Quote_Generator_Project.ipynb

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        "**Summary**\n",
        "In this Notebook, I finetune GPT-2 on \"Arabic\" Quotes dataset using gpt-2\" library.\n",
        "\n",
        "**Dataset** : Quotes-500k\n",
        "\n",
        "**Goal** : is to Generate Arabic Quotes from trained Language Model."
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "**BrainStorming Steps**\n",
        "\n",
        "\n",
        "1.   Importing data & pre-processing\n",
        "2.   Preparing the dataset\n",
        "3.   finetune GPT-2 model\n",
        "4.   generate quotes\n",
        "5.   evaluation\n",
        "\n",
        "\n",
        "\n"
      ]
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      "source": [
        "# 1.   Importing Data & Preprocessing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2FNTfuEY16C"
      },
      "source": [
        "Prepare the dataset and build a TextDataset"
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      "execution_count": null,
      "metadata": {
        "id": "dQJhvI0Chj1n"
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      "source": [
        "import pandas as pd"
      ]
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      "execution_count": null,
      "metadata": {
        "id": "cVrtqowHhj4U"
      },
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      "source": [
        "data= pd.read_csv('/content/complete_arabic_quotes.csv')"
      ]
    },
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      "metadata": {
        "id": "VawbuPrX-NsJ"
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      "source": [
        "Exploring dataset"
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        "data.shape"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>quote</th>\n",
              "      <th>tag</th>\n",
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              "    <tr>\n",
              "      <th>29307</th>\n",
              "      <td>الطريق إلى النجاح مليء بالعديد من أماكن وقوف السيارات المغرية.</td>\n",
              "      <td>النجاح</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44521</th>\n",
              "      <td>نحن بحاجة إلى أن نعيش تجربتنا كما هي وأعيننا مفتوحة. الكون هو ما هو عليه ، سواء أحببنا ذلك أم لا.</td>\n",
              "      <td>خبرة</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7238</th>\n",
              "      <td>ما يدور في ذهني هو نوع من Chautauqua ... هذا هو الاسم الوحيد الذي يمكنني التفكير فيه ... مثل عرض الخيمة المتنقل Chautauquas الذي كان يتنقل عبر أمر...</td>\n",
              "      <td>ملهم</td>\n",
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              "    <tr>\n",
              "      <th>73700</th>\n",
              "      <td>الفتيات سوف يجتمعن فقط للالتقاء. الرجال بحاجة إلى نشاط كعذر. وإلا فإنه من الشائع جدًا أن يتعاملوا معه.</td>\n",
              "      <td>أولاد</td>\n",
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              "      <th>85189</th>\n",
              "      <td>يمكنك أن تقرأ كلمة الله ، ولكن بدون وساطة وصلاة ، نصبح ضعفاء روحيًا وغير ممتلئين.</td>\n",
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              "                                                                                                                                                       quote  \\\n",
              "29307                                                                                         الطريق إلى النجاح مليء بالعديد من أماكن وقوف السيارات المغرية.   \n",
              "44521                                                      نحن بحاجة إلى أن نعيش تجربتنا كما هي وأعيننا مفتوحة. الكون هو ما هو عليه ، سواء أحببنا ذلك أم لا.   \n",
              "7238   ما يدور في ذهني هو نوع من Chautauqua ... هذا هو الاسم الوحيد الذي يمكنني التفكير فيه ... مثل عرض الخيمة المتنقل Chautauquas الذي كان يتنقل عبر أمر...   \n",
              "73700                                                 الفتيات سوف يجتمعن فقط للالتقاء. الرجال بحاجة إلى نشاط كعذر. وإلا فإنه من الشائع جدًا أن يتعاملوا معه.   \n",
              "85189                                                                      يمكنك أن تقرأ كلمة الله ، ولكن بدون وساطة وصلاة ، نصبح ضعفاء روحيًا وغير ممتلئين.   \n",
              "\n",
              "          tag  \n",
              "29307  النجاح  \n",
              "44521    خبرة  \n",
              "7238     ملهم  \n",
              "73700   أولاد  \n",
              "85189    محنة  "
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      "source": [
        "# we will create a simple text generator not based on a any other feature\n",
        "#we don't need the tag\n",
        "data = data.drop('tag', axis=1)"
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        "# studing the length of each input\n",
        "length = []\n",
        "for index, row in data.iterrows():   \n",
        "    l = len (row['quote'])\n",
        "    length.append(l)"
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        "len(length)"
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        "data[\"quote-len\"]=length"
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              "      <td>لكن الله بين محبته لنا ، لأنه ونحن بعد خطاة مات المسيح لأجلنا. رومية 5: 8</td>\n",
              "      <td>73</td>\n",
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              "      <th>10726</th>\n",
              "      <td>لا تكن حكيما بأعينك. اطلبوا طرق الله.</td>\n",
              "      <td>37</td>\n",
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              "      <th>51192</th>\n",
              "      <td>لا تكذب لحماية مشاعري. أفضل أن تقول الحقيقة بمحبة واحترام. سأكون بخير!</td>\n",
              "      <td>70</td>\n",
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              "      <th>49784</th>\n",
              "      <td>المطر ليس تغيرًا موسميًا ، إنه شعور يعيد الذكريات!</td>\n",
              "      <td>50</td>\n",
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              "      <td>إذا لم يستطع المرء الاستمتاع بقراءة كتاب مرارًا وتكرارًا ، فلا فائدة من قراءته على الإطلاق.</td>\n",
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              "                                                                                             quote  \\\n",
              "61126                    لكن الله بين محبته لنا ، لأنه ونحن بعد خطاة مات المسيح لأجلنا. رومية 5: 8   \n",
              "10726                                                        لا تكن حكيما بأعينك. اطلبوا طرق الله.   \n",
              "51192                       لا تكذب لحماية مشاعري. أفضل أن تقول الحقيقة بمحبة واحترام. سأكون بخير!   \n",
              "49784                                           المطر ليس تغيرًا موسميًا ، إنه شعور يعيد الذكريات!   \n",
              "66879  إذا لم يستطع المرء الاستمتاع بقراءة كتاب مرارًا وتكرارًا ، فلا فائدة من قراءته على الإطلاق.   \n",
              "\n",
              "       quote-len  \n",
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              "10726         37  \n",
              "51192         70  \n",
              "49784         50  \n",
              "66879         91  "
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        "import matplotlib.pyplot as plt"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "E8U1bhU9A4hj",
        "outputId": "d8725ae2-f0a5-48e0-c58d-1767b043bc1d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set(style=\"darkgrid\")\n",
        " \n",
        "# creating a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
        "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
        " \n",
        "# assigning a graph to each ax\n",
        "sns.boxplot(data[\"quote-len\"], ax=ax_box)\n",
        "sns.histplot(data=data, x=\"quote-len\", ax=ax_hist)\n",
        " \n",
        "# Remove x axis name for the boxplot\n",
        "ax_box.set(xlabel='')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CvUJcLrABxWg"
      },
      "source": [
        "we will set the max_length=150, each quote longer that 150 will be deleted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rQAk8m7NGA_C"
      },
      "outputs": [],
      "source": [
        "max_length = 150"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sFChzWSeEC1k"
      },
      "outputs": [],
      "source": [
        "quote_list = data.values.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "52Nj_qCHA4om"
      },
      "outputs": [],
      "source": [
        "for elm in quote_list:\n",
        "    if elm[1]> max_length:\n",
        "     quote_list.remove(elm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gsT2bTkBFzlE"
      },
      "outputs": [],
      "source": [
        "data = pd.DataFrame(quote_list, columns = ['text', 'leng'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "N9rnwUT9A4rR",
        "outputId": "63e07bbe-6593-4fbd-ec9d-503e20f70dc2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-53ea1b60-51b5-4746-8797-7b6cb189fbc6\">\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>text</th>\n",
              "      <th>leng</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>يجب أن ترقص كما لو أنه لا يوجد أحد يشاهد ، أحب كما لو أنك لن تتأذى أبدًا ، وغني كأن لا أحد يستمع ، وعيش كما لو كانت جنة على الأرض.</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>أنت تعلم أنك في حالة حب عندما لا تستطيع النوم لأن الواقع في النهاية أفضل من أحلامك.</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>الصديق هو الشخص الذي يعرف كل شيء عنك ولا يزال يحبك.</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>لا يمكن للظلام أن يطرد الظلام: النور وحده قادر على فعل ذلك. لا يمكن للكراهية أن تطرد الكراهية: الحب وحده هو الذي يفعل ذلك.</td>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>نقبل الحب الذي نظن اننا نستحقه.</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53ea1b60-51b5-4746-8797-7b6cb189fbc6')\"\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",
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              "      </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-53ea1b60-51b5-4746-8797-7b6cb189fbc6 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-53ea1b60-51b5-4746-8797-7b6cb189fbc6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                                                                                                 text  \\\n",
              "0  يجب أن ترقص كما لو أنه لا يوجد أحد يشاهد ، أحب كما لو أنك لن تتأذى أبدًا ، وغني كأن لا أحد يستمع ، وعيش كما لو كانت جنة على الأرض.   \n",
              "1                                                 أنت تعلم أنك في حالة حب عندما لا تستطيع النوم لأن الواقع في النهاية أفضل من أحلامك.   \n",
              "2                                                                                 الصديق هو الشخص الذي يعرف كل شيء عنك ولا يزال يحبك.   \n",
              "3          لا يمكن للظلام أن يطرد الظلام: النور وحده قادر على فعل ذلك. لا يمكن للكراهية أن تطرد الكراهية: الحب وحده هو الذي يفعل ذلك.   \n",
              "4                                                                                                     نقبل الحب الذي نظن اننا نستحقه.   \n",
              "\n",
              "   leng  \n",
              "0   130  \n",
              "1    83  \n",
              "2    51  \n",
              "3   122  \n",
              "4    31  "
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QhZQfQ0PA4tx",
        "outputId": "e9c19434-9ceb-4a7d-a7ab-7f4bdd3b3af6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(86242, 2)"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "_zYLlpPgA4wa",
        "outputId": "b57b46df-3c70-4585-e9ee-bca7a30f32bf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f3ba437cfd0>"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.displot(data, x=\"leng\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2GaQV8wxKH-7"
      },
      "outputs": [],
      "source": [
        "pd.options.display.max_colwidth = 150"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "U1gXfxBGHB3F",
        "outputId": "6e263782-4a2b-4687-e664-9d9426ca6b8f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6eb1943a-bdd5-477e-bcc6-d13f15b26650\">\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>text</th>\n",
              "      <th>leng</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>55196</th>\n",
              "      <td>معرفة الكتاب تتخذ إجراءات بناءً على الأسباب</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22728</th>\n",
              "      <td>حتى الآن ، أتساءل عن مدى اقتناع حياتي.</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52427</th>\n",
              "      <td>ابتسامة طفل توسع الكون (D'un enfant le sourire - Agrandit l'univers)</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78686</th>\n",
              "      <td>القول المأثور \"إذا كنت تريد القيام بشيء ما ، اسأل امرأة مشغولة\" هو اعتراف مباشر بالأبوة في معسكر التدريب على الكفاءة.</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53500</th>\n",
              "      <td>بنفس الدرجة التي يصبح فيها فهمك لأفريكا وموقفك تجاهها أكثر إيجابية ، سيصبح فهمك وموقفك تجاه نفسك أكثر إيجابية أيضًا ...</td>\n",
              "      <td>119</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            '<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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                                                                                          text  \\\n",
              "55196                                                                              معرفة الكتاب تتخذ إجراءات بناءً على الأسباب   \n",
              "22728                                                                                   حتى الآن ، أتساءل عن مدى اقتناع حياتي.   \n",
              "52427                                                     ابتسامة طفل توسع الكون (D'un enfant le sourire - Agrandit l'univers)   \n",
              "78686    القول المأثور \"إذا كنت تريد القيام بشيء ما ، اسأل امرأة مشغولة\" هو اعتراف مباشر بالأبوة في معسكر التدريب على الكفاءة.   \n",
              "53500  بنفس الدرجة التي يصبح فيها فهمك لأفريكا وموقفك تجاهها أكثر إيجابية ، سيصبح فهمك وموقفك تجاه نفسك أكثر إيجابية أيضًا ...   \n",
              "\n",
              "       leng  \n",
              "55196    43  \n",
              "22728    38  \n",
              "52427    68  \n",
              "78686   117  \n",
              "53500   119  "
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G9uab_LhHB-g"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7CQN1NrFKl4a"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "myOS_f9k-TLg"
      },
      "source": [
        "Preparing data for model fine-tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AFSoPqGoY0xW"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p-T1qrFBqT6Y"
      },
      "outputs": [],
      "source": [
        "train, test = train_test_split(data,test_size=0.15, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQ2YVAXnqT8c",
        "outputId": "7ca685f7-f8c7-4faf-f042-0ccb6f160b83"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(73305, 2)"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYZb0FsCet9L",
        "outputId": "f0adac09-1344-4897-bd26-65381cb6d535"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['text', 'leng'], dtype='object')"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MFnHztAiqT-q",
        "outputId": "44887558-050e-454d-8afa-7846ef5035d3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(12937, 2)"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jtcU_3ssKtUr"
      },
      "source": [
        "dropping len column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pjxJAe3beraT"
      },
      "outputs": [],
      "source": [
        "train = train.drop('leng', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JFM1cM2Oerc0"
      },
      "outputs": [],
      "source": [
        "test = test.drop('leng', axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ePVL_Yb3YUVd"
      },
      "source": [
        "saving a text file for our datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "peZxoN3cell0"
      },
      "outputs": [],
      "source": [
        "train.to_csv(r'train_dataset.txt', header=None, index=None, sep=' ', mode='a')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tEhaKWY5elph"
      },
      "outputs": [],
      "source": [
        "test.to_csv(r'test_dataset.txt', header=None, index=None, sep=' ', mode='a')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "qB_mU-zNYG2D",
        "outputId": "d4ce48a0-ee6b-4ba7-8c48-222e213c27a1"
      },
      "outputs": [
        {
          "data": {
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              "      <th>85779</th>\n",
              "      <td>في السياسة البناءة ، هناك \"ضبط في كل مكان\" وفي سياسة هدامة ، هناك سياسة عدم العدالة.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32744</th>\n",
              "      <td>كلما علمت شعبك ألا يحتاج إليك ، زادت قيمتك.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10715</th>\n",
              "      <td>فالأفضل أن تبقى صامتًا لخطر الاعتقاد بأنك أحمق ، من أن تتكلم وتزيل كل شك في ذلك.</td>\n",
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            "text/plain": [
              "                                                                                       text\n",
              "85779  في السياسة البناءة ، هناك \"ضبط في كل مكان\" وفي سياسة هدامة ، هناك سياسة عدم العدالة.\n",
              "32744                                           كلما علمت شعبك ألا يحتاج إليك ، زادت قيمتك.\n",
              "10715      فالأفضل أن تبقى صامتًا لخطر الاعتقاد بأنك أحمق ، من أن تتكلم وتزيل كل شك في ذلك."
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
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        "train.head(3)"
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        "Now we can build our TextDataset( custom implementation of the Pytroch Dataset class implemented by the transformers library). Therefore we create a TextDataset instance with the tokenizer and the path to our datasets. We also create our data_collator, which is used in training to form a batch from our dataset."
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.23.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.13.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.13.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.9.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n"
          ]
        }
      ],
      "source": [
        "pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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              "Downloading:   0%|          | 0.00/1.94M [00:00<?, ?B/s]"
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"aubmindlab/aragpt2-base\")\n",
        "\n",
        "train_path = '/content/train_dataset.txt'\n",
        "test_path = '/content/test_dataset.txt'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zn4b6dtzaYyQ"
      },
      "outputs": [],
      "source": [
        "from transformers import TextDataset,DataCollatorForLanguageModeling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ThnG9H-gfu6L"
      },
      "outputs": [],
      "source": [
        "\n",
        "def load_dataset(train_path,test_path,tokenizer):\n",
        "    train_dataset = TextDataset(\n",
        "          tokenizer=tokenizer,\n",
        "          file_path=train_path,\n",
        "          block_size=128)\n",
        "\n",
        "    test_dataset = TextDataset(\n",
        "          tokenizer=tokenizer,\n",
        "          file_path=test_path,\n",
        "          block_size=128)\n",
        "\n",
        "    data_collator = DataCollatorForLanguageModeling(\n",
        "        tokenizer=tokenizer, mlm=False,\n",
        "    )\n",
        "    return train_dataset,test_dataset,data_collator\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WbP6mfVeL-IM"
      },
      "source": [
        "A DataCollator is a function that takes a list of samples from a Dataset and collate them into a batch, as a dictionary. of PyTorch/TensorFlow tensors or NumPy arrays."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cZoTWqHybDzW",
        "outputId": "32f86f2f-1e65-42b0-e5fd-b653e465b626"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/data/datasets/language_modeling.py:58: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py\n",
            "  FutureWarning,\n"
          ]
        }
      ],
      "source": [
        "train_dataset,test_dataset,data_collator = load_dataset(train_path,test_path,tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jMATc0Jfo1d"
      },
      "source": [
        "Initialize Trainer with TrainingArguments and GPT-2 model\n",
        "The Trainer class provides an API for feature-complete training. It is used in most of the example scripts from Huggingface. Before we can instantiate our Trainer we need to download our GPT-2 model and create TrainingArguments. The TrainingArguments are used to define the Hyperparameters, which we use in the training process like the learning_rate, num_train_epochs, or per_device_train_batch_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g-5VHvIebD2E"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer, TrainingArguments, AutoModelWithLMHead"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:1116: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f817a4650db4d66848395264f92e00f",
              "version_major": 2,
              "version_minor": 0
            },
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              "Downloading:   0%|          | 0.00/553M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = AutoModelWithLMHead.from_pretrained(\"aubmindlab/aragpt2-base\")\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./gpt2-arQuotes\", #The output directory\n",
        "    overwrite_output_dir=True, #overwrite the content of the output directory\n",
        "    num_train_epochs=3, # number of training epochs\n",
        "    per_device_train_batch_size=32, # batch size for training\n",
        "    per_device_eval_batch_size=64,  # batch size for evaluation\n",
        "    eval_steps = 400, # Number of update steps between two evaluations.\n",
        "    save_steps=800, # after # steps model is saved\n",
        "    warmup_steps=500,# number of warmup steps for learning rate scheduler\n",
        "    )\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "DEQAIrFIf0FF",
        "outputId": "69ee9be0-beef-4380-9dc0-b6364d1fec49"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 12324\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 32\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 1158\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1158' max='1158' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1158/1158 22:45, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>5.447200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>4.055000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to ./gpt2-arQuotes/checkpoint-800\n",
            "Configuration saved in ./gpt2-arQuotes/checkpoint-800/config.json\n",
            "Model weights saved in ./gpt2-arQuotes/checkpoint-800/pytorch_model.bin\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1158, training_loss=4.641399218834332, metrics={'train_runtime': 1368.8772, 'train_samples_per_second': 27.009, 'train_steps_per_second': 0.846, 'total_flos': 2415122251776000.0, 'train_loss': 4.641399218834332, 'epoch': 3.0})"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jVaK803f0HI",
        "outputId": "0a72cfdc-9638-4d73-ae37-5756e159b748"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to ./gpt2-arQuotes\n",
            "Configuration saved in ./gpt2-arQuotes/config.json\n",
            "Model weights saved in ./gpt2-arQuotes/pytorch_model.bin\n"
          ]
        }
      ],
      "source": [
        "trainer.save_model()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xeOdZCbfgQgc"
      },
      "source": [
        "To test the model we use another highlight of the transformers library called pipeline. Pipelines are objects that offer a simple API dedicated to several tasks, text-generation amongst others."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OMgc_6pPbD5B"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "McyKl60fbD8y",
        "outputId": "7253945b-0ffa-4b0e-b03c-7da2197b249a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "loading configuration file ./gpt2-arQuotes/config.json\n",
            "Model config GPT2Config {\n",
            "  \"_name_or_path\": \"./gpt2-arQuotes\",\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 0,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"reorder_and_upcast_attn\": false,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"scale_attn_by_inverse_layer_idx\": false,\n",
            "  \"scale_attn_weights\": true,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 5,\n",
            "      \"repetition_penalty\": 3.0,\n",
            "      \"top_p\": 0.95\n",
            "    }\n",
            "  },\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 64000\n",
            "}\n",
            "\n",
            "loading configuration file ./gpt2-arQuotes/config.json\n",
            "Model config GPT2Config {\n",
            "  \"_name_or_path\": \"./gpt2-arQuotes\",\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 0,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"reorder_and_upcast_attn\": false,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"scale_attn_by_inverse_layer_idx\": false,\n",
            "  \"scale_attn_weights\": true,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 5,\n",
            "      \"repetition_penalty\": 3.0,\n",
            "      \"top_p\": 0.95\n",
            "    }\n",
            "  },\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 64000\n",
            "}\n",
            "\n",
            "loading weights file ./gpt2-arQuotes/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing GPT2LMHeadModel.\n",
            "\n",
            "All the weights of GPT2LMHeadModel were initialized from the model checkpoint at ./gpt2-arQuotes.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use GPT2LMHeadModel for predictions without further training.\n",
            "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--aubmindlab--aragpt2-base/snapshots/e5f5983ece6f9546e77f7096c1ed06d11a4100fe/config.json\n",
            "Model config GPT2Config {\n",
            "  \"_name_or_path\": \"aubmindlab/aragpt2-base\",\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 0,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"reorder_and_upcast_attn\": false,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"scale_attn_by_inverse_layer_idx\": false,\n",
            "  \"scale_attn_weights\": true,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 5,\n",
            "      \"repetition_penalty\": 3.0,\n",
            "      \"top_p\": 0.95\n",
            "    }\n",
            "  },\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 64000\n",
            "}\n",
            "\n",
            "loading file vocab.json from cache at /root/.cache/huggingface/hub/models--aubmindlab--aragpt2-base/snapshots/e5f5983ece6f9546e77f7096c1ed06d11a4100fe/vocab.json\n",
            "loading file merges.txt from cache at /root/.cache/huggingface/hub/models--aubmindlab--aragpt2-base/snapshots/e5f5983ece6f9546e77f7096c1ed06d11a4100fe/merges.txt\n",
            "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--aubmindlab--aragpt2-base/snapshots/e5f5983ece6f9546e77f7096c1ed06d11a4100fe/tokenizer.json\n",
            "loading file added_tokens.json from cache at None\n",
            "loading file special_tokens_map.json from cache at None\n",
            "loading file tokenizer_config.json from cache at None\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--aubmindlab--aragpt2-base/snapshots/e5f5983ece6f9546e77f7096c1ed06d11a4100fe/config.json\n",
            "Model config GPT2Config {\n",
            "  \"_name_or_path\": \"aubmindlab/aragpt2-base\",\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 0,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"reorder_and_upcast_attn\": false,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"scale_attn_by_inverse_layer_idx\": false,\n",
            "  \"scale_attn_weights\": true,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 5,\n",
            "      \"repetition_penalty\": 3.0,\n",
            "      \"top_p\": 0.95\n",
            "    }\n",
            "  },\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 64000\n",
            "}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "quote = pipeline('text-generation',model='./gpt2-arQuotes', tokenizer='aubmindlab/aragpt2-base')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1lWo-pQbEA9",
        "outputId": "11f9e109-92b9-4363-e5b4-7b043d4a1b7d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1301: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 50 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  UserWarning,\n"
          ]
        }
      ],
      "source": [
        "result = quote('ركز على أهدافك وكن')[0]['generated_text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tTpcQHxEfu_h",
        "outputId": "af94376d-f896-4c62-cfb9-4934b5b476a0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ركز على أهدافك وكن صادقًا مع نفسك.\"\n",
            "\"أنت لا تعرف كيف يمكنك أن تعيش حياة سعيدة ، ولكن ما يهم هو كيف يمكن أن تكون سعيدا؟ \"\"إذا كنت تريد أن تصبح شخصً جيدً\n"
          ]
        }
      ],
      "source": [
        "print(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c9yrunC5gZDT"
      },
      "outputs": [],
      "source": [
        "def generate (text):\n",
        "  result = quote(text)[0]['generated_text']\n",
        "  print(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8T3Fj8anrHGb"
      },
      "outputs": [],
      "source": [
        "def generate_short (text):\n",
        "  res = quote(text)[0]['generated_text']\n",
        "  result = res.rsplit('.', 1)\n",
        "  final = result[0]\n",
        "  print(final)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NxnpoGLmgcuj",
        "outputId": "f4baf53b-90de-4553-921b-ef7917091ced"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "وجود أصدقاء جيدين هو أن تكون سعيدًا.\"\n",
            "\"إذا كنت لا تعرف ما إذا كان هناك شخص آخر في هذا العالم ، فأنت بحاجة إلى معرفة من أنت وما هي أحلامك؟ \"\"أريدك أن تعيش حياة سعيدة\n"
          ]
        }
      ],
      "source": [
        "generate('وجود أصدقاء جيدين هو')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-ZT00MMrp24",
        "outputId": "a6b75e00-3daf-4e00-88fc-1b699c7bf043"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "إذا كنت تريد أن تعيش حياة جيدة ، فأنت بحاجة إلى تغيير حياتك\n"
          ]
        }
      ],
      "source": [
        "generate_short('إذا كنت تريد أن تعيش حياة جيدة ، فأنت')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sIKPtN6Prp5e",
        "outputId": "2993546a-bc91-4d65-f745-319d1f205dce"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "تحتاج إلى مواصلة الكفاح وعيش حياة أفضل.\"\n",
            "\"إذا كنت تريد أن تكون سعيدًا ، فافعل ما ترغب فيه.... إذا كنت تريد ذلك ، فابحث عن شخص آخر في العالم الذي تعيش فيه\n"
          ]
        }
      ],
      "source": [
        "generate_short('تحتاج إلى مواصلة الكفاح و')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KFc-sRVBlC7G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S4V-JiNVqVeQ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hEcZ_2MZC7yZ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mk8SsC2xGA2q"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c-4VYjyYGA5C"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e5uWc2x2GA7a"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PNnS14xIC763"
      },
      "outputs": [],
      "source": [
        "pip install keybert"
      ]
    },
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            "[('تعيش', 0.6376), ('تريد', 0.5759), ('تحتاج', 0.5754), ('وعيش', 0.5556), ('فافعل', 0.5487)]\n"
          ]
        }
      ],
      "source": [
        "from keybert import KeyBERT"
      ]
    },
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        "def tri (list):\n",
        "  tri_list=[]\n",
        "  for elm in list:\n",
        "    if elm[1]>0.5:\n",
        "      tri_list.append(elm[0])\n",
        "  return tri_list[:5]\n"
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            "['تعيش', 'تريد', 'تحتاج', 'وعيش', 'فافعل']\n"
          ]
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        "doc = \"\"\" تحتاج إلى مواصلة الكفاح وعيش حياة أفضل\n",
        "إذا كنت تريد أن تكون سعيدًا ، فافعل ما ترغب فيه.... إذا كنت تريد ذلك ، فابحث عن شخص آخر في العالم الذي تعيش فيه \"\"\"\n",
        "kw_model = KeyBERT()\n",
        "keywords = kw_model.extract_keywords(doc)\n",
        "result = tri(keywords)\n",
        "print(result)"
      ]
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