,\n",
              " 'العيون': <gensim.models.keyedvectors.Vocab at 0x7fa05111e190>,\n",
              " 'حولك': <gensim.models.keyedvectors.Vocab at 0x7fa05111e1d0>,\n",
              " 'بارك': <gensim.models.keyedvectors.Vocab at 0x7fa05111e210>,\n",
              " 'الفوز': <gensim.models.keyedvectors.Vocab at 0x7fa05111e250>,\n",
              " 'رضي': <gensim.models.keyedvectors.Vocab at 0x7fa05111e290>,\n",
              " 'ههههه': <gensim.models.keyedvectors.Vocab at 0x7fa05111e2d0>,\n",
              " 'كلامك': <gensim.models.keyedvectors.Vocab at 0x7fa05111e310>,\n",
              " 'عنهم': <gensim.models.keyedvectors.Vocab at 0x7fa05111e350>,\n",
              " 'وجع': <gensim.models.keyedvectors.Vocab at 0x7fa05111e390>,\n",
              " 'ألا': <gensim.models.keyedvectors.Vocab at 0x7fa05111e3d0>,\n",
              " 'ماني': <gensim.models.keyedvectors.Vocab at 0x7fa05111e410>,\n",
              " 'مثلا': <gensim.models.keyedvectors.Vocab at 0x7fa05111e450>,\n",
              " 'ساعات': <gensim.models.keyedvectors.Vocab at 0x7fa05111e490>,\n",
              " 'بهم': <gensim.models.keyedvectors.Vocab at 0x7fa05111e4d0>,\n",
              " 'نادي': <gensim.models.keyedvectors.Vocab at 0x7fa05111e510>,\n",
              " 'شكل': <gensim.models.keyedvectors.Vocab at 0x7fa05111e550>,\n",
              " 'بحبك': <gensim.models.keyedvectors.Vocab at 0x7fa05111e590>,\n",
              " 'ياليت': <gensim.models.keyedvectors.Vocab at 0x7fa05111e5d0>,\n",
              " 'حل': <gensim.models.keyedvectors.Vocab at 0x7fa05111e610>,\n",
              " 'للناس': <gensim.models.keyedvectors.Vocab at 0x7fa05111e650>,\n",
              " 'ترجع': <gensim.models.keyedvectors.Vocab at 0x7fa05111e690>,\n",
              " 'اليه': <gensim.models.keyedvectors.Vocab at 0x7fa05111e6d0>,\n",
              " 'ولد': <gensim.models.keyedvectors.Vocab at 0x7fa05111e710>,\n",
              " 'شلون': <gensim.models.keyedvectors.Vocab at 0x7fa05111e750>,\n",
              " 'سلمان': <gensim.models.keyedvectors.Vocab at 0x7fa05111e790>,\n",
              " 'يرجع': <gensim.models.keyedvectors.Vocab at 0x7fa05111e7d0>,\n",
              " 'نقول': <gensim.models.keyedvectors.Vocab at 0x7fa05111e810>,\n",
              " 'تسليما': <gensim.models.keyedvectors.Vocab at 0x7fa05111e850>,\n",
              " 'وضع': <gensim.models.keyedvectors.Vocab at 0x7fa05111e890>,\n",
              " 'قريبا': <gensim.models.keyedvectors.Vocab at 0x7fa05111e8d0>,\n",
              " 'البرد': <gensim.models.keyedvectors.Vocab at 0x7fa05111e910>,\n",
              " 'أحسن': <gensim.models.keyedvectors.Vocab at 0x7fa05111e950>,\n",
              " 'احمد': <gensim.models.keyedvectors.Vocab at 0x7fa05111e990>,\n",
              " 'جنتك': <gensim.models.keyedvectors.Vocab at 0x7fa05111e9d0>,\n",
              " 'الحديث': <gensim.models.keyedvectors.Vocab at 0x7fa05111ea10>,\n",
              " 'تكتب': <gensim.models.keyedvectors.Vocab at 0x7fa05111ea50>,\n",
              " 'هلا': <gensim.models.keyedvectors.Vocab at 0x7fa05111ea90>,\n",
              " 'دعوة': <gensim.models.keyedvectors.Vocab at 0x7fa05111ead0>,\n",
              " 'دول': <gensim.models.keyedvectors.Vocab at 0x7fa05111eb10>,\n",
              " 'زمن': <gensim.models.keyedvectors.Vocab at 0x7fa05111eb50>,\n",
              " 'سيكون': <gensim.models.keyedvectors.Vocab at 0x7fa05111eb90>,\n",
              " 'وربي': <gensim.models.keyedvectors.Vocab at 0x7fa05111ebd0>,\n",
              " 'لم_يكن': <gensim.models.keyedvectors.Vocab at 0x7fa05111ec10>,\n",
              " 'بحب': <gensim.models.keyedvectors.Vocab at 0x7fa05111ec50>,\n",
              " 'فين': <gensim.models.keyedvectors.Vocab at 0x7fa05111ec90>,\n",
              " 'يدخل': <gensim.models.keyedvectors.Vocab at 0x7fa05111ecd0>,\n",
              " 'الخاص': <gensim.models.keyedvectors.Vocab at 0x7fa05111ed10>,\n",
              " 'نفسها': <gensim.models.keyedvectors.Vocab at 0x7fa05111ed50>,\n",
              " 'قدام': <gensim.models.keyedvectors.Vocab at 0x7fa05111ed90>,\n",
              " 'الأهلي': <gensim.models.keyedvectors.Vocab at 0x7fa05111edd0>,\n",
              " '#مصر': <gensim.models.keyedvectors.Vocab at 0x7fa05111ee10>,\n",
              " 'احساس': <gensim.models.keyedvectors.Vocab at 0x7fa05111ee50>,\n",
              " 'عينك': <gensim.models.keyedvectors.Vocab at 0x7fa05111ee90>,\n",
              " 'ولك': <gensim.models.keyedvectors.Vocab at 0x7fa05111eed0>,\n",
              " 'ذنب': <gensim.models.keyedvectors.Vocab at 0x7fa05111ef10>,\n",
              " 'حسن': <gensim.models.keyedvectors.Vocab at 0x7fa05111ef50>,\n",
              " 'لابد': <gensim.models.keyedvectors.Vocab at 0x7fa05111ef90>,\n",
              " 'العقل': <gensim.models.keyedvectors.Vocab at 0x7fa05111efd0>,\n",
              " 'صباحك': <gensim.models.keyedvectors.Vocab at 0x7fa051123050>,\n",
              " 'بيننا': <gensim.models.keyedvectors.Vocab at 0x7fa051123090>,\n",
              " 'شعب': <gensim.models.keyedvectors.Vocab at 0x7fa0511230d0>,\n",
              " 'رائع': <gensim.models.keyedvectors.Vocab at 0x7fa051123110>,\n",
              " 'الورد': <gensim.models.keyedvectors.Vocab at 0x7fa051123150>,\n",
              " 'تعبت': <gensim.models.keyedvectors.Vocab at 0x7fa051123190>,\n",
              " 'امام': <gensim.models.keyedvectors.Vocab at 0x7fa0511231d0>,\n",
              " 'اسعد': <gensim.models.keyedvectors.Vocab at 0x7fa051123210>,\n",
              " 'كدة': <gensim.models.keyedvectors.Vocab at 0x7fa051123250>,\n",
              " 'السعودي': <gensim.models.keyedvectors.Vocab at 0x7fa051123290>,\n",
              " 'تغيب': <gensim.models.keyedvectors.Vocab at 0x7fa0511232d0>,\n",
              " 'هههههههه': <gensim.models.keyedvectors.Vocab at 0x7fa051123310>,\n",
              " 'وبعد': <gensim.models.keyedvectors.Vocab at 0x7fa051123350>,\n",
              " 'وبس': <gensim.models.keyedvectors.Vocab at 0x7fa051123390>,\n",
              " 'تبكي': <gensim.models.keyedvectors.Vocab at 0x7fa0511233d0>,\n",
              " 'ياللي': <gensim.models.keyedvectors.Vocab at 0x7fa051123410>,\n",
              " 'يلعب': <gensim.models.keyedvectors.Vocab at 0x7fa051123450>,\n",
              " 'رب_العالمين': <gensim.models.keyedvectors.Vocab at 0x7fa051123490>,\n",
              " 'مالي': <gensim.models.keyedvectors.Vocab at 0x7fa0511234d0>,\n",
              " 'خلك': <gensim.models.keyedvectors.Vocab at 0x7fa051123510>,\n",
              " 'دخل': <gensim.models.keyedvectors.Vocab at 0x7fa051123550>,\n",
              " 'كلما': <gensim.models.keyedvectors.Vocab at 0x7fa051123590>,\n",
              " 'نبي': <gensim.models.keyedvectors.Vocab at 0x7fa0511235d0>,\n",
              " 'قصة': <gensim.models.keyedvectors.Vocab at 0x7fa051123610>,\n",
              " 'لحظه': <gensim.models.keyedvectors.Vocab at 0x7fa051123650>,\n",
              " 'يتكلم': <gensim.models.keyedvectors.Vocab at 0x7fa051123690>,\n",
              " 'الدولة': <gensim.models.keyedvectors.Vocab at 0x7fa0511236d0>,\n",
              " 'ميسي': <gensim.models.keyedvectors.Vocab at 0x7fa051123710>,\n",
              " 'جمهور': <gensim.models.keyedvectors.Vocab at 0x7fa051123750>,\n",
              " 'حياته': <gensim.models.keyedvectors.Vocab at 0x7fa051123790>,\n",
              " 'الغلا': <gensim.models.keyedvectors.Vocab at 0x7fa0511237d0>,\n",
              " 'الماضي': <gensim.models.keyedvectors.Vocab at 0x7fa051123810>,\n",
              " 'السؤال': <gensim.models.keyedvectors.Vocab at 0x7fa051123850>,\n",
              " 'النور': <gensim.models.keyedvectors.Vocab at 0x7fa051123890>,\n",
              " 'الصورة': <gensim.models.keyedvectors.Vocab at 0x7fa0511238d0>,\n",
              " 'بشكل': <gensim.models.keyedvectors.Vocab at 0x7fa051123910>,\n",
              " 'سهل': <gensim.models.keyedvectors.Vocab at 0x7fa051123950>,\n",
              " 'الواقع': <gensim.models.keyedvectors.Vocab at 0x7fa051123990>,\n",
              " 'الحساب': <gensim.models.keyedvectors.Vocab at 0x7fa0511239d0>,\n",
              " 'قرب': <gensim.models.keyedvectors.Vocab at 0x7fa051123a10>,\n",
              " 'اختي': <gensim.models.keyedvectors.Vocab at 0x7fa051123a50>,\n",
              " 'نص': <gensim.models.keyedvectors.Vocab at 0x7fa051123a90>,\n",
              " 'تحتاج': <gensim.models.keyedvectors.Vocab at 0x7fa051123ad0>,\n",
              " 'تعود': <gensim.models.keyedvectors.Vocab at 0x7fa051123b10>,\n",
              " 'امس': <gensim.models.keyedvectors.Vocab at 0x7fa051123b50>,\n",
              " 'حاول': <gensim.models.keyedvectors.Vocab at 0x7fa051123b90>,\n",
              " 'ولن': <gensim.models.keyedvectors.Vocab at 0x7fa051123bd0>,\n",
              " 'اغفر': <gensim.models.keyedvectors.Vocab at 0x7fa051123c10>,\n",
              " 'قتل': <gensim.models.keyedvectors.Vocab at 0x7fa051123c50>,\n",
              " 'البحر': <gensim.models.keyedvectors.Vocab at 0x7fa051123c90>,\n",
              " 'أين': <gensim.models.keyedvectors.Vocab at 0x7fa051123cd0>,\n",
              " '100': <gensim.models.keyedvectors.Vocab at 0x7fa051123d50>,\n",
              " 'للاسف': <gensim.models.keyedvectors.Vocab at 0x7fa051123d90>,\n",
              " 'الحمدالله': <gensim.models.keyedvectors.Vocab at 0x7fa051123dd0>,\n",
              " 'مشتاق': <gensim.models.keyedvectors.Vocab at 0x7fa051123e10>,\n",
              " 'شوية': <gensim.models.keyedvectors.Vocab at 0x7fa051123e50>,\n",
              " 'تعب': <gensim.models.keyedvectors.Vocab at 0x7fa051123e90>,\n",
              " 'رقم': <gensim.models.keyedvectors.Vocab at 0x7fa051123ed0>,\n",
              " 'تسلم': <gensim.models.keyedvectors.Vocab at 0x7fa051123f10>,\n",
              " 'قيمة': <gensim.models.keyedvectors.Vocab at 0x7fa051123f50>,\n",
              " 'دور': <gensim.models.keyedvectors.Vocab at 0x7fa051123f90>,\n",
              " 'تحزن': <gensim.models.keyedvectors.Vocab at 0x7fa051123fd0>,\n",
              " 'ذهب': <gensim.models.keyedvectors.Vocab at 0x7fa051128050>,\n",
              " 'فهي': <gensim.models.keyedvectors.Vocab at 0x7fa051128090>,\n",
              " 'عليا': <gensim.models.keyedvectors.Vocab at 0x7fa0511280d0>,\n",
              " 'سبحانه': <gensim.models.keyedvectors.Vocab at 0x7fa051128110>,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word2vec.wv"
      ],
      "metadata": {
        "id": "O3IBTv3H2Sm_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFoet4ZiwiRL",
        "outputId": "15e57eb1-5e37-49b4-9567-9fd9de679842"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
            "  \n"
          ]
        }
      ],
      "source": [
        "word2vec_feature = []\n",
        "\n",
        "# adds the word2vec average\n",
        "for tweet in x_train:\n",
        "    average_vec = np.zeros(300)\n",
        "    for word in tweet:\n",
        "        if word in word2vec.wv:\n",
        "            average_vec += (word2vec.wv[word] / len(tweet))\n",
        "        else:\n",
        "            pass\n",
        "    word2vec_feature.append(average_vec)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(word2vec_feature)"
      ],
      "metadata": {
        "id": "dVf4rv7Y3xKw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e453e154-2a4e-41bb-80fa-c8cedf754d14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "412375"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T4BF3gy2eJD_",
        "outputId": "fda53c29-4ead-425a-98ef-948cfcffc229"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "412375"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_vec=word2vec_feature\n",
        "y_train_vec=y_train"
      ],
      "metadata": {
        "id": "k-p6Vuw74B9t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word2vec_feature = []\n",
        "\n",
        "# adds the word2vec average\n",
        "for tweet in x_test:\n",
        "    average_vec = np.zeros(300)\n",
        "    for word in tweet:\n",
        "        if word in word2vec.wv:\n",
        "            average_vec += (word2vec.wv[word] / len(tweet))\n",
        "        else:\n",
        "            pass\n",
        "    word2vec_feature.append(average_vec)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SXj640Jkeq82",
        "outputId": "cb88575f-5d24-4e37-b36b-7eec0ae25a43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_vec=word2vec_feature\n",
        "y_test_vec=y_test"
      ],
      "metadata": {
        "id": "64zAytAoev8L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word2vec_feature = []\n",
        "\n",
        "# adds the word2vec average\n",
        "for tweet in x_test:\n",
        "    average_vec = np.zeros(300)\n",
        "    for word in tweet:\n",
        "        if word in word2vec.wv:\n",
        "            average_vec += (word2vec.wv[word] / len(tweet))\n",
        "        else:\n",
        "            pass\n",
        "    word2vec_feature.append(average_vec)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "npUTmhz1qwZ3",
        "outputId": "b6c923b8-a2f1-487b-b9c7-9b0778faea4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_vec=word2vec_feature\n",
        "y_test_vec=y_test"
      ],
      "metadata": {
        "id": "YS4Vp2X6q3Ld"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tweets_list=[]\n",
        "# adds the word2vec average\n",
        "for tweet in x_train[0:100000]:\n",
        "    i=0\n",
        "    one_tweet=[]\n",
        "    for i in range(20):\n",
        "      if i<len(tweet):\n",
        "        if tweet[i] in word2vec.wv:\n",
        "            one_tweet.append(word2vec.wv[tweet[i]])\n",
        "        else:\n",
        "            one_tweet.append( np.zeros(300))\n",
        "      else:\n",
        "        one_tweet.append( np.zeros(300))\n",
        "    tweets_list.append(one_tweet)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LC-j-PVOy7t0",
        "outputId": "67f41315-fd32-4930-cdbd-4bd96471793a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
            "  if __name__ == '__main__':\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del word2vec"
      ],
      "metadata": {
        "id": "qarPAjCztSAp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_vec=np.array(tweets_list)\n",
        "x_train_vec=x_train_vec.reshape(x_train_vec.shape[0],-1)"
      ],
      "metadata": {
        "id": "avlIaDnj2MHy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del  tweets_list"
      ],
      "metadata": {
        "id": "WpP23oFfoc7f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_vec=y_train[0:100000]"
      ],
      "metadata": {
        "id": "_N2OpbDoopVg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##LR Model"
      ],
      "metadata": {
        "id": "evPNeH-Yoksr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression()\n"
      ],
      "metadata": {
        "id": "nJQzAAyHHD5H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, classification_report\n",
        "from time import time\n",
        "def train_predict(classifier,X_train,y_train,X_test,y_test, average= 'macro'): \n",
        "    '''\n",
        "\n",
        "    inputs:\n",
        "       - classifier: the learning algorithm to be trained and predicted on\n",
        "       - x_train  : train set Features\n",
        "       - y_train  : train set Labels\n",
        "       - x_test   : test set Features\n",
        "       - y_train  : test set Labels\n",
        " \n",
        "    '''\n",
        "    \n",
        "    results = {}\n",
        "    \n",
        "    # Fit the classifier to the training data and Calculate the time\n",
        "    start_train = time()\n",
        "    classifier = classifier.fit(X_train, y_train)\n",
        "    end_train = time()\n",
        "    results['train_time'] = round((end_train-start_train), 2)\n",
        "\n",
        "    # Predict the training/testing labels and Calculate the time\n",
        "    start_test = time()\n",
        "    y_pred_train = classifier.predict(X_train)\n",
        "    y_pred_test = classifier.predict(X_test)\n",
        "    end_test = time()   \n",
        "    results['pred_time'] = round((end_test-start_test), 2)\n",
        "            \n",
        "    # Model evaluation (training/testing data) using accuracy\n",
        "    results['acc_train'] = round(accuracy_score(y_train, y_pred_train), 2)\n",
        "    results['acc_test'] = round(accuracy_score(y_test, y_pred_test), 2)\n",
        "    \n",
        "    # Model evaluation (training/testing data) using f1-score\n",
        "    results['f1_train'] = round(f1_score(y_train, y_pred_train, average= average), 2)\n",
        "    results['f1_test'] = round(f1_score(y_test, y_pred_test, average= average), 2)\n",
        "    \n",
        "    # Display classification_report\n",
        "    report = classification_report(y_test, y_pred_test)\n",
        "       \n",
        "    \n",
        "    # Return the results\n",
        "    return results, report"
      ],
      "metadata": {
        "id": "8B0DPhEDKSOm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#results, report = train_predict(lr, x_train_vec,  np.argmax(y_train_vec.to_numpy(), axis=1),\n",
        "                               x_train_vec,  np.argmax(y_train_vec.to_numpy(), axis=1))"
      ],
      "metadata": {
        "id": "HERYz9shKh6M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f397b9e-09cb-48e1-8ad6-d111b43194ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results"
      ],
      "metadata": {
        "id": "sfrbXrkTN1CN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83d2d838-790a-41af-bf22-e11312447a68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'acc_test': 0.17,\n",
              " 'acc_train': 0.17,\n",
              " 'f1_test': 0.09,\n",
              " 'f1_train': 0.09,\n",
              " 'pred_time': 5.7,\n",
              " 'train_time': 633.66}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(report)"
      ],
      "metadata": {
        "id": "NLQySsBdN27f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c86d6595-9fb9-495b-adf0-3baf2932821e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.14      0.04      0.06      5756\n",
            "           1       0.16      0.06      0.09      5730\n",
            "           2       0.18      0.02      0.04      3510\n",
            "           3       0.18      0.57      0.27     12435\n",
            "           4       0.18      0.03      0.05      3397\n",
            "           5       0.17      0.03      0.06      6036\n",
            "           6       0.16      0.24      0.19      9264\n",
            "           7       0.16      0.09      0.12      6089\n",
            "           8       0.15      0.15      0.15      7963\n",
            "           9       0.19      0.03      0.05      2427\n",
            "          10       0.17      0.01      0.02      4097\n",
            "          11       0.18      0.28      0.22      9655\n",
            "          12       0.16      0.23      0.19      6779\n",
            "          13       0.16      0.08      0.10      5921\n",
            "          14       0.12      0.01      0.01      3145\n",
            "          15       0.25      0.02      0.04      3593\n",
            "          16       0.33      0.01      0.01      2000\n",
            "          17       0.16      0.00      0.00      2203\n",
            "\n",
            "    accuracy                           0.17    100000\n",
            "   macro avg       0.18      0.10      0.09    100000\n",
            "weighted avg       0.17      0.17      0.13    100000\n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "sQkYBgFwexY9",
        "SPagXaiLoEfl",
        "K6L9dJ51ZIRe",
        "LkVBUMgEihKV"
      ],
      "name": "ML NLP Arabic Dialect Identification in the Wild.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}