arabic_poet_classifier.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L0SvEcoUFM3m"
      },
      "source": [
        "# Arabic Poet Classifier: Poet Detection from Poetry\n",
        "\n",
        "Arabic Poet Classifier that detects the poet from poetry"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets\n",
        "!pip install pyarabic\n",
        "!pip install tashaphyne\n",
        "\n",
        "!pip install nltk\n",
        "!pip install nltk scikit-learn\n",
        "\n",
        "!python -m nltk.downloader punkt\n",
        "!pip install Arabic-Stopwords\n",
        "!pip install transformers\n",
        "!pip install fasttext\n",
        "\n",
        "!pip install spacy-transformers\n"
      ],
      "metadata": {
        "id": "CdfM1uGrb134",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6e3a992-3c33-4e73-85e0-3cd41b95395c"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.19.1-py3-none-any.whl (542 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.14.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n",
            "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (14.0.2)\n",
            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.4)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.3.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.5)\n",
            "Requirement already satisfied: huggingface-hub>=0.21.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.23.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2024.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.19.1 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n",
            "Collecting pyarabic\n",
            "  Downloading PyArabic-0.6.15-py3-none-any.whl (126 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m126.4/126.4 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from pyarabic) (1.16.0)\n",
            "Installing collected packages: pyarabic\n",
            "Successfully installed pyarabic-0.6.15\n",
            "Collecting tashaphyne\n",
            "  Downloading Tashaphyne-0.3.6-py3-none-any.whl (251 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.5/251.5 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyarabic in /usr/local/lib/python3.10/dist-packages (from tashaphyne) (0.6.15)\n",
            "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from pyarabic->tashaphyne) (1.16.0)\n",
            "Installing collected packages: tashaphyne\n",
            "Successfully installed tashaphyne-0.3.6\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.4.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2023.12.25)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.4)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.4.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2023.12.25)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.4)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.4)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
            "/usr/lib/python3.10/runpy.py:126: RuntimeWarning: 'nltk.downloader' found in sys.modules after import of package 'nltk', but prior to execution of 'nltk.downloader'; this may result in unpredictable behaviour\n",
            "  warn(RuntimeWarning(msg))\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "Collecting Arabic-Stopwords\n",
            "  Downloading Arabic_Stopwords-0.4.3-py3-none-any.whl (360 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m360.5/360.5 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyarabic>=0.6.2 in /usr/local/lib/python3.10/dist-packages (from Arabic-Stopwords) (0.6.15)\n",
            "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from pyarabic>=0.6.2->Arabic-Stopwords) (1.16.0)\n",
            "Installing collected packages: Arabic-Stopwords\n",
            "Successfully installed Arabic-Stopwords-0.4.3\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.14.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n",
            "Collecting fasttext\n",
            "  Downloading fasttext-0.9.2.tar.gz (68 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m68.8/68.8 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pybind11>=2.2 (from fasttext)\n",
            "  Using cached pybind11-2.12.0-py3-none-any.whl (234 kB)\n",
            "Requirement already satisfied: setuptools>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from fasttext) (67.7.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from fasttext) (1.25.2)\n",
            "Building wheels for collected packages: fasttext\n",
            "  Building wheel for fasttext (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fasttext: filename=fasttext-0.9.2-cp310-cp310-linux_x86_64.whl size=4227137 sha256=fe604f2acc2953958d91e19bc0cba4e2ae13cfd66dffe8b1c3396f3b24ee8428\n",
            "  Stored in directory: /root/.cache/pip/wheels/a5/13/75/f811c84a8ab36eedbaef977a6a58a98990e8e0f1967f98f394\n",
            "Successfully built fasttext\n",
            "Installing collected packages: pybind11, fasttext\n",
            "Successfully installed fasttext-0.9.2 pybind11-2.12.0\n",
            "Collecting spacy-transformers\n",
            "  Downloading spacy_transformers-1.3.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (197 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m197.8/197.8 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<4.1.0,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from spacy-transformers) (3.7.4)\n",
            "Collecting transformers<4.37.0,>=3.4.0 (from spacy-transformers)\n",
            "  Downloading transformers-4.36.2-py3-none-any.whl (8.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from spacy-transformers) (2.3.0+cu121)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from spacy-transformers) (2.4.8)\n",
            "Collecting spacy-alignments<1.0.0,>=0.7.2 (from spacy-transformers)\n",
            "  Downloading spacy_alignments-0.9.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (313 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.0/314.0 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.10/dist-packages (from spacy-transformers) (1.25.2)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (1.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (1.0.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (2.0.8)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (3.0.9)\n",
            "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (8.2.3)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (1.1.2)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (2.0.10)\n",
            "Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (0.3.4)\n",
            "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (0.9.4)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (6.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (4.66.4)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (2.31.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (2.7.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (3.1.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (67.7.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (24.0)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4.1.0,>=3.5.0->spacy-transformers) (3.4.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->spacy-transformers) (3.14.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->spacy-transformers) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->spacy-transformers) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->spacy-transformers) (3.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->spacy-transformers) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.8.0->spacy-transformers)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->spacy-transformers) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->spacy-transformers)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m51.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers<4.37.0,>=3.4.0->spacy-transformers) (0.23.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers<4.37.0,>=3.4.0->spacy-transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<4.37.0,>=3.4.0->spacy-transformers) (2023.12.25)\n",
            "Collecting tokenizers<0.19,>=0.14 (from transformers<4.37.0,>=3.4.0->spacy-transformers)\n",
            "  Downloading tokenizers-0.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m69.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers<4.37.0,>=3.4.0->spacy-transformers) (0.4.3)\n",
            "Requirement already satisfied: language-data>=1.2 in /usr/local/lib/python3.10/dist-packages (from langcodes<4.0.0,>=3.2.0->spacy<4.1.0,>=3.5.0->spacy-transformers) (1.2.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<4.1.0,>=3.5.0->spacy-transformers) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.2 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<4.1.0,>=3.5.0->spacy-transformers) (2.18.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<4.1.0,>=3.5.0->spacy-transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<4.1.0,>=3.5.0->spacy-transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<4.1.0,>=3.5.0->spacy-transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<4.1.0,>=3.5.0->spacy-transformers) (2024.2.2)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<4.1.0,>=3.5.0->spacy-transformers) (0.7.11)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<4.1.0,>=3.5.0->spacy-transformers) (0.1.4)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<4.1.0,>=3.5.0->spacy-transformers) (8.1.7)\n",
            "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<4.1.0,>=3.5.0->spacy-transformers) (0.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<4.1.0,>=3.5.0->spacy-transformers) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->spacy-transformers) (1.3.0)\n",
            "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<4.1.0,>=3.5.0->spacy-transformers) (1.1.1)\n",
            "Installing collected packages: spacy-alignments, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, tokenizers, nvidia-cusolver-cu12, transformers, spacy-transformers\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.19.1\n",
            "    Uninstalling tokenizers-0.19.1:\n",
            "      Successfully uninstalled tokenizers-0.19.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.41.0\n",
            "    Uninstalling transformers-4.41.0:\n",
            "      Successfully uninstalled transformers-4.41.0\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 spacy-alignments-0.9.1 spacy-transformers-1.3.5 tokenizers-0.15.2 transformers-4.36.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "zH3yLMCg80w2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83ebb319-b47e-4622-d96d-ba38e9a26cc2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "import string\n",
        "import nltk\n",
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import spacy_transformers\n",
        "\n",
        "import arabicstopwords.arabicstopwords as stp\n",
        "from time import time\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from pandas import read_csv\n",
        "from pandas.plotting import scatter_matrix\n",
        "from matplotlib import pyplot\n",
        "\n",
        "from pyarabic.araby import normalize_hamza, normalize_alef\n",
        "from pyarabic.araby import normalize_ligature, strip_diacritics\n",
        "from arabicstopwords.arabicstopwords import stopwords_list\n",
        "from tashaphyne.stemming import ArabicLightStemmer\n",
        "from transformers import BertTokenizer\n",
        "\n",
        "import fasttext\n",
        "import fasttext.util\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.feature_selection import SelectKBest, chi2, mutual_info_classif\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import LabelBinarizer, LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler, MultiLabelBinarizer\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.metrics import precision_score, recall_score\n",
        "from sklearn.decomposition import PCA, LatentDirichletAllocation\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_sDmCvK88A4"
      },
      "source": [
        "# .1. Loading Dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ywFKqIM9CcW"
      },
      "source": [
        "## 1.1. Loading First Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "22vGQDCWM3xS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333,
          "referenced_widgets": [
            "a6531978d72747188732b8816a353bd3",
            "0f17c90a71ee4834ac325f5b296659ce",
            "fca089495b08484db1f0c94a6dc373b4",
            "2f6f71b4f7904b0b907bf3a0b8097b75",
            "b78b7372c7d246e6af214048b9d8a811",
            "8c4ab14eb53b41a484470b71af8caf9e",
            "52217e88772b4e77896bd59e60b108fc",
            "7387776b6bc445cf8d0e75a363188774",
            "48efe39871354af790122c85cfbe5df8",
            "57bb26fe75e04d13b91468db5e2242eb",
            "002afe5ccc78485b8db8087346c41a7f",
            "989f827e765342a3a6ec88ac84f82b9e",
            "ff92a4b612e1428b84630aaa42c3874d",
            "2538db0953ad49b9be7c8e2bcd9c5228",
            "1eb5a269a39c4e7f9f7fb8ad91bde9a2",
            "47571743aeda4e1e97c200164284c3c4",
            "1fdf386e6eef4356a5770d7856a8f4b8",
            "6f97c822a72a44958d244bce55ea39b3",
            "5b4895d5947241a58ab4065073511303",
            "cd044e504e0d4b299a43e7af4c5af3fc",
            "ceecb1fc7ea84508965ab67c5a25e5c6",
            "09a939325af54932b77869bad96b11bd",
            "5a3e129b917945a9b6b29c020f6e6e54",
            "98bf94896bff462591843b1e0791a99b",
            "abfc18f3628d4454ad0b3cb024c6fb59",
            "d56108542d9d42be86d8c0f4f518b6be",
            "d66444be2c01427e959f3db3308f6019",
            "75ab5d089e3f4bf0b7c38c52d5a4d3bd",
            "d3ef49d1121745f7949c58032709d9f1",
            "2245f414aa0c4d03a11a71c42a88d899",
            "6791b0cd9d5c403dacc37719437ee4a9",
            "711eebfd54c14a2aa643397698b84111",
            "cecb1bf2ad494fc78f20203f00c23e57",
            "f88c1682a7cc41429c7c69504f516512",
            "61e9375b1f73432b87fff3874292d085",
            "6bf471d6c3da444198bacd35b99c0957",
            "73e98c6eaf2a4722b8869b60b676cec8",
            "b92cf270f1104839ab5292f33f13e133",
            "8f84c955c60b4a5c9fd23ec4d882b150",
            "9ee3ee615cc14e65a052c622f853836e",
            "37ffaaa7e1b143d8b227d24069bcc591",
            "7112db89ce9b441381f9a4d3a0bb4bf7",
            "bda083f6c84d4f11b472578029f7280b",
            "012d2342f8f841418f6a5904fa7fd5b1",
            "72d9ca1de06c4f609f03ac68cda3dbc6",
            "7497ee908ca946af99e040ff375edebc",
            "de99e62803ef4cb4a7719bcc7a57a0df",
            "acdb663ad85d499487879813ee2d32de",
            "9274322dda3642a9a99803efb36dd260",
            "a3fd610b58d2469fbc5e91de9d63b6e8",
            "dda09c5e95594c40b1c5dfc2decb0e94",
            "d2a6e2956c8740b3805e4adb766a5ec0",
            "b78e9fe92e784bf9b1877e1c9be8a6a5",
            "b71b10059dba455ab2f2c4828ea5ce79",
            "6c8464f187e9469a93e22d87cca5b14d"
          ]
        },
        "outputId": "20b5eab9-8260-4c04-8599-03eacccf0818"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6531978d72747188732b8816a353bd3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Repo card metadata block was not found. Setting CardData to empty.\n",
            "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/17.4k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "989f827e765342a3a6ec88ac84f82b9e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/120M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a3e129b917945a9b6b29c020f6e6e54"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/144M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f88c1682a7cc41429c7c69504f516512"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/254630 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72d9ca1de06c4f609f03ac68cda3dbc6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset size: 254630\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset_dict = load_dataset(\"arbml/ashaar\", download_mode=\"force_redownload\")\n",
        "\n",
        "print(f\"Train dataset size: {len(dataset_dict['train'])}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "WCyAcTT3ZoZH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67ecdb64-4ae1-467b-ba73-d617c89dfa90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(254630, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Ashaar Dataset as Dataframe\n",
        "dataset_ashaar = pd.DataFrame(dataset_dict['train'])\n",
        "dataset_ashaar.head()\n",
        "dataset_ashaar.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EicLwEbIgbiT"
      },
      "source": [
        "from Ashaar dataset we are using four features besides the peot name as target.\n",
        "\n",
        "Current Features:\n",
        "- poem meter\n",
        "- poem verses\n",
        "- poem theme\n",
        "- poet era\n",
        "\n",
        "Target:\n",
        "- poet name"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWchKXh8Na58"
      },
      "source": [
        "## 1.2. Create Project dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "OdO-mjBqNaRb"
      },
      "outputs": [],
      "source": [
        "dataset_project = dataset_ashaar.drop(['poem title', 'poem url', 'poet description', 'poet url', 'poet location', 'poem description', 'poem language type'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_project.head()"
      ],
      "metadata": {
        "id": "7fdOyFYJ9lFn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "03644ecc-9d4d-434e-ff4b-405420ce9a6e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        poem meter                                        poem verses  \\\n",
              "0       بحر الخفيف  [أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...   \n",
              "1  بحر مجزوء الرمل  [مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...   \n",
              "2       بحر البسيط  [العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...   \n",
              "3       بحر الكامل  [لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...   \n",
              "4       بحر الوافر  [يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...   \n",
              "\n",
              "    poem theme         poet name        poet era  \n",
              "0  قصيدة دينية  الامير منجك باشا  العصر العثماني  \n",
              "1  قصيدة دينية  الامير منجك باشا  العصر العثماني  \n",
              "2     قصيدة ذم  الامير منجك باشا  العصر العثماني  \n",
              "3   قصيدة عامه  الامير منجك باشا  العصر العثماني  \n",
              "4   قصيدة عامه  الامير منجك باشا  العصر العثماني  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bdd9ff89-d2cc-4019-be21-1ef9bb2ac820\" 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>poem meter</th>\n",
              "      <th>poem verses</th>\n",
              "      <th>poem theme</th>\n",
              "      <th>poet name</th>\n",
              "      <th>poet era</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>بحر الخفيف</td>\n",
              "      <td>[أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>الامير منجك باشا</td>\n",
              "      <td>العصر العثماني</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>بحر مجزوء الرمل</td>\n",
              "      <td>[مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>الامير منجك باشا</td>\n",
              "      <td>العصر العثماني</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>بحر البسيط</td>\n",
              "      <td>[العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...</td>\n",
              "      <td>قصيدة ذم</td>\n",
              "      <td>الامير منجك باشا</td>\n",
              "      <td>العصر العثماني</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>بحر الكامل</td>\n",
              "      <td>[لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>الامير منجك باشا</td>\n",
              "      <td>العصر العثماني</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>بحر الوافر</td>\n",
              "      <td>[يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>الامير منجك باشا</td>\n",
              "      <td>العصر العثماني</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bdd9ff89-d2cc-4019-be21-1ef9bb2ac820')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-bdd9ff89-d2cc-4019-be21-1ef9bb2ac820 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-bdd9ff89-d2cc-4019-be21-1ef9bb2ac820');\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",
              "\n",
              "\n",
              "<div id=\"df-58a922d2-4ca6-4041-a178-9ae646a7acb5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58a922d2-4ca6-4041-a178-9ae646a7acb5')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-58a922d2-4ca6-4041-a178-9ae646a7acb5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset_project"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQOCEt149Vi1"
      },
      "source": [
        "# .2. Data Preprocessing / Processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBagoizolnma"
      },
      "source": [
        "## 2.1. Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "J-AjBZFGCZBp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdeaa1d3-73bd-4bc0-b9d3-7c771ce2d909"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values in the DataFrame:\n",
            "poem meter     101277\n",
            "poem verses         0\n",
            "poem theme     187110\n",
            "poet name           0\n",
            "poet era       107209\n",
            "dtype: int64\n",
            "---------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "# Check for missing values in the DataFrame\n",
        "missing_values = dataset_project.isnull().sum()\n",
        "\n",
        "# Print the missing values\n",
        "print(\"Missing values in the DataFrame:\")\n",
        "print(missing_values)\n",
        "print('---------------------------------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "4Z1tLtRpCaH2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5d9ee4b-55f0-43c0-f429-3f941ccc4755"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values in the DataFrame after removing rows with missing values:\n",
            "poem meter     0\n",
            "poem verses    0\n",
            "poem theme     0\n",
            "poet name      0\n",
            "poet era       0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54701"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Remove rows with any missing values\n",
        "dataset_project = dataset_project.dropna()\n",
        "\n",
        "# Check if any missing values remain\n",
        "missing_values = dataset_project.isnull().sum()\n",
        "print(\"Missing values in the DataFrame after removing rows with missing values:\")\n",
        "print(missing_values)\n",
        "\n",
        "len(dataset_project)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features and target\n",
        "features = dataset_project.drop('poet name', axis=1)\n",
        "target = dataset_project['poet name']"
      ],
      "metadata": {
        "id": "Yu1ZBU6AwcRA"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7GU5ycivm0Ap"
      },
      "source": [
        "## 2.2. Normalization\n",
        "\n",
        "- Strip Deiacritics (Done)\n",
        "- Strip Elongation (Done)\n",
        "- Normalize Hamza\n",
        "- Normalize Alef\n",
        "- Normalize Yah"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "haYTo4FbpFcA"
      },
      "outputs": [],
      "source": [
        "# Function to preprocess text\n",
        "\n",
        "def preprocess_text(row):\n",
        "\n",
        "  # text = row['poem verses']\n",
        "  # text = text.join(text)\n",
        "\n",
        "  # Combine text columns into a single string\n",
        "  text = \" \".join([str(row['poem meter']), str(row['poem verses']), str(row['poem theme']), str(row['poet era'])])\n",
        "\n",
        "  # Clean and normalize the text\n",
        "  text = text.strip()  # Remove extra spaces\n",
        "  text = normalize_hamza(text)  # Normalize Hamza\n",
        "  text = normalize_alef(text)   # Normalize Alef\n",
        "  text = normalize_ligature(text)  # Normalize Ligature\n",
        "  text = strip_diacritics(text).replace(\"ى\", \"ي\") # Normalize Yah\n",
        "  text = text.translate(str.maketrans('', '', string.punctuation))  # Remove punctuation\n",
        "\n",
        "\n",
        "  return text\n",
        "\n",
        "features['Normalized Poem'] = features.apply(lambda row: preprocess_text(row), axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features.head()"
      ],
      "metadata": {
        "id": "ynkTOvb8cPjU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "141bbcc3-ae0c-4144-d12f-26f544a70f77"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        poem meter                                        poem verses  \\\n",
              "0       بحر الخفيف  [أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...   \n",
              "1  بحر مجزوء الرمل  [مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...   \n",
              "2       بحر البسيط  [العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...   \n",
              "3       بحر الكامل  [لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...   \n",
              "4       بحر الوافر  [يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...   \n",
              "\n",
              "    poem theme        poet era  \\\n",
              "0  قصيدة دينية  العصر العثماني   \n",
              "1  قصيدة دينية  العصر العثماني   \n",
              "2     قصيدة ذم  العصر العثماني   \n",
              "3   قصيدة عامه  العصر العثماني   \n",
              "4   قصيدة عامه  العصر العثماني   \n",
              "\n",
              "                                     Normalized Poem  \n",
              "0  بحر الخفيف ءصبح الملك للذي فطر الخل ق بتقدير ل...  \n",
              "1  بحر مجزوء الرمل من ءي مولا ارتجي ولاي باب التج...  \n",
              "2  بحر البسيط العبد عبدك يا من ءنت سيده وليس غيرك...  \n",
              "3  بحر الكامل لو كنت ءطمع بالمنام توهما لسالت طيف...  \n",
              "4  بحر الوافر يعد علي ءنفاسي ذنوبا ءذا ما قلت ءفد...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e920563c-7818-4815-b615-c7964225a2cf\" 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>poem meter</th>\n",
              "      <th>poem verses</th>\n",
              "      <th>poem theme</th>\n",
              "      <th>poet era</th>\n",
              "      <th>Normalized Poem</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>بحر الخفيف</td>\n",
              "      <td>[أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الخفيف ءصبح الملك للذي فطر الخل ق بتقدير ل...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>بحر مجزوء الرمل</td>\n",
              "      <td>[مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر مجزوء الرمل من ءي مولا ارتجي ولاي باب التج...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>بحر البسيط</td>\n",
              "      <td>[العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...</td>\n",
              "      <td>قصيدة ذم</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر البسيط العبد عبدك يا من ءنت سيده وليس غيرك...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>بحر الكامل</td>\n",
              "      <td>[لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الكامل لو كنت ءطمع بالمنام توهما لسالت طيف...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>بحر الوافر</td>\n",
              "      <td>[يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الوافر يعد علي ءنفاسي ذنوبا ءذا ما قلت ءفد...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e920563c-7818-4815-b615-c7964225a2cf')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-e920563c-7818-4815-b615-c7964225a2cf 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-e920563c-7818-4815-b615-c7964225a2cf');\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",
              "\n",
              "\n",
              "<div id=\"df-1c507a17-d9b4-4d1e-8098-195f8172d32c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c507a17-d9b4-4d1e-8098-195f8172d32c')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1c507a17-d9b4-4d1e-8098-195f8172d32c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "features",
              "summary": "{\n  \"name\": \"features\",\n  \"rows\": 54701,\n  \"fields\": [\n    {\n      \"column\": \"poem meter\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 66,\n        \"samples\": [\n          \"\\u0628\\u062d\\u0631 \\u0645\\u0646\\u0647\\u0648\\u0643 \\u0627\\u0644\\u0628\\u0633\\u064a\\u0637\",\n          \"\\u0628\\u062d\\u0631 \\u0645\\u062c\\u0632\\u0648\\u0621 \\u0627\\u0644\\u0647\\u0632\\u062c\",\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u062e\\u0641\\u064a\\u0641\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poem verses\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poem theme\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u062f\\u064a\\u0646\\u064a\\u0629\",\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u0630\\u0645\",\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u063a\\u0632\\u0644\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poet era\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0639\\u062b\\u0645\\u0627\\u0646\\u064a\",\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0639\\u0628\\u0627\\u0633\\u064a\",\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0645\\u0645\\u0644\\u0648\\u0643\\u064a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Normalized Poem\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54701,\n        \"samples\": [\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u062e\\u0641\\u064a\\u0641 \\u064a\\u0627 \\u0639\\u0630\\u0627\\u0631\\u0627 \\u0627\\u0644\\u062c\\u0645\\u0627\\u0644 \\u0648\\u0627\\u0644\\u062d\\u0628 \\u0648\\u0627\\u0644\\u0621\\u062d\\u0644\\u0627\\u0645 \\u0628\\u0644 \\u064a\\u0627 \\u0628\\u0647\\u0627\\u0621 \\u0647\\u0630\\u0627 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u062e\\u0644\\u0642 \\u0627\\u0644\\u0628\\u0644\\u0628\\u0644 \\u0627\\u0644\\u062c\\u0645\\u064a\\u0644 \\u0644\\u064a\\u0634\\u062f\\u0648 \\u0648\\u062e\\u0644\\u0642\\u062a\\u0646 \\u0644\\u0644\\u063a\\u0631\\u0627\\u0645 \\u0627\\u0644\\u0633\\u0639\\u064a\\u062f \\u0648\\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0627\\u0644\\u0631\\u062d\\u064a\\u0628 \\u0643\\u0627\\u0644\\u0642\\u0628\\u0631 \\u0644\\u0648\\u0644\\u0627 \\u0645\\u0627 \\u062a\\u062c\\u0644\\u064a\\u0646 \\u0645\\u0646 \\u0642\\u0637\\u0648\\u0628 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0648\\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0627\\u0644\\u062a\\u064a \\u062a\\u062e\\u0631 \\u0644\\u0647\\u0627 \\u0627\\u0644\\u0621\\u062d\\u0644\\u0627\\u0645 \\u0645\\u0648\\u062a \\u0645\\u062b\\u0642\\u0644 \\u0643\\u0627\\u0644\\u0642\\u064a\\u0648\\u062f \\u0648\\u0627\\u0644\\u0634\\u0628\\u0627\\u0628 \\u0627\\u0644\\u062d\\u0628\\u064a\\u0628 \\u0634\\u064a\\u062e\\u0648\\u062e\\u0629 \\u062a\\u0633\\u0639\\u0627 \\u0621\\u0644\\u0627 \\u0627\\u0644\\u0645\\u0648\\u062a \\u0641\\u064a \\u0637\\u0631\\u064a\\u0642 \\u0643\\u0621\\u0648\\u062f \\u0648\\u0627\\u0644\\u0631\\u0628\\u064a\\u0639 \\u0627\\u0644\\u062c\\u0645\\u064a\\u0644 \\u0641\\u064a \\u0647\\u0627\\u062a\\u0647 \\u0627\\u0644\\u062f\\u0646\\u064a\\u0627 \\u062e\\u0631\\u064a\\u0641 \\u064a\\u0630\\u0648\\u064a \\u0631\\u0641\\u064a\\u0641 \\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0648\\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0627\\u0644\\u0639\\u0630\\u0627\\u0628 \\u0641\\u064a \\u0636\\u0641\\u0629 \\u0627\\u0644\\u062c\\u062f\\u0648\\u0644 \\u0634\\u0648\\u0643 \\u0645\\u0635\\u0641\\u062d \\u0628\\u0627\\u0644\\u062d\\u062f\\u064a\\u062f \\u0648\\u0627\\u0644\\u0637\\u064a\\u0648\\u0631 \\u0627\\u0644\\u062a\\u064a \\u062a\\u063a\\u0646\\u064a \\u0648\\u062a\\u0642\\u0636\\u064a \\u0639\\u064a\\u0634\\u0647\\u0627 \\u0641\\u064a \\u062a\\u0631\\u0646\\u0645 \\u0648\\u063a\\u0631\\u064a\\u062f \\u0621\\u0646\\u0647\\u0627 \\u0641\\u064a \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u062a\\u0634\\u0643\\u0648 \\u0621\\u0644\\u0627 \\u0627\\u0644\\u0621\\u064a\\u0627\\u0645 \\u0639\\u0628\\u0621 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0628\\u0627\\u0644\\u062a\\u063a\\u0631\\u064a\\u062f \\u0648\\u0627\\u0644\\u0621\\u0646\\u0627\\u0634\\u064a\\u062f \\u0621\\u0646\\u0647\\u0627 \\u0634\\u0647\\u0642\\u0627\\u062a \\u062a\\u062a\\u0634\\u0638\\u0627 \\u0645\\u0646 \\u0643\\u0644 \\u0642\\u0644\\u0628 \\u0639\\u0645\\u064a\\u062f \\u0635\\u0648\\u0631\\u0629 \\u0644\\u0644\\u0648\\u062c\\u0648\\u062f \\u0634\\u0648\\u0647\\u0627\\u0621 \\u0644\\u0648\\u0644\\u0627 \\u0634\\u0641\\u0642 \\u0627\\u0644\\u062d\\u0633\\u0646 \\u0641\\u0648\\u0642 \\u062a\\u0644\\u0643 \\u0627\\u0644\\u062e\\u062f\\u0648\\u062f \\u064a\\u0627 \\u0632\\u0647\\u0648\\u0631 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0644\\u0644\\u062d\\u0628 \\u0621\\u0646\\u062a\\u0646 \\u0648\\u0644\\u0643\\u0646\\u0647 \\u0645\\u062e\\u064a\\u0641 \\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0641\\u0633\\u0628\\u064a\\u0644 \\u0627\\u0644\\u063a\\u0631\\u0627\\u0645 \\u062c\\u0645 \\u0627\\u0644\\u0645\\u0647\\u0627\\u0648\\u064a \\u0648\\u0627\\u0641\\u0631 \\u0627\\u0644\\u0647\\u0648\\u0644 \\u0645\\u0633\\u062a\\u0631\\u0627\\u0628 \\u0627\\u0644\\u0635\\u0639\\u064a\\u062f \\u0631\\u063a\\u0645 \\u0645\\u0627 \\u0641\\u064a\\u0647 \\u0645\\u0646 \\u062c\\u0645\\u0627\\u0644 \\u0648\\u0641\\u0646 \\u0639\\u0628\\u0642\\u0631\\u064a \\u0645\\u0627 \\u0621\\u0646 \\u0644\\u0647 \\u0645\\u0646 \\u0645\\u0632\\u064a\\u062f \\u0648\\u0621\\u0646\\u0627\\u0634\\u064a\\u062f \\u062a\\u0633\\u0643\\u0631 \\u0627\\u0644\\u0645\\u0644\\u0621 \\u0627\\u0644\\u0621\\u0639\\u0644\\u0627 \\u0648\\u062a\\u0634\\u062c\\u064a \\u062c\\u0648\\u0627\\u0646\\u062d \\u0627\\u0644\\u062c\\u0644\\u0645\\u0648\\u062f \\u0648\\u0621\\u0631\\u064a\\u062c \\u064a\\u0643\\u0627\\u062f \\u064a\\u0630\\u0647\\u0628 \\u0628\\u0627\\u0644\\u0621\\u0644\\u0628\\u0627\\u0628 \\u0645\\u0627 \\u0628\\u064a\\u0646 \\u063a\\u0627\\u0645\\u0636 \\u0648\\u0634\\u062f\\u064a\\u062f \\u0648\\u0633\\u0628\\u064a\\u0644 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0631\\u062d\\u0628 \\u0648\\u0621\\u0646\\u062a\\u0646 \\u0627\\u0644\\u0644\\u0648\\u0627\\u062a\\u064a \\u062a\\u0641\\u0631\\u0634\\u0646\\u0647 \\u0628\\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0621\\u0646 \\u0621\\u0631\\u062f\\u062a\\u0646 \\u0621\\u0646 \\u064a\\u0643\\u0648\\u0646 \\u0628\\u0647\\u064a\\u062c\\u0627 \\u0631\\u0627\\u0621\\u0639 \\u0627\\u0644\\u0633\\u062d\\u0631 \\u0630\\u0627 \\u062c\\u0645\\u0627\\u0644 \\u0641\\u0631\\u064a\\u062f \\u0621\\u0648 \\u0628\\u0634\\u0648\\u0643 \\u064a\\u062f\\u0645\\u064a \\u0627\\u0644\\u0641\\u0636\\u064a\\u0644\\u0629 \\u0648\\u0627\\u0644\\u062d\\u0628 \\u0648\\u064a\\u0642\\u0636\\u064a \\u0639\\u0644\\u0627 \\u0628\\u0647\\u0627\\u0621 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0621\\u0646 \\u0621\\u0631\\u062f\\u062a\\u0646 \\u0621\\u0646 \\u064a\\u0643\\u0648\\u0646 \\u0634\\u0646\\u064a\\u0639\\u0627 \\u0645\\u0638\\u0644\\u0645 \\u0627\\u0644\\u0621\\u0641\\u0642 \\u0645\\u064a\\u062a \\u0627\\u0644\\u062a\\u063a\\u0631\\u064a\\u062f \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0639\\u0627\\u0645\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u062d\\u062f\\u064a\\u062b\",\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u0633\\u0631\\u064a\\u0639 \\u0646\\u0648\\u0627\\u0644\\u0643 \\u0627\\u0644\\u0633\\u0639\\u062f\\u064a \\u064a\\u0627 \\u0633\\u064a\\u062f\\u064a \\u0621\\u0631\\u062c\\u0648 \\u0639\\u0644\\u0627 \\u0639\\u0627\\u062f\\u0627\\u062a\\u0647 \\u0645\\u0631\\u0628\\u0639\\u0647 \\u0644\\u064a \\u0621\\u0634\\u0647\\u0631 \\u0621\\u0631\\u0628\\u0639\\u0629 \\u0621\\u062e\\u0631\\u062a \\u0641\\u062d\\u0638\\u064a \\u0627\\u0644\\u0645\\u0634\\u0621\\u0648\\u0645 \\u0628\\u0627\\u0644\\u0621\\u0631\\u0628\\u0639\\u0647 \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0645\\u0645\\u0644\\u0648\\u0643\\u064a\",\n          \"\\u0628\\u062d\\u0631 \\u0645\\u062c\\u0632\\u0648\\u0621 \\u0627\\u0644\\u0633\\u0631\\u064a\\u0639 \\u0644\\u0627 \\u062a\\u0639\\u062c\\u0628\\u064a \\u0621\\u0646 \\u062a\\u0631\\u064a \\u062c\\u0633\\u0645\\u064a \\u0646\\u062d\\u064a\\u0644\\u0627 \\u064a\\u0634\\u0641 \\u0648\\u0643\\u0627\\u0646 \\u0645\\u0627\\u0621 \\u0627\\u0644\\u0635\\u0628\\u0627 \\u0639\\u0646 \\u0633\\u0642\\u064a\\u0647 \\u0644\\u0627 \\u064a\\u0643\\u0641 \\u0639\\u0631\\u0636\\u062a\\u0647 \\u0644\\u0644\\u0647\\u0648\\u0627\\u0627 \\u0641\\u0645\\u0627 \\u0644\\u0647 \\u0644\\u0627 \\u064a\\u062c\\u0641 \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u062d\\u062f\\u064a\\u062b\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DT6B01gwm-Rn"
      },
      "source": [
        "## 2.3. Tokenization\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Z3YD7C2wqtWz"
      },
      "outputs": [],
      "source": [
        "# Function to tokenize text\n",
        "def tokenize_text(text):\n",
        "    return word_tokenize(text)\n",
        "\n",
        "features['Tokens'] = features['Normalized Poem'].apply(tokenize_text)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "features.head()"
      ],
      "metadata": {
        "id": "uV5cgxjTcO7I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "02b3f70b-741f-467c-c74c-91b17655ae9e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        poem meter                                        poem verses  \\\n",
              "0       بحر الخفيف  [أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...   \n",
              "1  بحر مجزوء الرمل  [مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...   \n",
              "2       بحر البسيط  [العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...   \n",
              "3       بحر الكامل  [لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...   \n",
              "4       بحر الوافر  [يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...   \n",
              "\n",
              "    poem theme        poet era  \\\n",
              "0  قصيدة دينية  العصر العثماني   \n",
              "1  قصيدة دينية  العصر العثماني   \n",
              "2     قصيدة ذم  العصر العثماني   \n",
              "3   قصيدة عامه  العصر العثماني   \n",
              "4   قصيدة عامه  العصر العثماني   \n",
              "\n",
              "                                     Normalized Poem  \\\n",
              "0  بحر الخفيف ءصبح الملك للذي فطر الخل ق بتقدير ل...   \n",
              "1  بحر مجزوء الرمل من ءي مولا ارتجي ولاي باب التج...   \n",
              "2  بحر البسيط العبد عبدك يا من ءنت سيده وليس غيرك...   \n",
              "3  بحر الكامل لو كنت ءطمع بالمنام توهما لسالت طيف...   \n",
              "4  بحر الوافر يعد علي ءنفاسي ذنوبا ءذا ما قلت ءفد...   \n",
              "\n",
              "                                              Tokens  \n",
              "0  [بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, ق,...  \n",
              "1  [بحر, مجزوء, الرمل, من, ءي, مولا, ارتجي, ولاي,...  \n",
              "2  [بحر, البسيط, العبد, عبدك, يا, من, ءنت, سيده, ...  \n",
              "3  [بحر, الكامل, لو, كنت, ءطمع, بالمنام, توهما, ل...  \n",
              "4  [بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, ما...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e337c450-9546-4eb8-9abb-645d198a182e\" 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>poem meter</th>\n",
              "      <th>poem verses</th>\n",
              "      <th>poem theme</th>\n",
              "      <th>poet era</th>\n",
              "      <th>Normalized Poem</th>\n",
              "      <th>Tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>بحر الخفيف</td>\n",
              "      <td>[أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الخفيف ءصبح الملك للذي فطر الخل ق بتقدير ل...</td>\n",
              "      <td>[بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, ق,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>بحر مجزوء الرمل</td>\n",
              "      <td>[مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر مجزوء الرمل من ءي مولا ارتجي ولاي باب التج...</td>\n",
              "      <td>[بحر, مجزوء, الرمل, من, ءي, مولا, ارتجي, ولاي,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>بحر البسيط</td>\n",
              "      <td>[العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...</td>\n",
              "      <td>قصيدة ذم</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر البسيط العبد عبدك يا من ءنت سيده وليس غيرك...</td>\n",
              "      <td>[بحر, البسيط, العبد, عبدك, يا, من, ءنت, سيده, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>بحر الكامل</td>\n",
              "      <td>[لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الكامل لو كنت ءطمع بالمنام توهما لسالت طيف...</td>\n",
              "      <td>[بحر, الكامل, لو, كنت, ءطمع, بالمنام, توهما, ل...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>بحر الوافر</td>\n",
              "      <td>[يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الوافر يعد علي ءنفاسي ذنوبا ءذا ما قلت ءفد...</td>\n",
              "      <td>[بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, ما...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e337c450-9546-4eb8-9abb-645d198a182e')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-e337c450-9546-4eb8-9abb-645d198a182e 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-e337c450-9546-4eb8-9abb-645d198a182e');\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",
              "\n",
              "\n",
              "<div id=\"df-bc1ead31-e9ae-4474-84fc-8dbbda790e4c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bc1ead31-e9ae-4474-84fc-8dbbda790e4c')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bc1ead31-e9ae-4474-84fc-8dbbda790e4c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "features",
              "summary": "{\n  \"name\": \"features\",\n  \"rows\": 54701,\n  \"fields\": [\n    {\n      \"column\": \"poem meter\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 66,\n        \"samples\": [\n          \"\\u0628\\u062d\\u0631 \\u0645\\u0646\\u0647\\u0648\\u0643 \\u0627\\u0644\\u0628\\u0633\\u064a\\u0637\",\n          \"\\u0628\\u062d\\u0631 \\u0645\\u062c\\u0632\\u0648\\u0621 \\u0627\\u0644\\u0647\\u0632\\u062c\",\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u062e\\u0641\\u064a\\u0641\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poem verses\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poem theme\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u062f\\u064a\\u0646\\u064a\\u0629\",\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u0630\\u0645\",\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u063a\\u0632\\u0644\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poet era\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0639\\u062b\\u0645\\u0627\\u0646\\u064a\",\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0639\\u0628\\u0627\\u0633\\u064a\",\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0645\\u0645\\u0644\\u0648\\u0643\\u064a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Normalized Poem\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54701,\n        \"samples\": [\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u062e\\u0641\\u064a\\u0641 \\u064a\\u0627 \\u0639\\u0630\\u0627\\u0631\\u0627 \\u0627\\u0644\\u062c\\u0645\\u0627\\u0644 \\u0648\\u0627\\u0644\\u062d\\u0628 \\u0648\\u0627\\u0644\\u0621\\u062d\\u0644\\u0627\\u0645 \\u0628\\u0644 \\u064a\\u0627 \\u0628\\u0647\\u0627\\u0621 \\u0647\\u0630\\u0627 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u062e\\u0644\\u0642 \\u0627\\u0644\\u0628\\u0644\\u0628\\u0644 \\u0627\\u0644\\u062c\\u0645\\u064a\\u0644 \\u0644\\u064a\\u0634\\u062f\\u0648 \\u0648\\u062e\\u0644\\u0642\\u062a\\u0646 \\u0644\\u0644\\u063a\\u0631\\u0627\\u0645 \\u0627\\u0644\\u0633\\u0639\\u064a\\u062f \\u0648\\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0627\\u0644\\u0631\\u062d\\u064a\\u0628 \\u0643\\u0627\\u0644\\u0642\\u0628\\u0631 \\u0644\\u0648\\u0644\\u0627 \\u0645\\u0627 \\u062a\\u062c\\u0644\\u064a\\u0646 \\u0645\\u0646 \\u0642\\u0637\\u0648\\u0628 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0648\\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0627\\u0644\\u062a\\u064a \\u062a\\u062e\\u0631 \\u0644\\u0647\\u0627 \\u0627\\u0644\\u0621\\u062d\\u0644\\u0627\\u0645 \\u0645\\u0648\\u062a \\u0645\\u062b\\u0642\\u0644 \\u0643\\u0627\\u0644\\u0642\\u064a\\u0648\\u062f \\u0648\\u0627\\u0644\\u0634\\u0628\\u0627\\u0628 \\u0627\\u0644\\u062d\\u0628\\u064a\\u0628 \\u0634\\u064a\\u062e\\u0648\\u062e\\u0629 \\u062a\\u0633\\u0639\\u0627 \\u0621\\u0644\\u0627 \\u0627\\u0644\\u0645\\u0648\\u062a \\u0641\\u064a \\u0637\\u0631\\u064a\\u0642 \\u0643\\u0621\\u0648\\u062f \\u0648\\u0627\\u0644\\u0631\\u0628\\u064a\\u0639 \\u0627\\u0644\\u062c\\u0645\\u064a\\u0644 \\u0641\\u064a \\u0647\\u0627\\u062a\\u0647 \\u0627\\u0644\\u062f\\u0646\\u064a\\u0627 \\u062e\\u0631\\u064a\\u0641 \\u064a\\u0630\\u0648\\u064a \\u0631\\u0641\\u064a\\u0641 \\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0648\\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0627\\u0644\\u0639\\u0630\\u0627\\u0628 \\u0641\\u064a \\u0636\\u0641\\u0629 \\u0627\\u0644\\u062c\\u062f\\u0648\\u0644 \\u0634\\u0648\\u0643 \\u0645\\u0635\\u0641\\u062d \\u0628\\u0627\\u0644\\u062d\\u062f\\u064a\\u062f \\u0648\\u0627\\u0644\\u0637\\u064a\\u0648\\u0631 \\u0627\\u0644\\u062a\\u064a \\u062a\\u063a\\u0646\\u064a \\u0648\\u062a\\u0642\\u0636\\u064a \\u0639\\u064a\\u0634\\u0647\\u0627 \\u0641\\u064a \\u062a\\u0631\\u0646\\u0645 \\u0648\\u063a\\u0631\\u064a\\u062f \\u0621\\u0646\\u0647\\u0627 \\u0641\\u064a \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u062a\\u0634\\u0643\\u0648 \\u0621\\u0644\\u0627 \\u0627\\u0644\\u0621\\u064a\\u0627\\u0645 \\u0639\\u0628\\u0621 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0628\\u0627\\u0644\\u062a\\u063a\\u0631\\u064a\\u062f \\u0648\\u0627\\u0644\\u0621\\u0646\\u0627\\u0634\\u064a\\u062f \\u0621\\u0646\\u0647\\u0627 \\u0634\\u0647\\u0642\\u0627\\u062a \\u062a\\u062a\\u0634\\u0638\\u0627 \\u0645\\u0646 \\u0643\\u0644 \\u0642\\u0644\\u0628 \\u0639\\u0645\\u064a\\u062f \\u0635\\u0648\\u0631\\u0629 \\u0644\\u0644\\u0648\\u062c\\u0648\\u062f \\u0634\\u0648\\u0647\\u0627\\u0621 \\u0644\\u0648\\u0644\\u0627 \\u0634\\u0641\\u0642 \\u0627\\u0644\\u062d\\u0633\\u0646 \\u0641\\u0648\\u0642 \\u062a\\u0644\\u0643 \\u0627\\u0644\\u062e\\u062f\\u0648\\u062f \\u064a\\u0627 \\u0632\\u0647\\u0648\\u0631 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0644\\u0644\\u062d\\u0628 \\u0621\\u0646\\u062a\\u0646 \\u0648\\u0644\\u0643\\u0646\\u0647 \\u0645\\u062e\\u064a\\u0641 \\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0641\\u0633\\u0628\\u064a\\u0644 \\u0627\\u0644\\u063a\\u0631\\u0627\\u0645 \\u062c\\u0645 \\u0627\\u0644\\u0645\\u0647\\u0627\\u0648\\u064a \\u0648\\u0627\\u0641\\u0631 \\u0627\\u0644\\u0647\\u0648\\u0644 \\u0645\\u0633\\u062a\\u0631\\u0627\\u0628 \\u0627\\u0644\\u0635\\u0639\\u064a\\u062f \\u0631\\u063a\\u0645 \\u0645\\u0627 \\u0641\\u064a\\u0647 \\u0645\\u0646 \\u062c\\u0645\\u0627\\u0644 \\u0648\\u0641\\u0646 \\u0639\\u0628\\u0642\\u0631\\u064a \\u0645\\u0627 \\u0621\\u0646 \\u0644\\u0647 \\u0645\\u0646 \\u0645\\u0632\\u064a\\u062f \\u0648\\u0621\\u0646\\u0627\\u0634\\u064a\\u062f \\u062a\\u0633\\u0643\\u0631 \\u0627\\u0644\\u0645\\u0644\\u0621 \\u0627\\u0644\\u0621\\u0639\\u0644\\u0627 \\u0648\\u062a\\u0634\\u062c\\u064a \\u062c\\u0648\\u0627\\u0646\\u062d \\u0627\\u0644\\u062c\\u0644\\u0645\\u0648\\u062f \\u0648\\u0621\\u0631\\u064a\\u062c \\u064a\\u0643\\u0627\\u062f \\u064a\\u0630\\u0647\\u0628 \\u0628\\u0627\\u0644\\u0621\\u0644\\u0628\\u0627\\u0628 \\u0645\\u0627 \\u0628\\u064a\\u0646 \\u063a\\u0627\\u0645\\u0636 \\u0648\\u0634\\u062f\\u064a\\u062f \\u0648\\u0633\\u0628\\u064a\\u0644 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0631\\u062d\\u0628 \\u0648\\u0621\\u0646\\u062a\\u0646 \\u0627\\u0644\\u0644\\u0648\\u0627\\u062a\\u064a \\u062a\\u0641\\u0631\\u0634\\u0646\\u0647 \\u0628\\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0621\\u0646 \\u0621\\u0631\\u062f\\u062a\\u0646 \\u0621\\u0646 \\u064a\\u0643\\u0648\\u0646 \\u0628\\u0647\\u064a\\u062c\\u0627 \\u0631\\u0627\\u0621\\u0639 \\u0627\\u0644\\u0633\\u062d\\u0631 \\u0630\\u0627 \\u062c\\u0645\\u0627\\u0644 \\u0641\\u0631\\u064a\\u062f \\u0621\\u0648 \\u0628\\u0634\\u0648\\u0643 \\u064a\\u062f\\u0645\\u064a \\u0627\\u0644\\u0641\\u0636\\u064a\\u0644\\u0629 \\u0648\\u0627\\u0644\\u062d\\u0628 \\u0648\\u064a\\u0642\\u0636\\u064a \\u0639\\u0644\\u0627 \\u0628\\u0647\\u0627\\u0621 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0621\\u0646 \\u0621\\u0631\\u062f\\u062a\\u0646 \\u0621\\u0646 \\u064a\\u0643\\u0648\\u0646 \\u0634\\u0646\\u064a\\u0639\\u0627 \\u0645\\u0638\\u0644\\u0645 \\u0627\\u0644\\u0621\\u0641\\u0642 \\u0645\\u064a\\u062a \\u0627\\u0644\\u062a\\u063a\\u0631\\u064a\\u062f \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0639\\u0627\\u0645\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u062d\\u062f\\u064a\\u062b\",\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u0633\\u0631\\u064a\\u0639 \\u0646\\u0648\\u0627\\u0644\\u0643 \\u0627\\u0644\\u0633\\u0639\\u062f\\u064a \\u064a\\u0627 \\u0633\\u064a\\u062f\\u064a \\u0621\\u0631\\u062c\\u0648 \\u0639\\u0644\\u0627 \\u0639\\u0627\\u062f\\u0627\\u062a\\u0647 \\u0645\\u0631\\u0628\\u0639\\u0647 \\u0644\\u064a \\u0621\\u0634\\u0647\\u0631 \\u0621\\u0631\\u0628\\u0639\\u0629 \\u0621\\u062e\\u0631\\u062a \\u0641\\u062d\\u0638\\u064a \\u0627\\u0644\\u0645\\u0634\\u0621\\u0648\\u0645 \\u0628\\u0627\\u0644\\u0621\\u0631\\u0628\\u0639\\u0647 \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0645\\u0645\\u0644\\u0648\\u0643\\u064a\",\n          \"\\u0628\\u062d\\u0631 \\u0645\\u062c\\u0632\\u0648\\u0621 \\u0627\\u0644\\u0633\\u0631\\u064a\\u0639 \\u0644\\u0627 \\u062a\\u0639\\u062c\\u0628\\u064a \\u0621\\u0646 \\u062a\\u0631\\u064a \\u062c\\u0633\\u0645\\u064a \\u0646\\u062d\\u064a\\u0644\\u0627 \\u064a\\u0634\\u0641 \\u0648\\u0643\\u0627\\u0646 \\u0645\\u0627\\u0621 \\u0627\\u0644\\u0635\\u0628\\u0627 \\u0639\\u0646 \\u0633\\u0642\\u064a\\u0647 \\u0644\\u0627 \\u064a\\u0643\\u0641 \\u0639\\u0631\\u0636\\u062a\\u0647 \\u0644\\u0644\\u0647\\u0648\\u0627\\u0627 \\u0641\\u0645\\u0627 \\u0644\\u0647 \\u0644\\u0627 \\u064a\\u062c\\u0641 \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u062d\\u062f\\u064a\\u062b\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FqOtHZDd9kMd"
      },
      "source": [
        "## 2.4. Stop Word Removal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "xsN1kBCMq78u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7b6fb5a-bb07-442c-a7cb-cca0c8fb677f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "\n",
        "# Function to remove stopwords\n",
        "def remove_stopwords(tokens):\n",
        "    stop_words = set(stopwords.words('arabic'))\n",
        "    return [token for token in tokens if token not in stop_words]\n",
        "\n",
        "features['Filtered Tokens'] = features['Tokens'].apply(remove_stopwords)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features.head()"
      ],
      "metadata": {
        "id": "DGzs8Wc-caAe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "84c138be-c37a-4b1c-c4fc-f03bcc8d771d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        poem meter                                        poem verses  \\\n",
              "0       بحر الخفيف  [أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...   \n",
              "1  بحر مجزوء الرمل  [مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...   \n",
              "2       بحر البسيط  [العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...   \n",
              "3       بحر الكامل  [لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...   \n",
              "4       بحر الوافر  [يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...   \n",
              "\n",
              "    poem theme        poet era  \\\n",
              "0  قصيدة دينية  العصر العثماني   \n",
              "1  قصيدة دينية  العصر العثماني   \n",
              "2     قصيدة ذم  العصر العثماني   \n",
              "3   قصيدة عامه  العصر العثماني   \n",
              "4   قصيدة عامه  العصر العثماني   \n",
              "\n",
              "                                     Normalized Poem  \\\n",
              "0  بحر الخفيف ءصبح الملك للذي فطر الخل ق بتقدير ل...   \n",
              "1  بحر مجزوء الرمل من ءي مولا ارتجي ولاي باب التج...   \n",
              "2  بحر البسيط العبد عبدك يا من ءنت سيده وليس غيرك...   \n",
              "3  بحر الكامل لو كنت ءطمع بالمنام توهما لسالت طيف...   \n",
              "4  بحر الوافر يعد علي ءنفاسي ذنوبا ءذا ما قلت ءفد...   \n",
              "\n",
              "                                              Tokens  \\\n",
              "0  [بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, ق,...   \n",
              "1  [بحر, مجزوء, الرمل, من, ءي, مولا, ارتجي, ولاي,...   \n",
              "2  [بحر, البسيط, العبد, عبدك, يا, من, ءنت, سيده, ...   \n",
              "3  [بحر, الكامل, لو, كنت, ءطمع, بالمنام, توهما, ل...   \n",
              "4  [بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, ما...   \n",
              "\n",
              "                                     Filtered Tokens  \n",
              "0  [بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, بت...  \n",
              "1  [بحر, مجزوء, الرمل, ءي, مولا, ارتجي, ولاي, باب...  \n",
              "2  [بحر, البسيط, العبد, عبدك, ءنت, سيده, وليس, غي...  \n",
              "3  [بحر, الكامل, كنت, ءطمع, بالمنام, توهما, لسالت...  \n",
              "4  [بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, قل...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d4c3203-84ea-44de-a083-d7ddc26b0d58\" 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>poem meter</th>\n",
              "      <th>poem verses</th>\n",
              "      <th>poem theme</th>\n",
              "      <th>poet era</th>\n",
              "      <th>Normalized Poem</th>\n",
              "      <th>Tokens</th>\n",
              "      <th>Filtered Tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>بحر الخفيف</td>\n",
              "      <td>[أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الخفيف ءصبح الملك للذي فطر الخل ق بتقدير ل...</td>\n",
              "      <td>[بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, ق,...</td>\n",
              "      <td>[بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, بت...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>بحر مجزوء الرمل</td>\n",
              "      <td>[مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر مجزوء الرمل من ءي مولا ارتجي ولاي باب التج...</td>\n",
              "      <td>[بحر, مجزوء, الرمل, من, ءي, مولا, ارتجي, ولاي,...</td>\n",
              "      <td>[بحر, مجزوء, الرمل, ءي, مولا, ارتجي, ولاي, باب...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>بحر البسيط</td>\n",
              "      <td>[العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...</td>\n",
              "      <td>قصيدة ذم</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر البسيط العبد عبدك يا من ءنت سيده وليس غيرك...</td>\n",
              "      <td>[بحر, البسيط, العبد, عبدك, يا, من, ءنت, سيده, ...</td>\n",
              "      <td>[بحر, البسيط, العبد, عبدك, ءنت, سيده, وليس, غي...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>بحر الكامل</td>\n",
              "      <td>[لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الكامل لو كنت ءطمع بالمنام توهما لسالت طيف...</td>\n",
              "      <td>[بحر, الكامل, لو, كنت, ءطمع, بالمنام, توهما, ل...</td>\n",
              "      <td>[بحر, الكامل, كنت, ءطمع, بالمنام, توهما, لسالت...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>بحر الوافر</td>\n",
              "      <td>[يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الوافر يعد علي ءنفاسي ذنوبا ءذا ما قلت ءفد...</td>\n",
              "      <td>[بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, ما...</td>\n",
              "      <td>[بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, قل...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d4c3203-84ea-44de-a083-d7ddc26b0d58')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2d4c3203-84ea-44de-a083-d7ddc26b0d58 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-2d4c3203-84ea-44de-a083-d7ddc26b0d58');\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",
              "\n",
              "\n",
              "<div id=\"df-4591017a-c343-4171-9792-e0629397cda7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4591017a-c343-4171-9792-e0629397cda7')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4591017a-c343-4171-9792-e0629397cda7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "features",
              "summary": "{\n  \"name\": \"features\",\n  \"rows\": 54701,\n  \"fields\": [\n    {\n      \"column\": \"poem meter\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 66,\n        \"samples\": [\n          \"\\u0628\\u062d\\u0631 \\u0645\\u0646\\u0647\\u0648\\u0643 \\u0627\\u0644\\u0628\\u0633\\u064a\\u0637\",\n          \"\\u0628\\u062d\\u0631 \\u0645\\u062c\\u0632\\u0648\\u0621 \\u0627\\u0644\\u0647\\u0632\\u062c\",\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u062e\\u0641\\u064a\\u0641\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poem verses\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poem theme\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u062f\\u064a\\u0646\\u064a\\u0629\",\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u0630\\u0645\",\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u063a\\u0632\\u0644\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poet era\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0639\\u062b\\u0645\\u0627\\u0646\\u064a\",\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0639\\u0628\\u0627\\u0633\\u064a\",\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0645\\u0645\\u0644\\u0648\\u0643\\u064a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Normalized Poem\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54701,\n        \"samples\": [\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u062e\\u0641\\u064a\\u0641 \\u064a\\u0627 \\u0639\\u0630\\u0627\\u0631\\u0627 \\u0627\\u0644\\u062c\\u0645\\u0627\\u0644 \\u0648\\u0627\\u0644\\u062d\\u0628 \\u0648\\u0627\\u0644\\u0621\\u062d\\u0644\\u0627\\u0645 \\u0628\\u0644 \\u064a\\u0627 \\u0628\\u0647\\u0627\\u0621 \\u0647\\u0630\\u0627 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u062e\\u0644\\u0642 \\u0627\\u0644\\u0628\\u0644\\u0628\\u0644 \\u0627\\u0644\\u062c\\u0645\\u064a\\u0644 \\u0644\\u064a\\u0634\\u062f\\u0648 \\u0648\\u062e\\u0644\\u0642\\u062a\\u0646 \\u0644\\u0644\\u063a\\u0631\\u0627\\u0645 \\u0627\\u0644\\u0633\\u0639\\u064a\\u062f \\u0648\\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0627\\u0644\\u0631\\u062d\\u064a\\u0628 \\u0643\\u0627\\u0644\\u0642\\u0628\\u0631 \\u0644\\u0648\\u0644\\u0627 \\u0645\\u0627 \\u062a\\u062c\\u0644\\u064a\\u0646 \\u0645\\u0646 \\u0642\\u0637\\u0648\\u0628 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0648\\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0627\\u0644\\u062a\\u064a \\u062a\\u062e\\u0631 \\u0644\\u0647\\u0627 \\u0627\\u0644\\u0621\\u062d\\u0644\\u0627\\u0645 \\u0645\\u0648\\u062a \\u0645\\u062b\\u0642\\u0644 \\u0643\\u0627\\u0644\\u0642\\u064a\\u0648\\u062f \\u0648\\u0627\\u0644\\u0634\\u0628\\u0627\\u0628 \\u0627\\u0644\\u062d\\u0628\\u064a\\u0628 \\u0634\\u064a\\u062e\\u0648\\u062e\\u0629 \\u062a\\u0633\\u0639\\u0627 \\u0621\\u0644\\u0627 \\u0627\\u0644\\u0645\\u0648\\u062a \\u0641\\u064a \\u0637\\u0631\\u064a\\u0642 \\u0643\\u0621\\u0648\\u062f \\u0648\\u0627\\u0644\\u0631\\u0628\\u064a\\u0639 \\u0627\\u0644\\u062c\\u0645\\u064a\\u0644 \\u0641\\u064a \\u0647\\u0627\\u062a\\u0647 \\u0627\\u0644\\u062f\\u0646\\u064a\\u0627 \\u062e\\u0631\\u064a\\u0641 \\u064a\\u0630\\u0648\\u064a \\u0631\\u0641\\u064a\\u0641 \\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0648\\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0627\\u0644\\u0639\\u0630\\u0627\\u0628 \\u0641\\u064a \\u0636\\u0641\\u0629 \\u0627\\u0644\\u062c\\u062f\\u0648\\u0644 \\u0634\\u0648\\u0643 \\u0645\\u0635\\u0641\\u062d \\u0628\\u0627\\u0644\\u062d\\u062f\\u064a\\u062f \\u0648\\u0627\\u0644\\u0637\\u064a\\u0648\\u0631 \\u0627\\u0644\\u062a\\u064a \\u062a\\u063a\\u0646\\u064a \\u0648\\u062a\\u0642\\u0636\\u064a \\u0639\\u064a\\u0634\\u0647\\u0627 \\u0641\\u064a \\u062a\\u0631\\u0646\\u0645 \\u0648\\u063a\\u0631\\u064a\\u062f \\u0621\\u0646\\u0647\\u0627 \\u0641\\u064a \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u062a\\u0634\\u0643\\u0648 \\u0621\\u0644\\u0627 \\u0627\\u0644\\u0621\\u064a\\u0627\\u0645 \\u0639\\u0628\\u0621 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0628\\u0627\\u0644\\u062a\\u063a\\u0631\\u064a\\u062f \\u0648\\u0627\\u0644\\u0621\\u0646\\u0627\\u0634\\u064a\\u062f \\u0621\\u0646\\u0647\\u0627 \\u0634\\u0647\\u0642\\u0627\\u062a \\u062a\\u062a\\u0634\\u0638\\u0627 \\u0645\\u0646 \\u0643\\u0644 \\u0642\\u0644\\u0628 \\u0639\\u0645\\u064a\\u062f \\u0635\\u0648\\u0631\\u0629 \\u0644\\u0644\\u0648\\u062c\\u0648\\u062f \\u0634\\u0648\\u0647\\u0627\\u0621 \\u0644\\u0648\\u0644\\u0627 \\u0634\\u0641\\u0642 \\u0627\\u0644\\u062d\\u0633\\u0646 \\u0641\\u0648\\u0642 \\u062a\\u0644\\u0643 \\u0627\\u0644\\u062e\\u062f\\u0648\\u062f \\u064a\\u0627 \\u0632\\u0647\\u0648\\u0631 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0644\\u0644\\u062d\\u0628 \\u0621\\u0646\\u062a\\u0646 \\u0648\\u0644\\u0643\\u0646\\u0647 \\u0645\\u062e\\u064a\\u0641 \\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0641\\u0633\\u0628\\u064a\\u0644 \\u0627\\u0644\\u063a\\u0631\\u0627\\u0645 \\u062c\\u0645 \\u0627\\u0644\\u0645\\u0647\\u0627\\u0648\\u064a \\u0648\\u0627\\u0641\\u0631 \\u0627\\u0644\\u0647\\u0648\\u0644 \\u0645\\u0633\\u062a\\u0631\\u0627\\u0628 \\u0627\\u0644\\u0635\\u0639\\u064a\\u062f \\u0631\\u063a\\u0645 \\u0645\\u0627 \\u0641\\u064a\\u0647 \\u0645\\u0646 \\u062c\\u0645\\u0627\\u0644 \\u0648\\u0641\\u0646 \\u0639\\u0628\\u0642\\u0631\\u064a \\u0645\\u0627 \\u0621\\u0646 \\u0644\\u0647 \\u0645\\u0646 \\u0645\\u0632\\u064a\\u062f \\u0648\\u0621\\u0646\\u0627\\u0634\\u064a\\u062f \\u062a\\u0633\\u0643\\u0631 \\u0627\\u0644\\u0645\\u0644\\u0621 \\u0627\\u0644\\u0621\\u0639\\u0644\\u0627 \\u0648\\u062a\\u0634\\u062c\\u064a \\u062c\\u0648\\u0627\\u0646\\u062d \\u0627\\u0644\\u062c\\u0644\\u0645\\u0648\\u062f \\u0648\\u0621\\u0631\\u064a\\u062c \\u064a\\u0643\\u0627\\u062f \\u064a\\u0630\\u0647\\u0628 \\u0628\\u0627\\u0644\\u0621\\u0644\\u0628\\u0627\\u0628 \\u0645\\u0627 \\u0628\\u064a\\u0646 \\u063a\\u0627\\u0645\\u0636 \\u0648\\u0634\\u062f\\u064a\\u062f \\u0648\\u0633\\u0628\\u064a\\u0644 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0631\\u062d\\u0628 \\u0648\\u0621\\u0646\\u062a\\u0646 \\u0627\\u0644\\u0644\\u0648\\u0627\\u062a\\u064a \\u062a\\u0641\\u0631\\u0634\\u0646\\u0647 \\u0628\\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0621\\u0646 \\u0621\\u0631\\u062f\\u062a\\u0646 \\u0621\\u0646 \\u064a\\u0643\\u0648\\u0646 \\u0628\\u0647\\u064a\\u062c\\u0627 \\u0631\\u0627\\u0621\\u0639 \\u0627\\u0644\\u0633\\u062d\\u0631 \\u0630\\u0627 \\u062c\\u0645\\u0627\\u0644 \\u0641\\u0631\\u064a\\u062f \\u0621\\u0648 \\u0628\\u0634\\u0648\\u0643 \\u064a\\u062f\\u0645\\u064a \\u0627\\u0644\\u0641\\u0636\\u064a\\u0644\\u0629 \\u0648\\u0627\\u0644\\u062d\\u0628 \\u0648\\u064a\\u0642\\u0636\\u064a \\u0639\\u0644\\u0627 \\u0628\\u0647\\u0627\\u0621 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0621\\u0646 \\u0621\\u0631\\u062f\\u062a\\u0646 \\u0621\\u0646 \\u064a\\u0643\\u0648\\u0646 \\u0634\\u0646\\u064a\\u0639\\u0627 \\u0645\\u0638\\u0644\\u0645 \\u0627\\u0644\\u0621\\u0641\\u0642 \\u0645\\u064a\\u062a \\u0627\\u0644\\u062a\\u063a\\u0631\\u064a\\u062f \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0639\\u0627\\u0645\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u062d\\u062f\\u064a\\u062b\",\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u0633\\u0631\\u064a\\u0639 \\u0646\\u0648\\u0627\\u0644\\u0643 \\u0627\\u0644\\u0633\\u0639\\u062f\\u064a \\u064a\\u0627 \\u0633\\u064a\\u062f\\u064a \\u0621\\u0631\\u062c\\u0648 \\u0639\\u0644\\u0627 \\u0639\\u0627\\u062f\\u0627\\u062a\\u0647 \\u0645\\u0631\\u0628\\u0639\\u0647 \\u0644\\u064a \\u0621\\u0634\\u0647\\u0631 \\u0621\\u0631\\u0628\\u0639\\u0629 \\u0621\\u062e\\u0631\\u062a \\u0641\\u062d\\u0638\\u064a \\u0627\\u0644\\u0645\\u0634\\u0621\\u0648\\u0645 \\u0628\\u0627\\u0644\\u0621\\u0631\\u0628\\u0639\\u0647 \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0645\\u0645\\u0644\\u0648\\u0643\\u064a\",\n          \"\\u0628\\u062d\\u0631 \\u0645\\u062c\\u0632\\u0648\\u0621 \\u0627\\u0644\\u0633\\u0631\\u064a\\u0639 \\u0644\\u0627 \\u062a\\u0639\\u062c\\u0628\\u064a \\u0621\\u0646 \\u062a\\u0631\\u064a \\u062c\\u0633\\u0645\\u064a \\u0646\\u062d\\u064a\\u0644\\u0627 \\u064a\\u0634\\u0641 \\u0648\\u0643\\u0627\\u0646 \\u0645\\u0627\\u0621 \\u0627\\u0644\\u0635\\u0628\\u0627 \\u0639\\u0646 \\u0633\\u0642\\u064a\\u0647 \\u0644\\u0627 \\u064a\\u0643\\u0641 \\u0639\\u0631\\u0636\\u062a\\u0647 \\u0644\\u0644\\u0647\\u0648\\u0627\\u0627 \\u0641\\u0645\\u0627 \\u0644\\u0647 \\u0644\\u0627 \\u064a\\u062c\\u0641 \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u062d\\u062f\\u064a\\u062b\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Filtered Tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x1pMeI6_9yo8"
      },
      "source": [
        "## 2.5. Stemming"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the stemmer\n",
        "stemmer = ArabicLightStemmer()"
      ],
      "metadata": {
        "id": "SQgOGrWvcah7"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "2dUD_2HTrGI8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a9ad707-396c-4e47-a220-ae909088ce90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample preprocessed data:\n",
            "0    حر خفيف ءصبح ملك الذي طر خل تقدير عزيز عليم غا...\n",
            "1    حر مجزوء رمل ءي مول رتج اي اب تج له حي رازق عط...\n",
            "2    حر بسيط عبد عبد ءن ده ليس غير ءوصاب نجد ءن سبي...\n",
            "3    حر كامل نت ءطمع منام وهم سال طيف ءن زور كرم صد...\n",
            "4    حر وافر عد على ءنفاس ذنوب ءذ قل ءفد حبيب ءبعد ...\n",
            "Name: Stemmed Poem Text, dtype: object\n"
          ]
        }
      ],
      "source": [
        "# Function to stem tokens\n",
        "def stem_tokens(tokens):\n",
        "  return [stemmer.light_stem(token) for token in tokens]\n",
        "\n",
        "features['Stemmed Tokens'] = features['Filtered Tokens'].apply(stem_tokens)\n",
        "features['Stemmed Poem Text'] = features['Stemmed Tokens'].apply(lambda x: ' '.join(x))\n",
        "\n",
        "\n",
        "# Print a sample of preprocessed data\n",
        "print(\"Sample preprocessed data:\")\n",
        "print(features['Stemmed Poem Text'].head())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features.head()"
      ],
      "metadata": {
        "id": "P8re8hDBcgHa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "outputId": "f935f8f7-0808-44f1-8937-98908a131a81"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        poem meter                                        poem verses  \\\n",
              "0       بحر الخفيف  [أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...   \n",
              "1  بحر مجزوء الرمل  [مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...   \n",
              "2       بحر البسيط  [العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...   \n",
              "3       بحر الكامل  [لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...   \n",
              "4       بحر الوافر  [يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...   \n",
              "\n",
              "    poem theme        poet era  \\\n",
              "0  قصيدة دينية  العصر العثماني   \n",
              "1  قصيدة دينية  العصر العثماني   \n",
              "2     قصيدة ذم  العصر العثماني   \n",
              "3   قصيدة عامه  العصر العثماني   \n",
              "4   قصيدة عامه  العصر العثماني   \n",
              "\n",
              "                                     Normalized Poem  \\\n",
              "0  بحر الخفيف ءصبح الملك للذي فطر الخل ق بتقدير ل...   \n",
              "1  بحر مجزوء الرمل من ءي مولا ارتجي ولاي باب التج...   \n",
              "2  بحر البسيط العبد عبدك يا من ءنت سيده وليس غيرك...   \n",
              "3  بحر الكامل لو كنت ءطمع بالمنام توهما لسالت طيف...   \n",
              "4  بحر الوافر يعد علي ءنفاسي ذنوبا ءذا ما قلت ءفد...   \n",
              "\n",
              "                                              Tokens  \\\n",
              "0  [بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, ق,...   \n",
              "1  [بحر, مجزوء, الرمل, من, ءي, مولا, ارتجي, ولاي,...   \n",
              "2  [بحر, البسيط, العبد, عبدك, يا, من, ءنت, سيده, ...   \n",
              "3  [بحر, الكامل, لو, كنت, ءطمع, بالمنام, توهما, ل...   \n",
              "4  [بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, ما...   \n",
              "\n",
              "                                     Filtered Tokens  \\\n",
              "0  [بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, بت...   \n",
              "1  [بحر, مجزوء, الرمل, ءي, مولا, ارتجي, ولاي, باب...   \n",
              "2  [بحر, البسيط, العبد, عبدك, ءنت, سيده, وليس, غي...   \n",
              "3  [بحر, الكامل, كنت, ءطمع, بالمنام, توهما, لسالت...   \n",
              "4  [بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, قل...   \n",
              "\n",
              "                                      Stemmed Tokens  \\\n",
              "0  [حر, خفيف, ءصبح, ملك, الذي, طر, خل, تقدير, عزي...   \n",
              "1  [حر, مجزوء, رمل, ءي, مول, رتج, اي, اب, تج, له,...   \n",
              "2  [حر, بسيط, عبد, عبد, ءن, ده, ليس, غير, ءوصاب, ...   \n",
              "3  [حر, كامل, نت, ءطمع, منام, وهم, سال, طيف, ءن, ...   \n",
              "4  [حر, وافر, عد, على, ءنفاس, ذنوب, ءذ, قل, ءفد, ...   \n",
              "\n",
              "                                   Stemmed Poem Text  \n",
              "0  حر خفيف ءصبح ملك الذي طر خل تقدير عزيز عليم غا...  \n",
              "1  حر مجزوء رمل ءي مول رتج اي اب تج له حي رازق عط...  \n",
              "2  حر بسيط عبد عبد ءن ده ليس غير ءوصاب نجد ءن سبي...  \n",
              "3  حر كامل نت ءطمع منام وهم سال طيف ءن زور كرم صد...  \n",
              "4  حر وافر عد على ءنفاس ذنوب ءذ قل ءفد حبيب ءبعد ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-88748184-42b5-4615-8460-26cd94474bf7\" 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>poem meter</th>\n",
              "      <th>poem verses</th>\n",
              "      <th>poem theme</th>\n",
              "      <th>poet era</th>\n",
              "      <th>Normalized Poem</th>\n",
              "      <th>Tokens</th>\n",
              "      <th>Filtered Tokens</th>\n",
              "      <th>Stemmed Tokens</th>\n",
              "      <th>Stemmed Poem Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>بحر الخفيف</td>\n",
              "      <td>[أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الخفيف ءصبح الملك للذي فطر الخل ق بتقدير ل...</td>\n",
              "      <td>[بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, ق,...</td>\n",
              "      <td>[بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, بت...</td>\n",
              "      <td>[حر, خفيف, ءصبح, ملك, الذي, طر, خل, تقدير, عزي...</td>\n",
              "      <td>حر خفيف ءصبح ملك الذي طر خل تقدير عزيز عليم غا...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>بحر مجزوء الرمل</td>\n",
              "      <td>[مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر مجزوء الرمل من ءي مولا ارتجي ولاي باب التج...</td>\n",
              "      <td>[بحر, مجزوء, الرمل, من, ءي, مولا, ارتجي, ولاي,...</td>\n",
              "      <td>[بحر, مجزوء, الرمل, ءي, مولا, ارتجي, ولاي, باب...</td>\n",
              "      <td>[حر, مجزوء, رمل, ءي, مول, رتج, اي, اب, تج, له,...</td>\n",
              "      <td>حر مجزوء رمل ءي مول رتج اي اب تج له حي رازق عط...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>بحر البسيط</td>\n",
              "      <td>[العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...</td>\n",
              "      <td>قصيدة ذم</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر البسيط العبد عبدك يا من ءنت سيده وليس غيرك...</td>\n",
              "      <td>[بحر, البسيط, العبد, عبدك, يا, من, ءنت, سيده, ...</td>\n",
              "      <td>[بحر, البسيط, العبد, عبدك, ءنت, سيده, وليس, غي...</td>\n",
              "      <td>[حر, بسيط, عبد, عبد, ءن, ده, ليس, غير, ءوصاب, ...</td>\n",
              "      <td>حر بسيط عبد عبد ءن ده ليس غير ءوصاب نجد ءن سبي...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>بحر الكامل</td>\n",
              "      <td>[لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الكامل لو كنت ءطمع بالمنام توهما لسالت طيف...</td>\n",
              "      <td>[بحر, الكامل, لو, كنت, ءطمع, بالمنام, توهما, ل...</td>\n",
              "      <td>[بحر, الكامل, كنت, ءطمع, بالمنام, توهما, لسالت...</td>\n",
              "      <td>[حر, كامل, نت, ءطمع, منام, وهم, سال, طيف, ءن, ...</td>\n",
              "      <td>حر كامل نت ءطمع منام وهم سال طيف ءن زور كرم صد...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>بحر الوافر</td>\n",
              "      <td>[يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الوافر يعد علي ءنفاسي ذنوبا ءذا ما قلت ءفد...</td>\n",
              "      <td>[بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, ما...</td>\n",
              "      <td>[بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, قل...</td>\n",
              "      <td>[حر, وافر, عد, على, ءنفاس, ذنوب, ءذ, قل, ءفد, ...</td>\n",
              "      <td>حر وافر عد على ءنفاس ذنوب ءذ قل ءفد حبيب ءبعد ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88748184-42b5-4615-8460-26cd94474bf7')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-88748184-42b5-4615-8460-26cd94474bf7 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-88748184-42b5-4615-8460-26cd94474bf7');\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",
              "\n",
              "\n",
              "<div id=\"df-6bee4fac-ad59-41a5-9f92-f7e3660094f9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6bee4fac-ad59-41a5-9f92-f7e3660094f9')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6bee4fac-ad59-41a5-9f92-f7e3660094f9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "features",
              "summary": "{\n  \"name\": \"features\",\n  \"rows\": 54701,\n  \"fields\": [\n    {\n      \"column\": \"poem meter\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 66,\n        \"samples\": [\n          \"\\u0628\\u062d\\u0631 \\u0645\\u0646\\u0647\\u0648\\u0643 \\u0627\\u0644\\u0628\\u0633\\u064a\\u0637\",\n          \"\\u0628\\u062d\\u0631 \\u0645\\u062c\\u0632\\u0648\\u0621 \\u0627\\u0644\\u0647\\u0632\\u062c\",\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u062e\\u0641\\u064a\\u0641\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poem verses\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poem theme\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u062f\\u064a\\u0646\\u064a\\u0629\",\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u0630\\u0645\",\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u063a\\u0632\\u0644\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poet era\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0639\\u062b\\u0645\\u0627\\u0646\\u064a\",\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0639\\u0628\\u0627\\u0633\\u064a\",\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0645\\u0645\\u0644\\u0648\\u0643\\u064a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Normalized Poem\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54701,\n        \"samples\": [\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u062e\\u0641\\u064a\\u0641 \\u064a\\u0627 \\u0639\\u0630\\u0627\\u0631\\u0627 \\u0627\\u0644\\u062c\\u0645\\u0627\\u0644 \\u0648\\u0627\\u0644\\u062d\\u0628 \\u0648\\u0627\\u0644\\u0621\\u062d\\u0644\\u0627\\u0645 \\u0628\\u0644 \\u064a\\u0627 \\u0628\\u0647\\u0627\\u0621 \\u0647\\u0630\\u0627 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u062e\\u0644\\u0642 \\u0627\\u0644\\u0628\\u0644\\u0628\\u0644 \\u0627\\u0644\\u062c\\u0645\\u064a\\u0644 \\u0644\\u064a\\u0634\\u062f\\u0648 \\u0648\\u062e\\u0644\\u0642\\u062a\\u0646 \\u0644\\u0644\\u063a\\u0631\\u0627\\u0645 \\u0627\\u0644\\u0633\\u0639\\u064a\\u062f \\u0648\\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0627\\u0644\\u0631\\u062d\\u064a\\u0628 \\u0643\\u0627\\u0644\\u0642\\u0628\\u0631 \\u0644\\u0648\\u0644\\u0627 \\u0645\\u0627 \\u062a\\u062c\\u0644\\u064a\\u0646 \\u0645\\u0646 \\u0642\\u0637\\u0648\\u0628 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0648\\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0627\\u0644\\u062a\\u064a \\u062a\\u062e\\u0631 \\u0644\\u0647\\u0627 \\u0627\\u0644\\u0621\\u062d\\u0644\\u0627\\u0645 \\u0645\\u0648\\u062a \\u0645\\u062b\\u0642\\u0644 \\u0643\\u0627\\u0644\\u0642\\u064a\\u0648\\u062f \\u0648\\u0627\\u0644\\u0634\\u0628\\u0627\\u0628 \\u0627\\u0644\\u062d\\u0628\\u064a\\u0628 \\u0634\\u064a\\u062e\\u0648\\u062e\\u0629 \\u062a\\u0633\\u0639\\u0627 \\u0621\\u0644\\u0627 \\u0627\\u0644\\u0645\\u0648\\u062a \\u0641\\u064a \\u0637\\u0631\\u064a\\u0642 \\u0643\\u0621\\u0648\\u062f \\u0648\\u0627\\u0644\\u0631\\u0628\\u064a\\u0639 \\u0627\\u0644\\u062c\\u0645\\u064a\\u0644 \\u0641\\u064a \\u0647\\u0627\\u062a\\u0647 \\u0627\\u0644\\u062f\\u0646\\u064a\\u0627 \\u062e\\u0631\\u064a\\u0641 \\u064a\\u0630\\u0648\\u064a \\u0631\\u0641\\u064a\\u0641 \\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0648\\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0627\\u0644\\u0639\\u0630\\u0627\\u0628 \\u0641\\u064a \\u0636\\u0641\\u0629 \\u0627\\u0644\\u062c\\u062f\\u0648\\u0644 \\u0634\\u0648\\u0643 \\u0645\\u0635\\u0641\\u062d \\u0628\\u0627\\u0644\\u062d\\u062f\\u064a\\u062f \\u0648\\u0627\\u0644\\u0637\\u064a\\u0648\\u0631 \\u0627\\u0644\\u062a\\u064a \\u062a\\u063a\\u0646\\u064a \\u0648\\u062a\\u0642\\u0636\\u064a \\u0639\\u064a\\u0634\\u0647\\u0627 \\u0641\\u064a \\u062a\\u0631\\u0646\\u0645 \\u0648\\u063a\\u0631\\u064a\\u062f \\u0621\\u0646\\u0647\\u0627 \\u0641\\u064a \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u062a\\u0634\\u0643\\u0648 \\u0621\\u0644\\u0627 \\u0627\\u0644\\u0621\\u064a\\u0627\\u0645 \\u0639\\u0628\\u0621 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0628\\u0627\\u0644\\u062a\\u063a\\u0631\\u064a\\u062f \\u0648\\u0627\\u0644\\u0621\\u0646\\u0627\\u0634\\u064a\\u062f \\u0621\\u0646\\u0647\\u0627 \\u0634\\u0647\\u0642\\u0627\\u062a \\u062a\\u062a\\u0634\\u0638\\u0627 \\u0645\\u0646 \\u0643\\u0644 \\u0642\\u0644\\u0628 \\u0639\\u0645\\u064a\\u062f \\u0635\\u0648\\u0631\\u0629 \\u0644\\u0644\\u0648\\u062c\\u0648\\u062f \\u0634\\u0648\\u0647\\u0627\\u0621 \\u0644\\u0648\\u0644\\u0627 \\u0634\\u0641\\u0642 \\u0627\\u0644\\u062d\\u0633\\u0646 \\u0641\\u0648\\u0642 \\u062a\\u0644\\u0643 \\u0627\\u0644\\u062e\\u062f\\u0648\\u062f \\u064a\\u0627 \\u0632\\u0647\\u0648\\u0631 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0644\\u0644\\u062d\\u0628 \\u0621\\u0646\\u062a\\u0646 \\u0648\\u0644\\u0643\\u0646\\u0647 \\u0645\\u062e\\u064a\\u0641 \\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0641\\u0633\\u0628\\u064a\\u0644 \\u0627\\u0644\\u063a\\u0631\\u0627\\u0645 \\u062c\\u0645 \\u0627\\u0644\\u0645\\u0647\\u0627\\u0648\\u064a \\u0648\\u0627\\u0641\\u0631 \\u0627\\u0644\\u0647\\u0648\\u0644 \\u0645\\u0633\\u062a\\u0631\\u0627\\u0628 \\u0627\\u0644\\u0635\\u0639\\u064a\\u062f \\u0631\\u063a\\u0645 \\u0645\\u0627 \\u0641\\u064a\\u0647 \\u0645\\u0646 \\u062c\\u0645\\u0627\\u0644 \\u0648\\u0641\\u0646 \\u0639\\u0628\\u0642\\u0631\\u064a \\u0645\\u0627 \\u0621\\u0646 \\u0644\\u0647 \\u0645\\u0646 \\u0645\\u0632\\u064a\\u062f \\u0648\\u0621\\u0646\\u0627\\u0634\\u064a\\u062f \\u062a\\u0633\\u0643\\u0631 \\u0627\\u0644\\u0645\\u0644\\u0621 \\u0627\\u0644\\u0621\\u0639\\u0644\\u0627 \\u0648\\u062a\\u0634\\u062c\\u064a \\u062c\\u0648\\u0627\\u0646\\u062d \\u0627\\u0644\\u062c\\u0644\\u0645\\u0648\\u062f \\u0648\\u0621\\u0631\\u064a\\u062c \\u064a\\u0643\\u0627\\u062f \\u064a\\u0630\\u0647\\u0628 \\u0628\\u0627\\u0644\\u0621\\u0644\\u0628\\u0627\\u0628 \\u0645\\u0627 \\u0628\\u064a\\u0646 \\u063a\\u0627\\u0645\\u0636 \\u0648\\u0634\\u062f\\u064a\\u062f \\u0648\\u0633\\u0628\\u064a\\u0644 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0631\\u062d\\u0628 \\u0648\\u0621\\u0646\\u062a\\u0646 \\u0627\\u0644\\u0644\\u0648\\u0627\\u062a\\u064a \\u062a\\u0641\\u0631\\u0634\\u0646\\u0647 \\u0628\\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0621\\u0646 \\u0621\\u0631\\u062f\\u062a\\u0646 \\u0621\\u0646 \\u064a\\u0643\\u0648\\u0646 \\u0628\\u0647\\u064a\\u062c\\u0627 \\u0631\\u0627\\u0621\\u0639 \\u0627\\u0644\\u0633\\u062d\\u0631 \\u0630\\u0627 \\u062c\\u0645\\u0627\\u0644 \\u0641\\u0631\\u064a\\u062f \\u0621\\u0648 \\u0628\\u0634\\u0648\\u0643 \\u064a\\u062f\\u0645\\u064a \\u0627\\u0644\\u0641\\u0636\\u064a\\u0644\\u0629 \\u0648\\u0627\\u0644\\u062d\\u0628 \\u0648\\u064a\\u0642\\u0636\\u064a \\u0639\\u0644\\u0627 \\u0628\\u0647\\u0627\\u0621 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0621\\u0646 \\u0621\\u0631\\u062f\\u062a\\u0646 \\u0621\\u0646 \\u064a\\u0643\\u0648\\u0646 \\u0634\\u0646\\u064a\\u0639\\u0627 \\u0645\\u0638\\u0644\\u0645 \\u0627\\u0644\\u0621\\u0641\\u0642 \\u0645\\u064a\\u062a \\u0627\\u0644\\u062a\\u063a\\u0631\\u064a\\u062f \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0639\\u0627\\u0645\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u062d\\u062f\\u064a\\u062b\",\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u0633\\u0631\\u064a\\u0639 \\u0646\\u0648\\u0627\\u0644\\u0643 \\u0627\\u0644\\u0633\\u0639\\u062f\\u064a \\u064a\\u0627 \\u0633\\u064a\\u062f\\u064a \\u0621\\u0631\\u062c\\u0648 \\u0639\\u0644\\u0627 \\u0639\\u0627\\u062f\\u0627\\u062a\\u0647 \\u0645\\u0631\\u0628\\u0639\\u0647 \\u0644\\u064a \\u0621\\u0634\\u0647\\u0631 \\u0621\\u0631\\u0628\\u0639\\u0629 \\u0621\\u062e\\u0631\\u062a \\u0641\\u062d\\u0638\\u064a \\u0627\\u0644\\u0645\\u0634\\u0621\\u0648\\u0645 \\u0628\\u0627\\u0644\\u0621\\u0631\\u0628\\u0639\\u0647 \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0645\\u0645\\u0644\\u0648\\u0643\\u064a\",\n          \"\\u0628\\u062d\\u0631 \\u0645\\u062c\\u0632\\u0648\\u0621 \\u0627\\u0644\\u0633\\u0631\\u064a\\u0639 \\u0644\\u0627 \\u062a\\u0639\\u062c\\u0628\\u064a \\u0621\\u0646 \\u062a\\u0631\\u064a \\u062c\\u0633\\u0645\\u064a \\u0646\\u062d\\u064a\\u0644\\u0627 \\u064a\\u0634\\u0641 \\u0648\\u0643\\u0627\\u0646 \\u0645\\u0627\\u0621 \\u0627\\u0644\\u0635\\u0628\\u0627 \\u0639\\u0646 \\u0633\\u0642\\u064a\\u0647 \\u0644\\u0627 \\u064a\\u0643\\u0641 \\u0639\\u0631\\u0636\\u062a\\u0647 \\u0644\\u0644\\u0647\\u0648\\u0627\\u0627 \\u0641\\u0645\\u0627 \\u0644\\u0647 \\u0644\\u0627 \\u064a\\u062c\\u0641 \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u062d\\u062f\\u064a\\u062b\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Filtered Tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stemmed Tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stemmed Poem Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54692,\n        \"samples\": [\n          \"\\u062d\\u0631 \\u0648\\u0627\\u0641\\u0631 \\u0643\\u0648 \\u062e\\u0627\\u0644 \\u062c\\u0647 \\u0645\\u0644\\u064a\\u062d \\u0643\\u0633 \\u0645\\u0644\\u0627\\u062d \\u062c\\u0645\\u0627\\u0644 \\u0644\\u0633\\u062a \\u0645\\u0644 \\u0638\\u0631 \\u0621\\u0644 \\u0643\\u064a\\u0641 \\u0621\\u0630 \\u0631\\u0621\\u064a \\u0648\\u062c\\u0647 \\u062e\\u0627\\u0644 \\u0642\\u0635\\u064a\\u062f \\u0645\\u062f\\u062d \\u0639\\u0635\\u0631 \\u0639\\u0628\\u0627\\u0633\",\n          \"\\u062d\\u0631 \\u062e\\u0641\\u064a\\u0641 \\u062e\\u0641 \\u062a\\u0627\\u0628\\u0627 \\u0627\\u062a\\u0628\\u0627 \\u062a\\u0642\\u0635 \\u0635\\u063a\\u0631 \\u0621\\u0644\\u0641\\u0627\\u0638 \\u0621\\u0644\\u062d\\u0627\\u0638 \\u062e\\u0641\\u0636 \\u0639\\u064a\\u0646 \\u0621\\u0646 \\u0633\\u0645 \\u0627\\u0632\\u062f\\u0647\\u0627\\u0621 \\u063a\\u0636 \\u0646\\u0641\\u0633 \\u0621\\u0646 \\u0637\\u0645 \\u0627\\u063a\\u062a\\u064a\\u0627\\u0638 \\u0637\\u0627\\u0644 \\u0639\\u0638 \\u0644\\u064a\\u0627\\u0644 \\u0648\\u0639\\u0638 \\u0621\\u0646 \\u0631\\u0639\\u0648\\u064a \\u0627\\u062a\\u0639\\u0627\\u0638 \\u062d\\u0641\\u0638 \\u0644\\u0647 \\u0621\\u0636\\u0627\\u0639 \\u0621\\u0645\\u0627\\u0646 \\u062d\\u0641\\u0627\\u0638 \\u0643\\u0646 \\u062d\\u062a\\u0641\\u0627\\u0638 \\u0621\\u064a\\u0642\\u0638 \\u0637\\u0631\\u0641 \\u0621\\u0646\\u0627\\u0645 \\u064a\\u0627\\u0645 \\u0642\\u064a\\u0633 \\u0631\\u0642\\u0648\\u062f \\u0621\\u064a\\u0642\\u0627\\u0638 \\u0642\\u0635\\u064a\\u062f \\u0639\\u0627\\u0645 \\u0639\\u0635\\u0631 \\u0645\\u0645\\u0644\\u0648\\u0643\",\n          \"\\u062d\\u0631 \\u0637\\u0648\\u064a\\u0644 \\u0621\\u0647\\u0644 \\u062e\\u0628\\u0631 \\u0646\\u062c\\u064a\\u062f \\u0631\\u0648 \\u0641\\u062c\\u0631 \\u062f\\u0645\\u0639 \\u0646\\u062c\\u064a\\u062f \\u0631\\u0648 \\u0631\\u0648 \\u0645\\u0633\\u064a\\u0627\\u0644 \\u0644\\u0627\\u0639 \\u0621\\u0646 \\u0632\\u0627\\u062f \\u0633\\u0643\\u0628 \\u0633\\u0643\\u0628 \\u063a\\u0631 \\u0621\\u0646 \\u0621\\u0627\\u062f \\u0630\\u0627\\u0621\\u0628 \\u0631\\u0627\\u0642 \\u0631\\u0627\\u062d \\u0631\\u0627\\u0645 \\u0642\\u0637 \\u062d\\u0632 \\u063a\\u0627\\u0632\\u0644 \\u063a\\u0632\\u0644 \\u062c\\u0621\\u0621\\u0630\\u0631 \\u0621\\u0633\\u0643\\u0631\\u0646 \\u064a\\u0647 \\u0621\\u0639\\u0631\\u0641 \\u0635\\u062d\\u0648 \\u0621\\u062d \\u062d\\u0648 \\u0642\\u0644\\u0628 \\u062d\\u0627\\u0648\\u0644 \\u0635\\u0644 \\u062d\\u0627\\u0644 \\u0642\\u0636 \\u062d\\u062a \\u0646\\u0642\\u0636 \\u0639\\u0645\\u0631 \\u0647\\u0648 \\u0644\\u064a\\u062a \\u0621\\u0628\\u0644 \\u0647\\u0648 \\u0645\\u0647\\u062c \\u0641\\u0631\\u0642 \\u0621\\u0647\\u0644 \\u0639\\u0634\\u0642 \\u0621\\u0639\\u0638\\u0645 \\u0628\\u0644\\u0648 \\u062d\\u0628 \\u062d\\u0628 \\u0628\\u062e\\u064a\\u0644 \\u0645\\u0627\\u0644 \\u0631\\u0627\\u0636 \\u062d\\u0645\\u0644 \\u0631\\u0636 \\u0631\\u064a\\u0642 \\u062c\\u0631\\u064a\\u0639 \\u0639\\u0631\\u0641 \\u0621\\u0646 \\u0645\\u0646 \\u0621\\u062d\\u0644 \\u0633\\u0644\\u0648 \\u0621\\u0637\\u0631\\u0642 \\u0621\\u0637\\u0631\\u0642 \\u0648\\u062e\\u0637 \\u0645\\u0641\\u0631\\u0642 \\u0641\\u0627\\u0631\\u0642 \\u0630\\u0627\\u062a \\u0634\\u0628\\u064a\\u0628 \\u0644\\u0647\\u0648 \\u0642\\u0637\\u0639 \\u0633\\u0628\\u0627\\u0631\\u064a\\u062a \\u0639\\u0644 \\u0621\\u0631\\u062d\\u0628 \\u0633\\u0627\\u0637 \\u0641\\u064a\\u0627\\u0641 \\u0645\\u0646\\u0627\\u0633\\u0645 \\u0637\\u0648 \\u0621\\u0644 \\u062f\\u0646\\u064a \\u063a\\u064a\\u062b \\u0642\\u0627\\u0639 \\u0633\\u0639\\u064a\\u062f \\u0641\\u0647 \\u0639\\u0631\\u0641 \\u062c\\u062f\\u0648 \\u062e\\u0644\\u0627\\u0644 \\u0633\\u0644\\u0637 \\u0647\\u0632\\u064a\\u0632 \\u0633\\u0637 \\u0633\\u064a\\u0641 \\u0634\\u062f \\u0647\\u0627\\u0645 \\u0648\\u0645 \\u0648\\u063a \\u0634\\u062f \\u0631\\u0627\\u062d \\u0646\\u0627\\u0631 \\u062c\\u062d\\u064a\\u0645 \\u0639\\u0644 \\u0639\\u062f \\u0621\\u0645 \\u0639\\u0644 \\u0641\\u0627\\u062f \\u062c\\u0646 \\u0645\\u0621\\u0648 \\u0642\\u0648 \\u0621\\u0630 \\u0621\\u0628\\u0637\\u0627\\u0644 \\u0637\\u0627\\u0644 \\u0641\\u0627\\u062d \\u0637\\u0644 \\u0648\\u0645 \\u0643\\u0641\\u0627\\u062d \\u0642\\u0648 \\u0633\\u0639 \\u0639\\u0644 \\u062d\\u062a \\u0639\\u0644 \\u0647\\u0627\\u0645 \\u0639\\u0632\\u0645 \\u0642\\u0648 \\u0639\\u062a\\u0644 \\u063a\\u0627 \\u0642\\u0635\\u0648 \\u062a\\u0627 \\u0633\\u0628\\u0642 \\u0621\\u0642\\u0648\\u0627\\u0644 \\u0639\\u0627\\u0644 \\u0641\\u064a \\u0648\\u0639\\u062f \\u0645\\u0635\\u062f\\u0627\\u0642 \\u0639\\u0631\\u0641 \\u0633\\u0647\\u0648 \\u062d\\u0644 \\u0639\\u0633\\u064a\\u0631 \\u0645\\u0634\\u0643\\u0644 \\u0631\\u0627\\u0639 \\u063a\\u064a\\u0631 \\u0631\\u062c \\u0621\\u0630 \\u0646\\u0642\\u0637\\u0639 \\u0631\\u062c\\u0648 \\u0639\\u064a\\u0644\\u0645 \\u0645\\u062e\\u0636\\u0645 \\u0639\\u0627\\u0644\\u0645 \\u0646\\u064a\\u0631 \\u0647\\u062f \\u0641\\u0648 \\u0641\\u062a\\u0648 \\u0642\\u0635\\u064a\\u062f \\u0639\\u0627\\u0645 \\u0639\\u0635\\u0631 \\u062d\\u062f\\u064a\\u062b\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding Categorical Features\n",
        "\n",
        "categorical_columns = ['poem meter', 'poem theme', 'poet era']\n",
        "\n",
        "print(\"Categorical Features:\", categorical_columns)\n",
        "\n",
        "# Encoding categorical features\n",
        "for col in categorical_columns:\n",
        "\n",
        "  le = LabelEncoder()\n",
        "\n",
        "  values = list(features[col].values)\n",
        "\n",
        "  le.fit(values)\n",
        "\n",
        "  features['encode ' + col] = le.transform(values)\n",
        "\n",
        "features.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "Q7kMeIdjfoA5",
        "outputId": "3ec0deef-62d1-4037-b23c-63d53425a598"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Categorical Features: ['poem meter', 'poem theme', 'poet era']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        poem meter                                        poem verses  \\\n",
              "0       بحر الخفيف  [أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...   \n",
              "1  بحر مجزوء الرمل  [مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...   \n",
              "2       بحر البسيط  [العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...   \n",
              "3       بحر الكامل  [لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...   \n",
              "4       بحر الوافر  [يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...   \n",
              "\n",
              "    poem theme        poet era  \\\n",
              "0  قصيدة دينية  العصر العثماني   \n",
              "1  قصيدة دينية  العصر العثماني   \n",
              "2     قصيدة ذم  العصر العثماني   \n",
              "3   قصيدة عامه  العصر العثماني   \n",
              "4   قصيدة عامه  العصر العثماني   \n",
              "\n",
              "                                     Normalized Poem  \\\n",
              "0  بحر الخفيف ءصبح الملك للذي فطر الخل ق بتقدير ل...   \n",
              "1  بحر مجزوء الرمل من ءي مولا ارتجي ولاي باب التج...   \n",
              "2  بحر البسيط العبد عبدك يا من ءنت سيده وليس غيرك...   \n",
              "3  بحر الكامل لو كنت ءطمع بالمنام توهما لسالت طيف...   \n",
              "4  بحر الوافر يعد علي ءنفاسي ذنوبا ءذا ما قلت ءفد...   \n",
              "\n",
              "                                              Tokens  \\\n",
              "0  [بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, ق,...   \n",
              "1  [بحر, مجزوء, الرمل, من, ءي, مولا, ارتجي, ولاي,...   \n",
              "2  [بحر, البسيط, العبد, عبدك, يا, من, ءنت, سيده, ...   \n",
              "3  [بحر, الكامل, لو, كنت, ءطمع, بالمنام, توهما, ل...   \n",
              "4  [بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, ما...   \n",
              "\n",
              "                                     Filtered Tokens  \\\n",
              "0  [بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, بت...   \n",
              "1  [بحر, مجزوء, الرمل, ءي, مولا, ارتجي, ولاي, باب...   \n",
              "2  [بحر, البسيط, العبد, عبدك, ءنت, سيده, وليس, غي...   \n",
              "3  [بحر, الكامل, كنت, ءطمع, بالمنام, توهما, لسالت...   \n",
              "4  [بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, قل...   \n",
              "\n",
              "                                      Stemmed Tokens  \\\n",
              "0  [حر, خفيف, ءصبح, ملك, الذي, طر, خل, تقدير, عزي...   \n",
              "1  [حر, مجزوء, رمل, ءي, مول, رتج, اي, اب, تج, له,...   \n",
              "2  [حر, بسيط, عبد, عبد, ءن, ده, ليس, غير, ءوصاب, ...   \n",
              "3  [حر, كامل, نت, ءطمع, منام, وهم, سال, طيف, ءن, ...   \n",
              "4  [حر, وافر, عد, على, ءنفاس, ذنوب, ءذ, قل, ءفد, ...   \n",
              "\n",
              "                                   Stemmed Poem Text  encode poem meter  \\\n",
              "0  حر خفيف ءصبح ملك الذي طر خل تقدير عزيز عليم غا...                  7   \n",
              "1  حر مجزوء رمل ءي مول رتج اي اب تج له حي رازق عط...                 36   \n",
              "2  حر بسيط عبد عبد ءن ده ليس غير ءوصاب نجد ءن سبي...                  4   \n",
              "3  حر كامل نت ءطمع منام وهم سال طيف ءن زور كرم صد...                 15   \n",
              "4  حر وافر عد على ءنفاس ذنوب ءذ قل ءفد حبيب ءبعد ...                 27   \n",
              "\n",
              "   encode poem theme  encode poet era  \n",
              "0                  4                4  \n",
              "1                  4                4  \n",
              "2                  5                4  \n",
              "3                 10                4  \n",
              "4                 10                4  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2c122051-1094-4411-8d6c-1af9865b0ee5\" 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>poem meter</th>\n",
              "      <th>poem verses</th>\n",
              "      <th>poem theme</th>\n",
              "      <th>poet era</th>\n",
              "      <th>Normalized Poem</th>\n",
              "      <th>Tokens</th>\n",
              "      <th>Filtered Tokens</th>\n",
              "      <th>Stemmed Tokens</th>\n",
              "      <th>Stemmed Poem Text</th>\n",
              "      <th>encode poem meter</th>\n",
              "      <th>encode poem theme</th>\n",
              "      <th>encode poet era</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>بحر الخفيف</td>\n",
              "      <td>[أَصبَحَ المُلك لِلَّذي فَطر الخَل, قَ بِتَقدي...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الخفيف ءصبح الملك للذي فطر الخل ق بتقدير ل...</td>\n",
              "      <td>[بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, ق,...</td>\n",
              "      <td>[بحر, الخفيف, ءصبح, الملك, للذي, فطر, الخل, بت...</td>\n",
              "      <td>[حر, خفيف, ءصبح, ملك, الذي, طر, خل, تقدير, عزي...</td>\n",
              "      <td>حر خفيف ءصبح ملك الذي طر خل تقدير عزيز عليم غا...</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>بحر مجزوء الرمل</td>\n",
              "      <td>[مِن أَي مَولى اِرتَجي, وَلاي باب التَجي, وَال...</td>\n",
              "      <td>قصيدة دينية</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر مجزوء الرمل من ءي مولا ارتجي ولاي باب التج...</td>\n",
              "      <td>[بحر, مجزوء, الرمل, من, ءي, مولا, ارتجي, ولاي,...</td>\n",
              "      <td>[بحر, مجزوء, الرمل, ءي, مولا, ارتجي, ولاي, باب...</td>\n",
              "      <td>[حر, مجزوء, رمل, ءي, مول, رتج, اي, اب, تج, له,...</td>\n",
              "      <td>حر مجزوء رمل ءي مول رتج اي اب تج له حي رازق عط...</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>بحر البسيط</td>\n",
              "      <td>[العَبد عَبدك يا مَن أَنتَ سَيدهُ, وَلَيسَ غَي...</td>\n",
              "      <td>قصيدة ذم</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر البسيط العبد عبدك يا من ءنت سيده وليس غيرك...</td>\n",
              "      <td>[بحر, البسيط, العبد, عبدك, يا, من, ءنت, سيده, ...</td>\n",
              "      <td>[بحر, البسيط, العبد, عبدك, ءنت, سيده, وليس, غي...</td>\n",
              "      <td>[حر, بسيط, عبد, عبد, ءن, ده, ليس, غير, ءوصاب, ...</td>\n",
              "      <td>حر بسيط عبد عبد ءن ده ليس غير ءوصاب نجد ءن سبي...</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>بحر الكامل</td>\n",
              "      <td>[لَو كُنتَ أَطمَع بِالمَنام تَوهما, لَسالَت طَ...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الكامل لو كنت ءطمع بالمنام توهما لسالت طيف...</td>\n",
              "      <td>[بحر, الكامل, لو, كنت, ءطمع, بالمنام, توهما, ل...</td>\n",
              "      <td>[بحر, الكامل, كنت, ءطمع, بالمنام, توهما, لسالت...</td>\n",
              "      <td>[حر, كامل, نت, ءطمع, منام, وهم, سال, طيف, ءن, ...</td>\n",
              "      <td>حر كامل نت ءطمع منام وهم سال طيف ءن زور كرم صد...</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>بحر الوافر</td>\n",
              "      <td>[يعد عَليَّ أَنفاسي ذُنوباً, إِذا ما قُلت أَفد...</td>\n",
              "      <td>قصيدة عامه</td>\n",
              "      <td>العصر العثماني</td>\n",
              "      <td>بحر الوافر يعد علي ءنفاسي ذنوبا ءذا ما قلت ءفد...</td>\n",
              "      <td>[بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, ما...</td>\n",
              "      <td>[بحر, الوافر, يعد, علي, ءنفاسي, ذنوبا, ءذا, قل...</td>\n",
              "      <td>[حر, وافر, عد, على, ءنفاس, ذنوب, ءذ, قل, ءفد, ...</td>\n",
              "      <td>حر وافر عد على ءنفاس ذنوب ءذ قل ءفد حبيب ءبعد ...</td>\n",
              "      <td>27</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c122051-1094-4411-8d6c-1af9865b0ee5')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2c122051-1094-4411-8d6c-1af9865b0ee5 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-2c122051-1094-4411-8d6c-1af9865b0ee5');\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",
              "\n",
              "\n",
              "<div id=\"df-2022791b-5746-4f41-9b25-a62f65a7e752\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2022791b-5746-4f41-9b25-a62f65a7e752')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2022791b-5746-4f41-9b25-a62f65a7e752 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "features",
              "summary": "{\n  \"name\": \"features\",\n  \"rows\": 54701,\n  \"fields\": [\n    {\n      \"column\": \"poem meter\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 66,\n        \"samples\": [\n          \"\\u0628\\u062d\\u0631 \\u0645\\u0646\\u0647\\u0648\\u0643 \\u0627\\u0644\\u0628\\u0633\\u064a\\u0637\",\n          \"\\u0628\\u062d\\u0631 \\u0645\\u062c\\u0632\\u0648\\u0621 \\u0627\\u0644\\u0647\\u0632\\u062c\",\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u062e\\u0641\\u064a\\u0641\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poem verses\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poem theme\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u062f\\u064a\\u0646\\u064a\\u0629\",\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u0630\\u0645\",\n          \"\\u0642\\u0635\\u064a\\u062f\\u0629 \\u063a\\u0632\\u0644\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poet era\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0639\\u062b\\u0645\\u0627\\u0646\\u064a\",\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0639\\u0628\\u0627\\u0633\\u064a\",\n          \"\\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0645\\u0645\\u0644\\u0648\\u0643\\u064a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Normalized Poem\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54701,\n        \"samples\": [\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u062e\\u0641\\u064a\\u0641 \\u064a\\u0627 \\u0639\\u0630\\u0627\\u0631\\u0627 \\u0627\\u0644\\u062c\\u0645\\u0627\\u0644 \\u0648\\u0627\\u0644\\u062d\\u0628 \\u0648\\u0627\\u0644\\u0621\\u062d\\u0644\\u0627\\u0645 \\u0628\\u0644 \\u064a\\u0627 \\u0628\\u0647\\u0627\\u0621 \\u0647\\u0630\\u0627 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u062e\\u0644\\u0642 \\u0627\\u0644\\u0628\\u0644\\u0628\\u0644 \\u0627\\u0644\\u062c\\u0645\\u064a\\u0644 \\u0644\\u064a\\u0634\\u062f\\u0648 \\u0648\\u062e\\u0644\\u0642\\u062a\\u0646 \\u0644\\u0644\\u063a\\u0631\\u0627\\u0645 \\u0627\\u0644\\u0633\\u0639\\u064a\\u062f \\u0648\\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0627\\u0644\\u0631\\u062d\\u064a\\u0628 \\u0643\\u0627\\u0644\\u0642\\u0628\\u0631 \\u0644\\u0648\\u0644\\u0627 \\u0645\\u0627 \\u062a\\u062c\\u0644\\u064a\\u0646 \\u0645\\u0646 \\u0642\\u0637\\u0648\\u0628 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0648\\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0627\\u0644\\u062a\\u064a \\u062a\\u062e\\u0631 \\u0644\\u0647\\u0627 \\u0627\\u0644\\u0621\\u062d\\u0644\\u0627\\u0645 \\u0645\\u0648\\u062a \\u0645\\u062b\\u0642\\u0644 \\u0643\\u0627\\u0644\\u0642\\u064a\\u0648\\u062f \\u0648\\u0627\\u0644\\u0634\\u0628\\u0627\\u0628 \\u0627\\u0644\\u062d\\u0628\\u064a\\u0628 \\u0634\\u064a\\u062e\\u0648\\u062e\\u0629 \\u062a\\u0633\\u0639\\u0627 \\u0621\\u0644\\u0627 \\u0627\\u0644\\u0645\\u0648\\u062a \\u0641\\u064a \\u0637\\u0631\\u064a\\u0642 \\u0643\\u0621\\u0648\\u062f \\u0648\\u0627\\u0644\\u0631\\u0628\\u064a\\u0639 \\u0627\\u0644\\u062c\\u0645\\u064a\\u0644 \\u0641\\u064a \\u0647\\u0627\\u062a\\u0647 \\u0627\\u0644\\u062f\\u0646\\u064a\\u0627 \\u062e\\u0631\\u064a\\u0641 \\u064a\\u0630\\u0648\\u064a \\u0631\\u0641\\u064a\\u0641 \\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0648\\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0627\\u0644\\u0639\\u0630\\u0627\\u0628 \\u0641\\u064a \\u0636\\u0641\\u0629 \\u0627\\u0644\\u062c\\u062f\\u0648\\u0644 \\u0634\\u0648\\u0643 \\u0645\\u0635\\u0641\\u062d \\u0628\\u0627\\u0644\\u062d\\u062f\\u064a\\u062f \\u0648\\u0627\\u0644\\u0637\\u064a\\u0648\\u0631 \\u0627\\u0644\\u062a\\u064a \\u062a\\u063a\\u0646\\u064a \\u0648\\u062a\\u0642\\u0636\\u064a \\u0639\\u064a\\u0634\\u0647\\u0627 \\u0641\\u064a \\u062a\\u0631\\u0646\\u0645 \\u0648\\u063a\\u0631\\u064a\\u062f \\u0621\\u0646\\u0647\\u0627 \\u0641\\u064a \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u062a\\u0634\\u0643\\u0648 \\u0621\\u0644\\u0627 \\u0627\\u0644\\u0621\\u064a\\u0627\\u0645 \\u0639\\u0628\\u0621 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0628\\u0627\\u0644\\u062a\\u063a\\u0631\\u064a\\u062f \\u0648\\u0627\\u0644\\u0621\\u0646\\u0627\\u0634\\u064a\\u062f \\u0621\\u0646\\u0647\\u0627 \\u0634\\u0647\\u0642\\u0627\\u062a \\u062a\\u062a\\u0634\\u0638\\u0627 \\u0645\\u0646 \\u0643\\u0644 \\u0642\\u0644\\u0628 \\u0639\\u0645\\u064a\\u062f \\u0635\\u0648\\u0631\\u0629 \\u0644\\u0644\\u0648\\u062c\\u0648\\u062f \\u0634\\u0648\\u0647\\u0627\\u0621 \\u0644\\u0648\\u0644\\u0627 \\u0634\\u0641\\u0642 \\u0627\\u0644\\u062d\\u0633\\u0646 \\u0641\\u0648\\u0642 \\u062a\\u0644\\u0643 \\u0627\\u0644\\u062e\\u062f\\u0648\\u062f \\u064a\\u0627 \\u0632\\u0647\\u0648\\u0631 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0644\\u0644\\u062d\\u0628 \\u0621\\u0646\\u062a\\u0646 \\u0648\\u0644\\u0643\\u0646\\u0647 \\u0645\\u062e\\u064a\\u0641 \\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0641\\u0633\\u0628\\u064a\\u0644 \\u0627\\u0644\\u063a\\u0631\\u0627\\u0645 \\u062c\\u0645 \\u0627\\u0644\\u0645\\u0647\\u0627\\u0648\\u064a \\u0648\\u0627\\u0641\\u0631 \\u0627\\u0644\\u0647\\u0648\\u0644 \\u0645\\u0633\\u062a\\u0631\\u0627\\u0628 \\u0627\\u0644\\u0635\\u0639\\u064a\\u062f \\u0631\\u063a\\u0645 \\u0645\\u0627 \\u0641\\u064a\\u0647 \\u0645\\u0646 \\u062c\\u0645\\u0627\\u0644 \\u0648\\u0641\\u0646 \\u0639\\u0628\\u0642\\u0631\\u064a \\u0645\\u0627 \\u0621\\u0646 \\u0644\\u0647 \\u0645\\u0646 \\u0645\\u0632\\u064a\\u062f \\u0648\\u0621\\u0646\\u0627\\u0634\\u064a\\u062f \\u062a\\u0633\\u0643\\u0631 \\u0627\\u0644\\u0645\\u0644\\u0621 \\u0627\\u0644\\u0621\\u0639\\u0644\\u0627 \\u0648\\u062a\\u0634\\u062c\\u064a \\u062c\\u0648\\u0627\\u0646\\u062d \\u0627\\u0644\\u062c\\u0644\\u0645\\u0648\\u062f \\u0648\\u0621\\u0631\\u064a\\u062c \\u064a\\u0643\\u0627\\u062f \\u064a\\u0630\\u0647\\u0628 \\u0628\\u0627\\u0644\\u0621\\u0644\\u0628\\u0627\\u0628 \\u0645\\u0627 \\u0628\\u064a\\u0646 \\u063a\\u0627\\u0645\\u0636 \\u0648\\u0634\\u062f\\u064a\\u062f \\u0648\\u0633\\u0628\\u064a\\u0644 \\u0627\\u0644\\u062d\\u064a\\u0627\\u0629 \\u0631\\u062d\\u0628 \\u0648\\u0621\\u0646\\u062a\\u0646 \\u0627\\u0644\\u0644\\u0648\\u0627\\u062a\\u064a \\u062a\\u0641\\u0631\\u0634\\u0646\\u0647 \\u0628\\u0627\\u0644\\u0648\\u0631\\u0648\\u062f \\u0621\\u0646 \\u0621\\u0631\\u062f\\u062a\\u0646 \\u0621\\u0646 \\u064a\\u0643\\u0648\\u0646 \\u0628\\u0647\\u064a\\u062c\\u0627 \\u0631\\u0627\\u0621\\u0639 \\u0627\\u0644\\u0633\\u062d\\u0631 \\u0630\\u0627 \\u062c\\u0645\\u0627\\u0644 \\u0641\\u0631\\u064a\\u062f \\u0621\\u0648 \\u0628\\u0634\\u0648\\u0643 \\u064a\\u062f\\u0645\\u064a \\u0627\\u0644\\u0641\\u0636\\u064a\\u0644\\u0629 \\u0648\\u0627\\u0644\\u062d\\u0628 \\u0648\\u064a\\u0642\\u0636\\u064a \\u0639\\u0644\\u0627 \\u0628\\u0647\\u0627\\u0621 \\u0627\\u0644\\u0648\\u062c\\u0648\\u062f \\u0621\\u0646 \\u0621\\u0631\\u062f\\u062a\\u0646 \\u0621\\u0646 \\u064a\\u0643\\u0648\\u0646 \\u0634\\u0646\\u064a\\u0639\\u0627 \\u0645\\u0638\\u0644\\u0645 \\u0627\\u0644\\u0621\\u0641\\u0642 \\u0645\\u064a\\u062a \\u0627\\u0644\\u062a\\u063a\\u0631\\u064a\\u062f \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0639\\u0627\\u0645\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u062d\\u062f\\u064a\\u062b\",\n          \"\\u0628\\u062d\\u0631 \\u0627\\u0644\\u0633\\u0631\\u064a\\u0639 \\u0646\\u0648\\u0627\\u0644\\u0643 \\u0627\\u0644\\u0633\\u0639\\u062f\\u064a \\u064a\\u0627 \\u0633\\u064a\\u062f\\u064a \\u0621\\u0631\\u062c\\u0648 \\u0639\\u0644\\u0627 \\u0639\\u0627\\u062f\\u0627\\u062a\\u0647 \\u0645\\u0631\\u0628\\u0639\\u0647 \\u0644\\u064a \\u0621\\u0634\\u0647\\u0631 \\u0621\\u0631\\u0628\\u0639\\u0629 \\u0621\\u062e\\u0631\\u062a \\u0641\\u062d\\u0638\\u064a \\u0627\\u0644\\u0645\\u0634\\u0621\\u0648\\u0645 \\u0628\\u0627\\u0644\\u0621\\u0631\\u0628\\u0639\\u0647 \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u0645\\u0645\\u0644\\u0648\\u0643\\u064a\",\n          \"\\u0628\\u062d\\u0631 \\u0645\\u062c\\u0632\\u0648\\u0621 \\u0627\\u0644\\u0633\\u0631\\u064a\\u0639 \\u0644\\u0627 \\u062a\\u0639\\u062c\\u0628\\u064a \\u0621\\u0646 \\u062a\\u0631\\u064a \\u062c\\u0633\\u0645\\u064a \\u0646\\u062d\\u064a\\u0644\\u0627 \\u064a\\u0634\\u0641 \\u0648\\u0643\\u0627\\u0646 \\u0645\\u0627\\u0621 \\u0627\\u0644\\u0635\\u0628\\u0627 \\u0639\\u0646 \\u0633\\u0642\\u064a\\u0647 \\u0644\\u0627 \\u064a\\u0643\\u0641 \\u0639\\u0631\\u0636\\u062a\\u0647 \\u0644\\u0644\\u0647\\u0648\\u0627\\u0627 \\u0641\\u0645\\u0627 \\u0644\\u0647 \\u0644\\u0627 \\u064a\\u062c\\u0641 \\u0642\\u0635\\u064a\\u062f\\u0629 \\u0642\\u0635\\u064a\\u0631\\u0647 \\u0627\\u0644\\u0639\\u0635\\u0631 \\u0627\\u0644\\u062d\\u062f\\u064a\\u062b\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Filtered Tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stemmed Tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stemmed Poem Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 54692,\n        \"samples\": [\n          \"\\u062d\\u0631 \\u0648\\u0627\\u0641\\u0631 \\u0643\\u0648 \\u062e\\u0627\\u0644 \\u062c\\u0647 \\u0645\\u0644\\u064a\\u062d \\u0643\\u0633 \\u0645\\u0644\\u0627\\u062d \\u062c\\u0645\\u0627\\u0644 \\u0644\\u0633\\u062a \\u0645\\u0644 \\u0638\\u0631 \\u0621\\u0644 \\u0643\\u064a\\u0641 \\u0621\\u0630 \\u0631\\u0621\\u064a \\u0648\\u062c\\u0647 \\u062e\\u0627\\u0644 \\u0642\\u0635\\u064a\\u062f \\u0645\\u062f\\u062d \\u0639\\u0635\\u0631 \\u0639\\u0628\\u0627\\u0633\",\n          \"\\u062d\\u0631 \\u062e\\u0641\\u064a\\u0641 \\u062e\\u0641 \\u062a\\u0627\\u0628\\u0627 \\u0627\\u062a\\u0628\\u0627 \\u062a\\u0642\\u0635 \\u0635\\u063a\\u0631 \\u0621\\u0644\\u0641\\u0627\\u0638 \\u0621\\u0644\\u062d\\u0627\\u0638 \\u062e\\u0641\\u0636 \\u0639\\u064a\\u0646 \\u0621\\u0646 \\u0633\\u0645 \\u0627\\u0632\\u062f\\u0647\\u0627\\u0621 \\u063a\\u0636 \\u0646\\u0641\\u0633 \\u0621\\u0646 \\u0637\\u0645 \\u0627\\u063a\\u062a\\u064a\\u0627\\u0638 \\u0637\\u0627\\u0644 \\u0639\\u0638 \\u0644\\u064a\\u0627\\u0644 \\u0648\\u0639\\u0638 \\u0621\\u0646 \\u0631\\u0639\\u0648\\u064a \\u0627\\u062a\\u0639\\u0627\\u0638 \\u062d\\u0641\\u0638 \\u0644\\u0647 \\u0621\\u0636\\u0627\\u0639 \\u0621\\u0645\\u0627\\u0646 \\u062d\\u0641\\u0627\\u0638 \\u0643\\u0646 \\u062d\\u062a\\u0641\\u0627\\u0638 \\u0621\\u064a\\u0642\\u0638 \\u0637\\u0631\\u0641 \\u0621\\u0646\\u0627\\u0645 \\u064a\\u0627\\u0645 \\u0642\\u064a\\u0633 \\u0631\\u0642\\u0648\\u062f \\u0621\\u064a\\u0642\\u0627\\u0638 \\u0642\\u0635\\u064a\\u062f \\u0639\\u0627\\u0645 \\u0639\\u0635\\u0631 \\u0645\\u0645\\u0644\\u0648\\u0643\",\n          \"\\u062d\\u0631 \\u0637\\u0648\\u064a\\u0644 \\u0621\\u0647\\u0644 \\u062e\\u0628\\u0631 \\u0646\\u062c\\u064a\\u062f \\u0631\\u0648 \\u0641\\u062c\\u0631 \\u062f\\u0645\\u0639 \\u0646\\u062c\\u064a\\u062f \\u0631\\u0648 \\u0631\\u0648 \\u0645\\u0633\\u064a\\u0627\\u0644 \\u0644\\u0627\\u0639 \\u0621\\u0646 \\u0632\\u0627\\u062f \\u0633\\u0643\\u0628 \\u0633\\u0643\\u0628 \\u063a\\u0631 \\u0621\\u0646 \\u0621\\u0627\\u062f \\u0630\\u0627\\u0621\\u0628 \\u0631\\u0627\\u0642 \\u0631\\u0627\\u062d \\u0631\\u0627\\u0645 \\u0642\\u0637 \\u062d\\u0632 \\u063a\\u0627\\u0632\\u0644 \\u063a\\u0632\\u0644 \\u062c\\u0621\\u0621\\u0630\\u0631 \\u0621\\u0633\\u0643\\u0631\\u0646 \\u064a\\u0647 \\u0621\\u0639\\u0631\\u0641 \\u0635\\u062d\\u0648 \\u0621\\u062d \\u062d\\u0648 \\u0642\\u0644\\u0628 \\u062d\\u0627\\u0648\\u0644 \\u0635\\u0644 \\u062d\\u0627\\u0644 \\u0642\\u0636 \\u062d\\u062a \\u0646\\u0642\\u0636 \\u0639\\u0645\\u0631 \\u0647\\u0648 \\u0644\\u064a\\u062a \\u0621\\u0628\\u0644 \\u0647\\u0648 \\u0645\\u0647\\u062c \\u0641\\u0631\\u0642 \\u0621\\u0647\\u0644 \\u0639\\u0634\\u0642 \\u0621\\u0639\\u0638\\u0645 \\u0628\\u0644\\u0648 \\u062d\\u0628 \\u062d\\u0628 \\u0628\\u062e\\u064a\\u0644 \\u0645\\u0627\\u0644 \\u0631\\u0627\\u0636 \\u062d\\u0645\\u0644 \\u0631\\u0636 \\u0631\\u064a\\u0642 \\u062c\\u0631\\u064a\\u0639 \\u0639\\u0631\\u0641 \\u0621\\u0646 \\u0645\\u0646 \\u0621\\u062d\\u0644 \\u0633\\u0644\\u0648 \\u0621\\u0637\\u0631\\u0642 \\u0621\\u0637\\u0631\\u0642 \\u0648\\u062e\\u0637 \\u0645\\u0641\\u0631\\u0642 \\u0641\\u0627\\u0631\\u0642 \\u0630\\u0627\\u062a \\u0634\\u0628\\u064a\\u0628 \\u0644\\u0647\\u0648 \\u0642\\u0637\\u0639 \\u0633\\u0628\\u0627\\u0631\\u064a\\u062a \\u0639\\u0644 \\u0621\\u0631\\u062d\\u0628 \\u0633\\u0627\\u0637 \\u0641\\u064a\\u0627\\u0641 \\u0645\\u0646\\u0627\\u0633\\u0645 \\u0637\\u0648 \\u0621\\u0644 \\u062f\\u0646\\u064a \\u063a\\u064a\\u062b \\u0642\\u0627\\u0639 \\u0633\\u0639\\u064a\\u062f \\u0641\\u0647 \\u0639\\u0631\\u0641 \\u062c\\u062f\\u0648 \\u062e\\u0644\\u0627\\u0644 \\u0633\\u0644\\u0637 \\u0647\\u0632\\u064a\\u0632 \\u0633\\u0637 \\u0633\\u064a\\u0641 \\u0634\\u062f \\u0647\\u0627\\u0645 \\u0648\\u0645 \\u0648\\u063a \\u0634\\u062f \\u0631\\u0627\\u062d \\u0646\\u0627\\u0631 \\u062c\\u062d\\u064a\\u0645 \\u0639\\u0644 \\u0639\\u062f \\u0621\\u0645 \\u0639\\u0644 \\u0641\\u0627\\u062f \\u062c\\u0646 \\u0645\\u0621\\u0648 \\u0642\\u0648 \\u0621\\u0630 \\u0621\\u0628\\u0637\\u0627\\u0644 \\u0637\\u0627\\u0644 \\u0641\\u0627\\u062d \\u0637\\u0644 \\u0648\\u0645 \\u0643\\u0641\\u0627\\u062d \\u0642\\u0648 \\u0633\\u0639 \\u0639\\u0644 \\u062d\\u062a \\u0639\\u0644 \\u0647\\u0627\\u0645 \\u0639\\u0632\\u0645 \\u0642\\u0648 \\u0639\\u062a\\u0644 \\u063a\\u0627 \\u0642\\u0635\\u0648 \\u062a\\u0627 \\u0633\\u0628\\u0642 \\u0621\\u0642\\u0648\\u0627\\u0644 \\u0639\\u0627\\u0644 \\u0641\\u064a \\u0648\\u0639\\u062f \\u0645\\u0635\\u062f\\u0627\\u0642 \\u0639\\u0631\\u0641 \\u0633\\u0647\\u0648 \\u062d\\u0644 \\u0639\\u0633\\u064a\\u0631 \\u0645\\u0634\\u0643\\u0644 \\u0631\\u0627\\u0639 \\u063a\\u064a\\u0631 \\u0631\\u062c \\u0621\\u0630 \\u0646\\u0642\\u0637\\u0639 \\u0631\\u062c\\u0648 \\u0639\\u064a\\u0644\\u0645 \\u0645\\u062e\\u0636\\u0645 \\u0639\\u0627\\u0644\\u0645 \\u0646\\u064a\\u0631 \\u0647\\u062f \\u0641\\u0648 \\u0641\\u062a\\u0648 \\u0642\\u0635\\u064a\\u062f \\u0639\\u0627\\u0645 \\u0639\\u0635\\u0631 \\u062d\\u062f\\u064a\\u062b\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"encode poem meter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 65,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          59,\n          45,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"encode poem theme\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          4,\n          5,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"encode poet era\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'{features.shape=}')\n",
        "print(f'{target.shape=}')\n",
        "\n",
        "print(f'{dataset_project.shape=}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXz7NfdTxnav",
        "outputId": "5728d778-2ff3-483b-8341-dadb8460b938"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "features.shape=(54701, 12)\n",
            "target.shape=(54701,)\n",
            "dataset_project.shape=(54701, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding Target\n",
        "le = LabelEncoder()\n",
        "target_encoding = le.fit_transform(target)"
      ],
      "metadata": {
        "id": "9TRohB7gjPaF"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(np.unique(target_encoding))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZXnt9P4xo7F",
        "outputId": "c0d8c331-798c-4c38-c19c-37acb5ac12b3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "348"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wypG8n2R911q"
      },
      "source": [
        "# .3. Features Extraction\n",
        "\n",
        "- 1. Character Features\n",
        "- 2. Lexical Features\n",
        "- 3. Specific Words Per Aurthors\n",
        "- 4. Syntatic Features\n",
        "- 5. Poetry Features\n",
        "- 6. Structural Features\n",
        "- 7. Semantic Features\n",
        "\n",
        "- Classification Features:\n",
        "    - Word-level features: word frequencies, n-grams, word embeddings.\n",
        "    - Poetic features: rhyme patterns, meter, poetic devices.\n",
        "    - Structural features: stanza length, line length."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oI_PtkEFo9Mi"
      },
      "source": [
        "## 3.1. Character Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "ZjMbX24ao8W4"
      },
      "outputs": [],
      "source": [
        "# Function to extract character n-grams\n",
        "def extract_char_ngrams(texts, n):\n",
        "  vectorizer = CountVectorizer(analyzer='char', ngram_range=(n, n))\n",
        "  ngram_counts = vectorizer.fit_transform(texts)\n",
        "  return vectorizer, ngram_counts\n",
        "\n",
        "\n",
        "# Function to select top features using Chi-Squared test\n",
        "def select_top_features_chi2(X, y, k=500):\n",
        "  chi2_selector = SelectKBest(chi2, k=k)\n",
        "  X_kbest = chi2_selector.fit_transform(X, y)\n",
        "  return chi2_selector, X_kbest\n",
        "\n",
        "\n",
        "# Function to select top features using Information Gain\n",
        "def select_top_features_ig(X, y, k=500):\n",
        "  ig_scores = mutual_info_classif(X, y)\n",
        "  top_indices = ig_scores.argsort()[-k:][::-1]\n",
        "  return top_indices\n",
        "\n",
        "\n",
        "# Function to perform PCA\n",
        "def perform_pca(X, n_components=1):\n",
        "  pca = TruncatedSVD(n_components=n_components)\n",
        "  X_pca = pca.fit_transform(X)\n",
        "  return pca, X_pca"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size = 0.2, random_state = 42)\n",
        "\n",
        "# Print the shapes of the training and testing sets\n",
        "print(f\"Shape of X_train: {X_train.shape}\")\n",
        "print(f\"Shape of X_test: {X_test.shape}\")\n",
        "print(f\"Shape of y_train: {y_train.shape}\")\n",
        "print(f\"Shape of y_test: {y_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BFhMnJLpug1M",
        "outputId": "0dee43c7-79d5-4b1d-a462-b4878dbdfeb3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (43760, 12)\n",
            "Shape of X_test: (10941, 12)\n",
            "Shape of y_train: (43760,)\n",
            "Shape of y_test: (10941,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train"
      ],
      "metadata": {
        "id": "6hDhD2TYFm3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dictionary to store the extracted character n-grams\n",
        "char_ngrams_features = {}\n",
        "\n",
        "# Extracting character 3-grams\n",
        "n = 3\n",
        "vectorizer, ngram_counts = extract_char_ngrams(X_train['Stemmed Poem Text'], n)\n",
        "char_ngrams_features[n] = {\n",
        "  'feature_names': vectorizer.get_feature_names_out(),\n",
        "  'counts': ngram_counts.toarray()\n",
        "}\n",
        "\n",
        "\n",
        "# Perform Chi-Squared feature selection\n",
        "chi2_selector, X_chi2_selected = select_top_features_chi2(ngram_counts, y_train, k=500)\n",
        "\n",
        "# Perform Information Gain feature selection\n",
        "ig_top_indices = select_top_features_ig(ngram_counts, y_train, k=500)\n",
        "\n",
        "# Get selected feature names\n",
        "selected_feature_names_chi2 = vectorizer.get_feature_names_out()[chi2_selector.get_support()]\n",
        "selected_feature_names_ig = vectorizer.get_feature_names_out()[ig_top_indices]\n",
        "\n",
        "\n",
        "# Perform PCA on the selected features from Chi-Squared\n",
        "pca_chi2, X_pca_chi2 = perform_pca(X_chi2_selected, n_components=1)  # Provide an integer value\n",
        "\n",
        "\n",
        "# Perform Information Gain feature selection\n",
        "ig_top_indices = select_top_features_ig(ngram_counts, y_train, k=500)\n",
        "\n",
        "# Extract selected features for Information Gain\n",
        "X_ig_selected = ngram_counts[:, ig_top_indices]\n",
        "\n",
        "# Perform PCA on the selected features from Information Gain\n",
        "pca_ig, X_pca_ig = perform_pca(X_ig_selected, n_components=1)  # Provide an integer value\n",
        "\n",
        "\n",
        "# Store selected features and PCA results in the dictionary\n",
        "char_ngrams_features[n]['selected_features_chi2'] = {\n",
        "  'feature_names': selected_feature_names_chi2,\n",
        "  'counts': X_chi2_selected.toarray(),\n",
        "  'pca_counts': X_pca_chi2\n",
        "}\n",
        "char_ngrams_features[n]['selected_features_ig'] = {\n",
        "  'feature_names': selected_feature_names_ig,\n",
        "  'counts': X_ig_selected.toarray(),\n",
        "  'pca_counts': X_pca_ig\n",
        "}\n"
      ],
      "metadata": {
        "id": "3-q8NzdkFnHt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the results\n",
        "print(\"Selected features using Chi-Squared:\")\n",
        "print(char_ngrams_features[n]['selected_features_chi2']['feature_names'])\n",
        "print(\"PCA transformed data shape (Chi-Squared):\", X_pca_chi2.shape)\n",
        "print(\"Values of the first 20 lines of X_pca_chi2:\")\n",
        "print(X_pca_chi2[:20])\n"
      ],
      "metadata": {
        "id": "jOCr6FxWFrSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSelected features using Information Gain:\")\n",
        "print(char_ngrams_features[n]['selected_features_ig']['feature_names'])\n",
        "print(\"PCA transformed data shape (Information Gain):\", X_pca_ig.shape)\n",
        "\n",
        "print(\"Values of the first 20 lines of X_pca_chi2:\")\n",
        "print(X_pca_ig[:20])"
      ],
      "metadata": {
        "id": "hAURmHCrFrV9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a DataFrame for X_pca_ig and X_pca_chi2\n",
        "# Change the column names accordingly\n",
        "df_pca_ig = pd.DataFrame(X_pca_ig, columns=['X_pca_ig'])\n",
        "df_pca_chi2 = pd.DataFrame(X_pca_chi2, columns=['X_pca_chi2'])\n",
        "\n",
        "# Reset the index of X_train[\"Stemmed Poem Text\"]\n",
        "X_train_reset_index = X_train[\"Stemmed Poem Text\"].reset_index(drop=True)\n",
        "\n",
        "# Concatenate X_train_reset_index with the PCA transformed data\n",
        "X_train_char = pd.concat([X_train_reset_index, df_pca_ig, df_pca_chi2], axis=1)\n",
        "\n",
        "# Display the new DataFrame\n",
        "X_train_char.head()\n"
      ],
      "metadata": {
        "id": "K3RnDK44FtJk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test"
      ],
      "metadata": {
        "id": "ZQqHUPAQFwcn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dictionary to store the extracted character n-grams\n",
        "char_ngrams_features_test = {}\n",
        "\n",
        "# Extracting character 3-grams for test data\n",
        "n = 3\n",
        "vectorizer_test, ngram_counts_test = extract_char_ngrams(X_test['Stemmed Poem Text'], n)\n",
        "char_ngrams_features_test[n] = {\n",
        "    'feature_names': vectorizer_test.get_feature_names_out(),\n",
        "    'counts': ngram_counts_test.toarray()\n",
        "}\n",
        "\n",
        "# Perform Chi-Squared feature selection for test data\n",
        "chi2_selector_test, X_chi2_selected_test = select_top_features_chi2(ngram_counts_test, y_test, k=500)\n",
        "\n",
        "# Perform Information Gain feature selection for test data\n",
        "ig_top_indices_test = select_top_features_ig(ngram_counts_test, y_test, k=500)\n",
        "\n",
        "# Get selected feature names for test data\n",
        "selected_feature_names_chi2_test = vectorizer_test.get_feature_names_out()[chi2_selector_test.get_support()]\n",
        "selected_feature_names_ig_test = vectorizer_test.get_feature_names_out()[ig_top_indices_test]\n",
        "\n",
        "# Perform PCA on the selected features from Chi-Squared for test data\n",
        "pca_chi2_test, X_pca_chi2_test = perform_pca(X_chi2_selected_test, n_components=1)  # Provide an integer value\n",
        "\n",
        "# Extract selected features for Information Gain for test data\n",
        "X_ig_selected_test = ngram_counts_test[:, ig_top_indices_test]\n",
        "\n",
        "# Perform PCA on the selected features from Information Gain for test data\n",
        "pca_ig_test, X_pca_ig_test = perform_pca(X_ig_selected_test, n_components=1)  # Provide an integer value\n",
        "\n",
        "\n",
        "# Store selected features and PCA results in the dictionary for test data\n",
        "char_ngrams_features_test[n]['selected_features_chi2'] = {\n",
        "  'feature_names': selected_feature_names_chi2_test,\n",
        "  'counts': X_chi2_selected_test.toarray(),\n",
        "  'pca_counts': X_pca_chi2_test\n",
        "}\n",
        "char_ngrams_features_test[n]['selected_features_ig'] = {\n",
        "  'feature_names': selected_feature_names_ig_test,\n",
        "  'counts': X_ig_selected_test.toarray(),\n",
        "  'pca_counts': X_pca_ig_test\n",
        "}"
      ],
      "metadata": {
        "id": "eAGUA54RFtL1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the results for test data\n",
        "print(\"Selected features using Chi-Squared for test data:\")\n",
        "print(char_ngrams_features_test[n]['selected_features_chi2']['feature_names'])\n",
        "print(\"PCA transformed data shape (Chi-Squared) for test data:\", X_pca_chi2_test.shape)\n",
        "print(\"Values of the first 20 lines of X_pca_chi2 for test data:\")\n",
        "print(X_pca_chi2_test[:20])"
      ],
      "metadata": {
        "id": "MbBfDW0tF2yD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSelected features using Information Gain for test data:\")\n",
        "print(char_ngrams_features_test[n]['selected_features_ig']['feature_names'])\n",
        "print(\"PCA transformed data shape (Information Gain) for test data:\", X_pca_ig_test.shape)\n",
        "\n",
        "print(\"Values of the first 20 lines of X_pca_chi2 for test data:\")\n",
        "print(X_pca_ig_test[:20])"
      ],
      "metadata": {
        "id": "MoFYlIl4F24A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a DataFrame for X_pca_ig and X_pca_chi2 for test data\n",
        "# Change the column names accordingly\n",
        "df_pca_ig_test = pd.DataFrame(X_pca_ig_test, columns=['X_pca_ig_test'])\n",
        "df_pca_chi2_test = pd.DataFrame(X_pca_chi2_test, columns=['X_pca_chi2_test'])\n",
        "\n",
        "# Reset the index of X_test[\"Stemmed Poem Text\"]\n",
        "X_test_reset_index = X_test[\"Stemmed Poem Text\"].reset_index(drop=True)\n",
        "\n",
        "# Concatenate X_test_reset_index with the PCA transformed data for test data\n",
        "X_test_char = pd.concat([X_test_reset_index, df_pca_ig_test, df_pca_chi2_test], axis=1)\n",
        "\n",
        "# Display the new DataFrame for test data\n",
        "X_test_char.head()"
      ],
      "metadata": {
        "id": "gFhK6K1EF26a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2. TF-IDF (Term Frequency-Inverse Document Frequency)"
      ],
      "metadata": {
        "id": "bLgHHlrB_w1n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform the text data into TF-IDF feature vectors\n",
        "vectorizer = TfidfVectorizer()\n",
        "features_tfidf = vectorizer.fit_transform(features['Normalized Poem'])"
      ],
      "metadata": {
        "id": "4v67W4C5kqNB"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxuASM-nCInr"
      },
      "source": [
        "# .4. Feature Selection\n",
        "\n",
        "\n",
        "Principal Componrnt Analysis (PCA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3mc7ICT-lwot"
      },
      "source": [
        "# .4.2 Split Dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size = 0.2, random_state = 42)\n",
        "\n",
        "# Print the shapes of the training and testing sets\n",
        "print(f\"Shape of X_train: {X_train.shape}\")\n",
        "print(f\"Shape of X_test: {X_test.shape}\")\n",
        "print(f\"Shape of y_train: {y_train.shape}\")\n",
        "print(f\"Shape of y_test: {y_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wQrhr08f4YR",
        "outputId": "3060944d-a19c-48b9-beef-c6b72e3bd919"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (43760, 12)\n",
            "Shape of X_test: (10941, 12)\n",
            "Shape of y_train: (43760,)\n",
            "Shape of y_test: (10941,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_tfidf, X_test_tfidf, y_train_tfidf, y_test_tfidf = train_test_split(features_tfidf, target, test_size = 0.2, random_state = 42)\n",
        "\n",
        "# Print the shapes of the training and testing sets\n",
        "print(f\"Shape of X_train: {X_train_tfidf.shape}\")\n",
        "print(f\"Shape of X_test: {X_test_tfidf.shape}\")\n",
        "print(f\"Shape of y_train: {y_train_tfidf.shape}\")\n",
        "print(f\"Shape of y_test: {y_test_tfidf.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-lkfMwuWgBYH",
        "outputId": "3100ef91-e8d5-40ee-897e-ac3225c55364"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (43760, 470392)\n",
            "Shape of X_test: (10941, 470392)\n",
            "Shape of y_train: (43760,)\n",
            "Shape of y_test: (10941,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_en, X_test_en, y_train_en, y_test_en = train_test_split(features, target_encoding, test_size = 0.2, random_state = 42)\n",
        "\n",
        "# Print the shapes of the training and testing sets\n",
        "print(f\"Shape of X_train: {X_train_en.shape}\")\n",
        "print(f\"Shape of X_test: {X_test_en.shape}\")\n",
        "print(f\"Shape of y_train: {y_train_en.shape}\")\n",
        "print(f\"Shape of y_test: {y_test_en.shape}\")"
      ],
      "metadata": {
        "id": "iMPIcN1hkvNp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aeaa35f5-cba4-48b2-c939-ffd3ba2d3eb1"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (43760, 12)\n",
            "Shape of X_test: (10941, 12)\n",
            "Shape of y_train: (43760,)\n",
            "Shape of y_test: (10941,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLgMg37yCQTT"
      },
      "source": [
        "# .5. Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-wwosnbEUt8"
      },
      "source": [
        "## 5.X.1. Training Helper Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FTo1UA7GgFO"
      },
      "source": [
        "## 5.X.2. Evaluation Helper Functions\n",
        "Evaluation metrics ccuracy, precision, recall, F1-score\n",
        "\n",
        "1. **Evaluation Results**\n",
        "\n",
        "The evaluation of our proposed solution involved assessing the performance of the AI models in generating Arabic poetry resembling the styles of renowned poets. The following metrics were utilized for evaluation:\n",
        "\n",
        "- Accuracy: The overall correctness of the generated poems in capturing the essence of the poets' styles.\n",
        "- Precision: The proportion of accurately generated poems resembling the style of the target poets out of all generated poems.\n",
        "- Recall: The proportion of accurately generated poems resembling the style of the target poets out of all potential poems that could be generated.\n",
        "- F1-Score: The harmonic mean of precision and recall, providing a balanced measure of the models' performance in capturing the poets' styles.\n",
        "\n",
        "Through rigorous evaluation using these metrics, we were able to quantitatively assess the effectiveness of our AI models in producing Arabic poetry that mirrors the styles of specific poets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3gIGyLBCUA-"
      },
      "source": [
        "## 5.1. Linear Support Vector Classification (LSVC)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sEuQ455dDeZj"
      },
      "source": [
        "### 5.1.1. Model Training"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the range for the 'C' parameter\n",
        "C_range = [0.01, 0.1, 1, 10, 100]  # Modify the range as needed\n",
        "accuracy_lsvc = []\n",
        "\n",
        "# Train and evaluate the Linear SVC model with different 'C' values\n",
        "for C in C_range:\n",
        "\n",
        "  # Initialize and train the Linear Support Vector Classifier (LSVC) model\n",
        "  model_lsvc = LinearSVC(C = C, random_state = 42)\n",
        "  model_lsvc.fit(X_train_tfidf, y_train_en)\n",
        "\n",
        "  # Make predictions on the test set\n",
        "  y_pred = model_lsvc.predict(X_test_tfidf)\n",
        "\n",
        "  # Evaluate the model's performance | y_test_tfidf\n",
        "  accuracy = accuracy_score(y_test_en, y_pred)\n",
        "  accuracy_lsvc.append(accuracy)\n",
        "  print(f\"C: {C}, Accuracy: {accuracy:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82JjFCpNS_zj",
        "outputId": "485d5134-f89e-4476-89dd-f0e1b7896451"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "C: 0.01, Accuracy: 0.2856\n",
            "C: 0.1, Accuracy: 0.4863\n",
            "C: 1, Accuracy: 0.5759\n",
            "C: 10, Accuracy: 0.5813\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "C: 100, Accuracy: 0.5802\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# report = classification_report(y_test_tfidf, y_pred)\n",
        "# print(\"Classification Report:\\n\" + report)\n",
        "\n",
        "# Plot the accuracy values\n",
        "plt.plot(C_range, accuracy_lsvc, marker='o')\n",
        "plt.title('Accuracy of Linear SVC Model with Different C Values')\n",
        "plt.xlabel('C Value')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.grid(True)\n",
        "\n",
        "# Use logarithmic scale for better visualization of 'C' values\n",
        "plt.xscale('log')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "MeRA7p-STXTA",
        "outputId": "155c7a52-5557-4ea9-94b6-fdb19b38afea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QSzgj4GrDjZB"
      },
      "source": [
        "### 5.1.2. Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the final model with the best 'C' value\n",
        "best_C = 10\n",
        "model_lsvc = LinearSVC(C = best_C, random_state = 42)\n",
        "model_lsvc.fit(X_train_tfidf, y_train_en)"
      ],
      "metadata": {
        "id": "d3HRVFguTX6D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_lsvc = model_lsvc.predict(X_test_tfidf)\n",
        "\n",
        "test_accuracy_lsvc = accuracy_score(y_test_en, y_pred)\n",
        "test_precision_lsvc = precision_score(y_test_en, y_pred)\n",
        "test_recall_lsvc = recall_score(y_test_en, y_pred)\n",
        "test_accuracy_lsvc = accuracy_score(y_test_en, y_pred)\n",
        "\n",
        "\n",
        "print(f\"Final model accuracy with C={best_C}: {test_accuracy_lsvc:.4f}\")\n",
        "print(f\"Final model precision with C={best_C}: {test_precision_lsvc:.4f}\")\n",
        "print(f\"Final model recall with C={best_C}: {test_recall_lsvc:.4f}\")\n",
        "print(f\"Final model accuracy with C={best_C}: {test_accuracy_lsvc:.4f}\")\n",
        "\n",
        "\n",
        "report_lsvc = classification_report(y_test_en, y_pred)\n",
        "print(\"Classification Report:\\n\" + report_lsvc)"
      ],
      "metadata": {
        "id": "dPPQt_f7kNLO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsV0o54PCYoF"
      },
      "source": [
        "## 5.2. FastText"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ct3teKCDDngP"
      },
      "source": [
        "### 5.2.1. Model Training"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data for FastText (FastText expects a specific format)\n",
        "train_data = pd.DataFrame({'label': y_train, 'text': X_train['Stemmed Poem Text']})\n",
        "test_data = pd.DataFrame({'label': y_test, 'text': X_test['Stemmed Poem Text']})\n",
        "\n",
        "# Add the required prefix to the labels\n",
        "train_data['label'] = '__label__' + train_data['label'].astype(str)\n",
        "test_data['label'] = '__label__' + test_data['label'].astype(str)\n",
        "\n",
        "# Save the data in the required format\n",
        "train_data[['label', 'text']].to_csv('train_fasttext.txt', index=False, sep=' ', header=False, quoting=3, escapechar='\\\\')\n",
        "test_data[['label', 'text']].to_csv('test_fasttext.txt', index=False, sep=' ', header=False, quoting=3, escapechar='\\\\')\n",
        "\n",
        "# Train the FastText model\n",
        "model_ft = fasttext.train_supervised(input='train_fasttext.txt', lr=1.0, epoch=25, wordNgrams=2)"
      ],
      "metadata": {
        "id": "xihtZ-UUUpJ1"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "result = model_ft.test('test_fasttext.txt')\n",
        "print(f\"Number of examples: {result[0]}\")\n",
        "print(f\"Precision: {result[1]}\")\n",
        "print(f\"Recall: {result[2]}\")"
      ],
      "metadata": {
        "id": "ccjwqlM91c4f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4eb80cf-d906-4d8e-a126-faaa337191c4"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of examples: 10937\n",
            "Precision: 0.9262137697723325\n",
            "Recall: 0.9262137697723325\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize lists to store epoch numbers and corresponding accuracy values\n",
        "epochs = []\n",
        "accuracy_ft = []\n",
        "results_ft = []\n",
        "\n",
        "# Train the FastText model and collect accuracy values for each epoch\n",
        "for epoch in range(1, 20):\n",
        "  # , wordNgrams = 2\n",
        "  model = fasttext.train_supervised(input='train_fasttext.txt', lr = 0.1, epoch = epoch)\n",
        "  result = model.test('test_fasttext.txt')\n",
        "  epochs.append(epoch)\n",
        "  accuracy_ft.append(result[1])\n",
        "  results_ft.append(result)"
      ],
      "metadata": {
        "id": "UkMIQuL_1dID"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the accuracy values\n",
        "plt.figure(figsize=(6, 4))\n",
        "plt.plot(epochs, accuracy_ft, marker='o')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy of FastText Model During Training')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TQW1kAzHWAit",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "926e8733-a082-43db-9a14-83211e4094dd"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rWAcCQsQDwEe"
      },
      "source": [
        "### 5.2.2. Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test set\n",
        "text_result_ft = model_ft.test('test_fasttext.txt')\n",
        "\n",
        "# Calculate F1-score\n",
        "precision_ft = result[1]\n",
        "recall_ft = result[2]\n",
        "f1_score_ft = 2 * (precision_ft * recall_ft) / (precision_ft + recall_ft)\n",
        "\n",
        "# Print the results\n",
        "print(f\"Number of samples: {text_result_ft[0]}\")\n",
        "print(f\"Precision: {precision_ft}\")\n",
        "print(f\"Recall: {recall_ft}\")\n",
        "print(f\"F1-score: {f1_score_ft}\")"
      ],
      "metadata": {
        "id": "Eau4FYz2WKg_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8f12962-652c-418c-f497-d53d38db8aef"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples: 10937\n",
            "Precision: 0.8644052299533693\n",
            "Recall: 0.8644052299533693\n",
            "F1-score: 0.8644052299533693\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BoumAC53CprB"
      },
      "source": [
        "## 5.3. Random Forest Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZkNmOM8KDp4Y"
      },
      "source": [
        "### 5.3.1. Model Training"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the classifier on the training data\n",
        "rf_classifier.fit(X_train_tfidf, y_train_en)"
      ],
      "metadata": {
        "id": "nnDq8DX-X9P3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test data\n",
        "y_pred_rf = rf_classifier.predict(X_test_tfidf)"
      ],
      "metadata": {
        "id": "oDF_UUuBDL9B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rEHaUp5BDzAQ"
      },
      "source": [
        "### 5.3.2. Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "accuracy_rf = accuracy_score(y_test_en, y_pred_rf)\n",
        "print(f\"Accuracy: {accuracy_rf:.4f}\")\n",
        "\n",
        "report_rf = classification_report(y_test_en, y_pred_rf)\n",
        "print(\"Classification Report:\\n\" + report_rf)"
      ],
      "metadata": {
        "id": "dMpZHHwTvVzM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ha45VUx0Crh9"
      },
      "source": [
        "## 5.4. Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnRLIxFhDsUX"
      },
      "source": [
        "### 5.4.1. Model Training"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the Logistic Regression model\n",
        "model_lr = LogisticRegression()\n",
        "\n",
        "# Fit the model on the training data\n",
        "model_lr.fit(X_train_tfidf, y_train)"
      ],
      "metadata": {
        "id": "XY77YOBfkrsU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the testing data\n",
        "y_pred_lr = model_lr.predict(X_test_tfidf)"
      ],
      "metadata": {
        "id": "OKvgB0Rqkru-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mi5WqZyADYxf"
      },
      "source": [
        "### 5.4.2. Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the accuracy of the model\n",
        "accuracy_lr = accuracy_score(y_test, y_pred_lr)\n",
        "\n",
        "print(f\"Model Accuracy: {accuracy * 100}%\")\n",
        "\n",
        "report_lr = classification_report(y_test_en, y_pred_lr)\n",
        "print(\"Classification Report:\\n\" + report_lr)"
      ],
      "metadata": {
        "id": "I0REgnBlksfR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSpo6gAhD_n6"
      },
      "source": [
        "# .6. Final Result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "748y8JudEH2_"
      },
      "source": [
        "## 6.1. Models Results Comparison"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.1.1. SVM Results"
      ],
      "metadata": {
        "id": "jIHUjAYkRIrL"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mReQf8kjRk5x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.1.2. FastText"
      ],
      "metadata": {
        "id": "ech-0XbCRgXY"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nv-n8wL_RHTq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.1.3. Random Forest"
      ],
      "metadata": {
        "id": "JdRvfLCAYlXB"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HQI4Xri2YlgZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.2. Best Peforming model"
      ],
      "metadata": {
        "id": "NMX9qZ8aQq1x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model FastText gives the best results with Precision of 92.62%"
      ],
      "metadata": {
        "id": "u5rn9yZCRoAO"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZYukZAtErT9"
      },
      "source": [
        "## 6.3. Extra\n",
        "\n",
        "\n",
        "1.2. Loading Second Dataset\n",
        "\n",
        "1.3. Loading Third Dataset\n",
        "\n",
        "Multi-Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0d4iizkxXzpj"
      },
      "source": [
        "## 6.4. Resources"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZLgQp6HkX3yT"
      },
      "source": [
        "### 6.4.1. Datasets\n",
        "- [Ashaar Dataset](https://huggingface.co/datasets/arbml/ashaar)\n",
        "- [Arabic Poetry](https://www.kaggle.com/datasets/ahmedabelal/arabic-poetry)\n",
        "- [Arabic Poetry Dataset (6th - 21st century)\n",
        "](https://www.kaggle.com/datasets/fahd09/arabic-poetry-dataset-478-2017/data)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Draft"
      ],
      "metadata": {
        "id": "8XkG-w7suP3n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Stope here"
      ],
      "metadata": {
        "id": "u7kfV7OguON8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "doc = nlp('السلام عليكم ورحمة الله وبركاته')\n",
        "embeddings = [token.vector for token in doc]\n",
        "print('Word embeddings:', embeddings)\n",
        "\n",
        "\n",
        "\n",
        "import spacy\n",
        "nlp = spacy.blank(\"ar\") # empty English pipeline\n",
        "\n",
        "# create the config with the name of the model\n",
        "# values omitted will get default values\n",
        "config = {\n",
        "    \"model\": {\n",
        "        \"@architectures\": \"spacy-transformers.TransformerModel.v3\",\n",
        "        \"name\": \"aubmindlab/bert-large-arabertv02\"\n",
        "    }\n",
        "}\n",
        "nlp.add_pipe(\"transformer\", config = config)\n",
        "nlp.initialize()\n",
        "doc = nlp(\"فريك الذرة لذيذة\")\n",
        "print(doc._.trf_data) # all the Transformer output is stored here\n",
        "\n",
        "\n",
        "# Transform the text data into TF-IDF feature vectors\n",
        "\n",
        "import spacy\n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "\n",
        "\n",
        "doc_train = nlp(X_train['Normalized Poem'])\n",
        "embeddings_train = [token.vector for token in doc]\n",
        "\n",
        "print('Word embeddings:', embeddings_train)\n"
      ],
      "metadata": {
        "id": "m8sv3LAfuOQX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Latent Dirichlet allocation (LDA)"
      ],
      "metadata": {
        "id": "3xz4L1XFvCa0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QBQ0BBeLEvJi"
      },
      "outputs": [],
      "source": [
        "# Initialize and train the LDA model\n",
        "lda_model = LatentDirichletAllocation(n_components=10, random_state=42)\n",
        "X_train_lda = lda_model.fit_transform(X_train_tfidf)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train_lda, X_test_lda, y_train_lda, y_test_lda = train_test_split(X_train_lda, y_train, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train a classifier (e.g., Linear Support Vector Classifier)\n",
        "model = LinearSVC()\n",
        "model.fit(X_train_lda, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test_lda)\n",
        "\n",
        "# Evaluate the model's performance\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "result = (len(y_test), report)\n",
        "print(f\"Number of examples: {result[0]}\")\n",
        "print(\"Classification Report:\")\n",
        "print(result[1])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install xgboost==1.2.1\n",
        "\n",
        "import xgboost as xgb\n",
        "import joblib\n",
        "\n",
        "# Initialize the XGBoost classifier with linear booster\n",
        "xgb_linear_classifier = xgb.XGBClassifier(booster='gblinear', n_estimators = 100, random_state = 42)\n",
        "# xgb_linear_classifier = xgb.XGBClassifier()\n",
        "\n",
        "# Train the classifier on the training data\n",
        "xgb_linear_classifier.fit(X_train_tfidf, y_train_en)\n",
        "\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = xgb_linear_classifier.predict(X_test_tfidf)\n",
        "\n",
        "# Evaluate the model's performance\n",
        "precision = precision_score(y_test_en, y_pred, average='weighted')\n",
        "recall = recall_score(y_test_en, y_pred, average='weighted')\n",
        "\n",
        "# Displaying the results in the same format as the FastText evaluation\n",
        "result = (len(y_test), precision, recall)\n",
        "print(f\"Number of examples: {result[0]}\")\n",
        "print(f\"Precision: {result[1]}\")\n",
        "print(f\"Recall: {result[2]}\")"
      ],
      "metadata": {
        "id": "elA-tJOysxQc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the XGBoost classifier\n",
        "xgb_classifier = xgb.XGBClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the classifier on the training data\n",
        "xgb_classifier.fit(X_train_tfidf, y_train_en)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = xgb_classifier.predict(X_test_tfidf)\n",
        "\n",
        "# Evaluate the model's performance\n",
        "precision = precision_score(y_test_en, y_pred, average='weighted')\n",
        "recall = recall_score(y_test_en, y_pred, average='weighted')\n",
        "\n",
        "# Displaying the results in the same format as the FastText evaluation\n",
        "result = (len(y_test), precision, recall)\n",
        "print(f\"Number of examples: {result[0]}\")\n",
        "print(f\"Precision: {result[1]}\")\n",
        "print(f\"Recall: {result[2]}\")"
      ],
      "metadata": {
        "id": "CQOZGhSgs0n6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5kMwuUr0vGNz"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "a6531978d72747188732b8816a353bd3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_0f17c90a71ee4834ac325f5b296659ce",
              "IPY_MODEL_fca089495b08484db1f0c94a6dc373b4",
              "IPY_MODEL_2f6f71b4f7904b0b907bf3a0b8097b75"
            ],
            "layout": "IPY_MODEL_b78b7372c7d246e6af214048b9d8a811"
          }
        },
        "0f17c90a71ee4834ac325f5b296659ce": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8c4ab14eb53b41a484470b71af8caf9e",
            "placeholder": "​",
            "style": "IPY_MODEL_52217e88772b4e77896bd59e60b108fc",
            "value": "Downloading readme: 100%"
          }
        },
        "fca089495b08484db1f0c94a6dc373b4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7387776b6bc445cf8d0e75a363188774",
            "max": 570,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_48efe39871354af790122c85cfbe5df8",
            "value": 570
          }
        },
        "2f6f71b4f7904b0b907bf3a0b8097b75": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_57bb26fe75e04d13b91468db5e2242eb",
            "placeholder": "​",
            "style": "IPY_MODEL_002afe5ccc78485b8db8087346c41a7f",
            "value": " 570/570 [00:00&lt;00:00, 20.3kB/s]"
          }
        },
        "b78b7372c7d246e6af214048b9d8a811": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8c4ab14eb53b41a484470b71af8caf9e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "52217e88772b4e77896bd59e60b108fc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "7387776b6bc445cf8d0e75a363188774": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "48efe39871354af790122c85cfbe5df8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "57bb26fe75e04d13b91468db5e2242eb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "002afe5ccc78485b8db8087346c41a7f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "989f827e765342a3a6ec88ac84f82b9e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_ff92a4b612e1428b84630aaa42c3874d",
              "IPY_MODEL_2538db0953ad49b9be7c8e2bcd9c5228",
              "IPY_MODEL_1eb5a269a39c4e7f9f7fb8ad91bde9a2"
            ],
            "layout": "IPY_MODEL_47571743aeda4e1e97c200164284c3c4"
          }
        },
        "ff92a4b612e1428b84630aaa42c3874d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1fdf386e6eef4356a5770d7856a8f4b8",
            "placeholder": "​",
            "style": "IPY_MODEL_6f97c822a72a44958d244bce55ea39b3",
            "value": "Downloading metadata: 100%"
          }
        },
        "2538db0953ad49b9be7c8e2bcd9c5228": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5b4895d5947241a58ab4065073511303",
            "max": 17353,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_cd044e504e0d4b299a43e7af4c5af3fc",
            "value": 17353
          }
        },
        "1eb5a269a39c4e7f9f7fb8ad91bde9a2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ceecb1fc7ea84508965ab67c5a25e5c6",
            "placeholder": "​",
            "style": "IPY_MODEL_09a939325af54932b77869bad96b11bd",
            "value": " 17.4k/17.4k [00:00&lt;00:00, 744kB/s]"
          }
        },
        "47571743aeda4e1e97c200164284c3c4": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1fdf386e6eef4356a5770d7856a8f4b8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6f97c822a72a44958d244bce55ea39b3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5b4895d5947241a58ab4065073511303": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cd044e504e0d4b299a43e7af4c5af3fc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "ceecb1fc7ea84508965ab67c5a25e5c6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "09a939325af54932b77869bad96b11bd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5a3e129b917945a9b6b29c020f6e6e54": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_98bf94896bff462591843b1e0791a99b",
              "IPY_MODEL_abfc18f3628d4454ad0b3cb024c6fb59",
              "IPY_MODEL_d56108542d9d42be86d8c0f4f518b6be"
            ],
            "layout": "IPY_MODEL_d66444be2c01427e959f3db3308f6019"
          }
        },
        "98bf94896bff462591843b1e0791a99b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_75ab5d089e3f4bf0b7c38c52d5a4d3bd",
            "placeholder": "​",
            "style": "IPY_MODEL_d3ef49d1121745f7949c58032709d9f1",
            "value": "Downloading data: 100%"
          }
        },
        "abfc18f3628d4454ad0b3cb024c6fb59": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2245f414aa0c4d03a11a71c42a88d899",
            "max": 119538351,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_6791b0cd9d5c403dacc37719437ee4a9",
            "value": 119538351
          }
        },
        "d56108542d9d42be86d8c0f4f518b6be": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_711eebfd54c14a2aa643397698b84111",
            "placeholder": "​",
            "style": "IPY_MODEL_cecb1bf2ad494fc78f20203f00c23e57",
            "value": " 120M/120M [00:00&lt;00:00, 175MB/s]"
          }
        },
        "d66444be2c01427e959f3db3308f6019": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "75ab5d089e3f4bf0b7c38c52d5a4d3bd": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d3ef49d1121745f7949c58032709d9f1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "2245f414aa0c4d03a11a71c42a88d899": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6791b0cd9d5c403dacc37719437ee4a9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "711eebfd54c14a2aa643397698b84111": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cecb1bf2ad494fc78f20203f00c23e57": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f88c1682a7cc41429c7c69504f516512": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_61e9375b1f73432b87fff3874292d085",
              "IPY_MODEL_6bf471d6c3da444198bacd35b99c0957",
              "IPY_MODEL_73e98c6eaf2a4722b8869b60b676cec8"
            ],
            "layout": "IPY_MODEL_b92cf270f1104839ab5292f33f13e133"
          }
        },
        "61e9375b1f73432b87fff3874292d085": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8f84c955c60b4a5c9fd23ec4d882b150",
            "placeholder": "​",
            "style": "IPY_MODEL_9ee3ee615cc14e65a052c622f853836e",
            "value": "Downloading data: 100%"
          }
        },
        "6bf471d6c3da444198bacd35b99c0957": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_37ffaaa7e1b143d8b227d24069bcc591",
            "max": 144443360,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_7112db89ce9b441381f9a4d3a0bb4bf7",
            "value": 144443360
          }
        },
        "73e98c6eaf2a4722b8869b60b676cec8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bda083f6c84d4f11b472578029f7280b",
            "placeholder": "​",
            "style": "IPY_MODEL_012d2342f8f841418f6a5904fa7fd5b1",
            "value": " 144M/144M [00:01&lt;00:00, 172MB/s]"
          }
        },
        "b92cf270f1104839ab5292f33f13e133": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8f84c955c60b4a5c9fd23ec4d882b150": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9ee3ee615cc14e65a052c622f853836e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "37ffaaa7e1b143d8b227d24069bcc591": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7112db89ce9b441381f9a4d3a0bb4bf7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "bda083f6c84d4f11b472578029f7280b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "012d2342f8f841418f6a5904fa7fd5b1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "72d9ca1de06c4f609f03ac68cda3dbc6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_7497ee908ca946af99e040ff375edebc",
              "IPY_MODEL_de99e62803ef4cb4a7719bcc7a57a0df",
              "IPY_MODEL_acdb663ad85d499487879813ee2d32de"
            ],
            "layout": "IPY_MODEL_9274322dda3642a9a99803efb36dd260"
          }
        },
        "7497ee908ca946af99e040ff375edebc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a3fd610b58d2469fbc5e91de9d63b6e8",
            "placeholder": "​",
            "style": "IPY_MODEL_dda09c5e95594c40b1c5dfc2decb0e94",
            "value": "Generating train split: 100%"
          }
        },
        "de99e62803ef4cb4a7719bcc7a57a0df": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d2a6e2956c8740b3805e4adb766a5ec0",
            "max": 254630,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_b78e9fe92e784bf9b1877e1c9be8a6a5",
            "value": 254630
          }
        },
        "acdb663ad85d499487879813ee2d32de": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b71b10059dba455ab2f2c4828ea5ce79",
            "placeholder": "​",
            "style": "IPY_MODEL_6c8464f187e9469a93e22d87cca5b14d",
            "value": " 254630/254630 [00:07&lt;00:00, 26030.64 examples/s]"
          }
        },
        "9274322dda3642a9a99803efb36dd260": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a3fd610b58d2469fbc5e91de9d63b6e8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "dda09c5e95594c40b1c5dfc2decb0e94": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "d2a6e2956c8740b3805e4adb766a5ec0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b78e9fe92e784bf9b1877e1c9be8a6a5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "b71b10059dba455ab2f2c4828ea5ce79": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6c8464f187e9469a93e22d87cca5b14d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}