QG_error.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "G6F9hjqZX8bR",
        "OzFRHf8jNkZG",
        "U-ycTZZ6gKqf",
        "pYMYVyvPMHrc"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Results \n",
        "\n",
        "* 66 questions in reference questions have no \"?\" marks\n",
        "\n",
        "## Banglat5\n",
        "\n",
        "```\n",
        "{'numbeams5': 12,\n",
        " 'greedy': 6,\n",
        " 'numbeams3': 184,\n",
        " 'numbeams10': 19,\n",
        " 'topk50_topp095': 52,\n",
        " 'topk90_topp095': 48,\n",
        " 'numbeams7': 18,\n",
        " 'topk10_temperature0.7': 7,\n",
        " 'topp1_temperature0.95': 173}\n",
        "```\n",
        "\n",
        " ## Mt5_small\n",
        "\n",
        " ```\n",
        " {'numbeams3': 184,\n",
        " 'topk50_topp095': 230,\n",
        " 'topk90_topp095': 201,\n",
        " 'topk10_temperature0.7': 103,\n",
        " 'numbeams5': 225,\n",
        " 'numbeams7': 250,\n",
        " 'numbeams10': 297,\n",
        " 'topp1_temperature0.95': 173,\n",
        " 'greedy: 154}\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "K6RN-vKMND4i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data viz"
      ],
      "metadata": {
        "id": "5u3700yFX6XC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "-k4oWi5eaEWO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install seaborn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBXk8FrhZZk4",
        "outputId": "f746e757-3243-4a02-824a-2f53fded4ec6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (0.11.2)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.7.3)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.3.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.21.6)\n",
            "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.7/dist-packages (from seaborn) (3.2.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (1.4.4)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn) (2022.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=2.2->seaborn) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "\n",
        "df = sns.load_dataset(\"titanic\")\n",
        "\n",
        "g = sns.catplot(\n",
        "    data=df, x=\"who\", y=\"survived\", col=\"class\",\n",
        "    kind=\"bar\", height=4, aspect=.6,\n",
        ")\n",
        "g.set_axis_labels(\"\", \"Survival Rate\")\n",
        "g.set_xticklabels([\"Men\", \"Women\", \"Children\"])\n",
        "g.set_titles(\"{col_name} {col_var}\")\n",
        "g.set(ylim=(0, 1))\n",
        "g.despine(left=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "aeahDnF4ZUVf",
        "outputId": "7fd0e23f-581f-4298-aaef-62b4bfed75a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f37199475d0>"
            ]
          },
          "metadata": {},
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 518.4x288 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pIHG-axNaMiS",
        "outputId": "ef2ff52c-ad8c-47f2-b995-27dd209cd238"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
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          "metadata": {},
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    {
      "cell_type": "code",
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        "settings = ['greedy','beam_search(3)','beam_search(5)','beam_search(7)','beam_search(10)','greedy','beam_search(3)','beam_search(5)','beam_search(7)','beam_search(10)']\n",
        "model = ['banglat5','banglat5','banglat5','banglat5','banglat5','mt5_small','mt5_small','mt5_small','mt5_small','mt5_small']\n",
        "values = [6,]"
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      "metadata": {
        "id": "5PAk8YomX-xY"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bt5 = {'numbeams5': 12,\n",
        " 'greedy': 6,\n",
        " 'numbeams3': 184,\n",
        " 'numbeams10': 19,\n",
        " 'topk50_topp095': 52,\n",
        " 'topk90_topp095': 48,\n",
        " 'numbeams7': 18,\n",
        " 'topk10_temperature0.7': 7,\n",
        " 'topp1_temperature0.95': 173}"
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      "metadata": {
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      "execution_count": null,
      "outputs": []
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        "id": "uXKm6BfnbO0H"
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      "execution_count": null,
      "outputs": []
    },
    {
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        "no_question_numbers = list(bt5.values())"
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      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
    {
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        "result_df = pd.DataFrame.from_dict({'settings':settings,'no_question_numbers':no_question_numbers})"
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      "execution_count": null,
      "outputs": []
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      "metadata": {
        "id": "EFfLGsQvboHg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "mt5 = {'numbeams3': 184,\n",
        " 'topk50_topp095': 230,\n",
        " 'topk90_topp095': 201,\n",
        " 'topk10_temperature0.7': 103,\n",
        " 'numbeams5': 225,\n",
        " 'numbeams7': 250,\n",
        " 'numbeams10': 297,\n",
        " 'topp1_temperature0.95': 173,\n",
        " 'greedy': 154}"
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      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
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        "settings = list(mt5.keys())"
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      "metadata": {
        "id": "o55cuxfnbuu6"
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      "execution_count": null,
      "outputs": []
    },
    {
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        "no_question_numbers = list(mt5.values())"
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      "outputs": []
    },
    {
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
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      "metadata": {
        "id": "XKxVQV_icG8k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
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      ],
      "metadata": {
        "id": "vyPkYw8TcKc6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "result"
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        "id": "NXrz1FiScOyh",
        "outputId": "9be468c5-3b8d-4a0c-f719-6263b43043d4"
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      "execution_count": null,
      "outputs": [
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              "                settings  no_question_numbers      model\n",
              "0              numbeams5                   12   banglat5\n",
              "1                 greedy                    6   banglat5\n",
              "2              numbeams3                  184   banglat5\n",
              "3             numbeams10                   19   banglat5\n",
              "4         topk50_topp095                   52   banglat5\n",
              "5         topk90_topp095                   48   banglat5\n",
              "6              numbeams7                   18   banglat5\n",
              "7  topk10_temperature0.7                    7   banglat5\n",
              "8  topp1_temperature0.95                  173   banglat5\n",
              "0              numbeams3                  184  mt5_small\n",
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              "6             numbeams10                  297  mt5_small\n",
              "7  topp1_temperature0.95                  173  mt5_small\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-17f8437a-3f7c-4cb3-80a1-4afd8efcf44a 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-17f8437a-3f7c-4cb3-80a1-4afd8efcf44a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result.to_csv(\"/content/gdrive/MyDrive/Related work QG/Results/errors/no_question_results.csv\",index=False)"
      ],
      "metadata": {
        "id": "pq4k4HCgcZIE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "\n",
        "g = sns.catplot(\n",
        "    data=result, y=\"settings\", x=\"no_question_numbers\", col=\"model\",\n",
        "    kind=\"bar\"\n",
        ")\n",
        "#g.set_axis_labels(\"\", \"Survival Rate\")\n",
        "#g.set_xticklabels([\"Men\", \"Women\", \"Children\"])\n",
        "#g.set_titles(\"Banglat5\",\"MT5\")\n",
        "#g.set(ylim=(0, 1))\n",
        "#g.despine(left=True)\n",
        "#g.savefig(\"/content/gdrive/MyDrive/Related work QG/Results/errors/no_question_results.png\")\n",
        "g.savefig(\"no_question_generation.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "dWLC4hhmcSuX",
        "outputId": "570ca614-86f1-4aca-894b-eed7e83bf544"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Results Getting"
      ],
      "metadata": {
        "id": "G6F9hjqZX8bR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KumCzdPYInCv",
        "outputId": "77e13bf0-b086-4717-b8ba-9dc89b23c7b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive/', force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "eIJV1dFvI3ct"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel(\"/content/gdrive/MyDrive/Related work QG/Results/banglat5-v1/greedy_banglat5(3).xlsx\")"
      ],
      "metadata": {
        "id": "YDFkeCTbJaI4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
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              "                                  Reference_question  \\\n",
              "0  কখন আইইসি রেড বুক সিডি-ডিএকে একটি আন্তর্জাতিক ...   \n",
              "1  ১৯৭৯ সালের সনি এবং ফিলিপস ডিজিটাল অডিও ডিস্ক ট...   \n",
              "2       কারা প্রথম সিডিতে দশ লক্ষ কপি বিক্রি করেছিল?   \n",
              "3  কোন শিল্পী তাদের সম্পূর্ণ ক্যাটালগকে সিডিতে রূ...   \n",
              "4  আর্মস-এ ডায়ার স্ট্রেইটস অ্যালবাম ব্রাদার্স কো...   \n",
              "\n",
              "                                  Predicted_question  \\\n",
              "0  কোন বছর রেড বুক সিডি-ডিএ স্ট্যান্ডার্ড আনুষ্ঠা...   \n",
              "1                      কে এই গবেষণা পরিচালনা করেছিল?   \n",
              "2  ১৯৮৫ সালে প্রথম কোন শিল্পী এক মিলিয়ন কপি সিডি...   \n",
              "3  কে প্রথম প্রধান শিল্পী হিসেবে তার সম্পূর্ণ ক্য...   \n",
              "4  কোন বছর ডাইর স্ট্রেইটসের অ্যালবাম ব্রাদার্স ইন...   \n",
              "\n",
              "                                Answer  \\\n",
              "0                                 ১৯৮৭   \n",
              "1  কিস শুহামার ইমমিনক এবং তোশিতাদা দোই   \n",
              "2                       ডাইর স্ট্রেইটস   \n",
              "3                          ডেভিড বোয়ি   \n",
              "4                                 ১৯৮৫   \n",
              "\n",
              "                                             Context  \n",
              "0  ফলস্বরূপ, ১৯৭৯ সালে, সনি এবং ফিলিপস একটি নতুন ...  \n",
              "1  ফলস্বরূপ, ১৯৭৯ সালে, সনি এবং ফিলিপস একটি নতুন ...  \n",
              "2  ১৯৮৩ সালের মার্চ মাসে ইউরোপ ও উত্তর আমেরিকায় ...  \n",
              "3  ১৯৮৩ সালের মার্চ মাসে ইউরোপ ও উত্তর আমেরিকায় ...  \n",
              "4  ১৯৮৩ সালের মার্চ মাসে ইউরোপ ও উত্তর আমেরিকায় ...  "
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              "      <th></th>\n",
              "      <th>Reference_question</th>\n",
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              "      <th>0</th>\n",
              "      <td>কখন আইইসি রেড বুক সিডি-ডিএকে একটি আন্তর্জাতিক ...</td>\n",
              "      <td>কোন বছর রেড বুক সিডি-ডিএ স্ট্যান্ডার্ড আনুষ্ঠা...</td>\n",
              "      <td>১৯৮৭</td>\n",
              "      <td>ফলস্বরূপ, ১৯৭৯ সালে, সনি এবং ফিলিপস একটি নতুন ...</td>\n",
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              "      <th>1</th>\n",
              "      <td>১৯৭৯ সালের সনি এবং ফিলিপস ডিজিটাল অডিও ডিস্ক ট...</td>\n",
              "      <td>কে এই গবেষণা পরিচালনা করেছিল?</td>\n",
              "      <td>কিস শুহামার ইমমিনক এবং তোশিতাদা দোই</td>\n",
              "      <td>ফলস্বরূপ, ১৯৭৯ সালে, সনি এবং ফিলিপস একটি নতুন ...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>কারা প্রথম সিডিতে দশ লক্ষ কপি বিক্রি করেছিল?</td>\n",
              "      <td>১৯৮৫ সালে প্রথম কোন শিল্পী এক মিলিয়ন কপি সিডি...</td>\n",
              "      <td>ডাইর স্ট্রেইটস</td>\n",
              "      <td>১৯৮৩ সালের মার্চ মাসে ইউরোপ ও উত্তর আমেরিকায় ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>কোন শিল্পী তাদের সম্পূর্ণ ক্যাটালগকে সিডিতে রূ...</td>\n",
              "      <td>কে প্রথম প্রধান শিল্পী হিসেবে তার সম্পূর্ণ ক্য...</td>\n",
              "      <td>ডেভিড বোয়ি</td>\n",
              "      <td>১৯৮৩ সালের মার্চ মাসে ইউরোপ ও উত্তর আমেরিকায় ...</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>আর্মস-এ ডায়ার স্ট্রেইটস অ্যালবাম ব্রাদার্স কো...</td>\n",
              "      <td>কোন বছর ডাইর স্ট্রেইটসের অ্যালবাম ব্রাদার্স ইন...</td>\n",
              "      <td>১৯৮৫</td>\n",
              "      <td>১৯৮৩ সালের মার্চ মাসে ইউরোপ ও উত্তর আমেরিকায় ...</td>\n",
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              "          const element = document.querySelector('#df-25800172-2a72-45fc-81b6-bb708f796c29');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_question_reference = df['Reference_question'].str.contains('?',regex = False)"
      ],
      "metadata": {
        "id": "lQwStxylMd9V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Reference_question'].str.contains('?',regex = False).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQqwSconL7kT",
        "outputId": "a011c454-38ca-447c-ef0c-b3d8315d193b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     13472\n",
              "False       66\n",
              "Name: Reference_question, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[no_question_reference == False]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "sD9ETMkcMa1x",
        "outputId": "80068b48-923f-45d7-9a4b-9d560f035851"
      },
      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      Reference_question  \\\n",
              "102                                ফিটসের দলগুলোর নাম কি   \n",
              "162    আমেরিকায় প্রতিষ্ঠিত আদিবাসী আমেরিকানদের একটি ...   \n",
              "165                     চীনে নগরায়নের অন্যতম একটি কারণ।   \n",
              "226    এমন এক ঘটনার নাম বলুন, যা বিপ্লবে অবদান রাখতে ...   \n",
              "266    নিউটনের দর্শন যখন জীবনে প্রয়োগ করা হয় তখন কি...   \n",
              "...                                                  ...   \n",
              "10420         মন্টানায় রেলপথ কতদিন ধরে গুরুত্বপূর্ণ ছিল   \n",
              "10459  কি বছরের মধ্যে আদিবাসী জনসংখ্যা ২৭.৯% বৃদ্ধি প...   \n",
              "10498  যিনি ১৯০০-এর দশকের প্রথম দিকে মন্টানায় বসতি স...   \n",
              "11179  বিপন্ন প্রজাতির কত শতাংশ হাঁস-স্ট্যাম্প ফান্ড ...   \n",
              "11195  যে কারণে আরবের অরিক্স ধ্বংস হয়ে গেছে, গতির এক...   \n",
              "\n",
              "                                      Predicted_question  \\\n",
              "102            আইজিএফইটি কোন দুটি পরিবারের মধ্যে বিভক্ত?   \n",
              "162    আমেরিকার নেটিভ আমেরিকানরা কোন সাম্রাজ্য তৈরি ক...   \n",
              "165      চীনের নগরায়ণের সঙ্গে সঙ্গে কী বৃদ্ধি পেয়েছিল?   \n",
              "226                বিপ্লবের পর কোন যুদ্ধ সংঘটিত হয়েছিল?   \n",
              "266         প্রাকৃতিক দর্শন ব্যবহার করার ফলে কী হয়েছিল?   \n",
              "...                                                  ...   \n",
              "10420  কোন্ দশকে রেলপথ মন্টানায় পরিবহনকে এক গুরুত্বপ...   \n",
              "10459  কোন বছরগুলোতে মন্টানার আদিবাসী জনসংখ্যা ২৭.৯ শ...   \n",
              "10498      মন্টানা প্রাইরিতে কে বসতি স্থাপন শুরু করেছিল?   \n",
              "11179  হাঁসের স্ট্যাম্প তহবিল ব্যবহার করে বিপন্ন প্রজ...   \n",
              "11195  কোন দুটো বিষয় আরব ওরিক্সকে বিলুপ্ত করে দিয়েছিল?   \n",
              "\n",
              "                                                  Answer  \\\n",
              "102             জংশন ফিট (জেএফইটি) এবং ইনসুলেটেড গেট ফিট   \n",
              "162                                   অ্যাজটেক সাম্রাজ্য   \n",
              "165                                      জনসংখ্যা বৃদ্ধি   \n",
              "226                                    নেপোলিয়নের যুদ্ধ   \n",
              "266    সামাজিক নীতি পরিবর্তনের জন্য বেশ কয়েকটি সফল প...   \n",
              "...                                                  ...   \n",
              "10420                                        ১৮৮০-এর দশক   \n",
              "10459                                     ১৯৮০ থেকে ১৯৯০   \n",
              "10498                                       জেমস জে. হিল   \n",
              "11179                                       এক তৃতীয়াংশ   \n",
              "11195          অটোমোবাইল এবং উচ্চ-চালিত রাইফেলের ব্যবহার   \n",
              "\n",
              "                                                 Context  \n",
              "102    ফিট দুটি পরিবারে বিভক্ত: জংশন ফিট (জেএফইটি) এব...  \n",
              "162    এশিয়াতে বিভিন্ন চীনা রাজবংশ ও জাপানি শোগুনেট ...  \n",
              "165    চীনে জনসংখ্যা বৃদ্ধির সাথে সাথে নগরায়ণ বৃদ্ধি...  \n",
              "226    এই পরিবর্তনগুলোর সঙ্গে ছিল ভয়াবহ বিশৃঙ্খলা, য...  \n",
              "266    আলোকায়নের ধারণা অনুসরণ করে, সংস্কারকরা শিল্প ...  \n",
              "...                                                  ...  \n",
              "10420  ১৮৮০-এর দশক থেকে রেলপথ মন্টানায় পরিবহনের একটি...  \n",
              "10459  মার্কিন যুক্তরাষ্ট্রের অধিকাংশ রাজ্যের তুলনায়...  \n",
              "10498  ১৯০০ সালের প্রথম দিকে, গ্রেট নর্দানের জেমস জে....  \n",
              "11179  ১৯৩৪ সালের ১৬ মার্চ প্রেসিডেন্ট ফ্রাঙ্কলিন ডি....  \n",
              "11195  আরব ওরিক্স, বৃহৎ হরিণের একটি প্রজাতি, এক সময় ...  \n",
              "\n",
              "[66 rows x 4 columns]"
            ],
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              "\n",
              "  <div id=\"df-d6477032-0b36-4aa2-b26f-815588d699d1\">\n",
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              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Reference_question</th>\n",
              "      <th>Predicted_question</th>\n",
              "      <th>Answer</th>\n",
              "      <th>Context</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>ফিটসের দলগুলোর নাম কি</td>\n",
              "      <td>আইজিএফইটি কোন দুটি পরিবারের মধ্যে বিভক্ত?</td>\n",
              "      <td>জংশন ফিট (জেএফইটি) এবং ইনসুলেটেড গেট ফিট</td>\n",
              "      <td>ফিট দুটি পরিবারে বিভক্ত: জংশন ফিট (জেএফইটি) এব...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>আমেরিকায় প্রতিষ্ঠিত আদিবাসী আমেরিকানদের একটি ...</td>\n",
              "      <td>আমেরিকার নেটিভ আমেরিকানরা কোন সাম্রাজ্য তৈরি ক...</td>\n",
              "      <td>অ্যাজটেক সাম্রাজ্য</td>\n",
              "      <td>এশিয়াতে বিভিন্ন চীনা রাজবংশ ও জাপানি শোগুনেট ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>চীনে নগরায়নের অন্যতম একটি কারণ।</td>\n",
              "      <td>চীনের নগরায়ণের সঙ্গে সঙ্গে কী বৃদ্ধি পেয়েছিল?</td>\n",
              "      <td>জনসংখ্যা বৃদ্ধি</td>\n",
              "      <td>চীনে জনসংখ্যা বৃদ্ধির সাথে সাথে নগরায়ণ বৃদ্ধি...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226</th>\n",
              "      <td>এমন এক ঘটনার নাম বলুন, যা বিপ্লবে অবদান রাখতে ...</td>\n",
              "      <td>বিপ্লবের পর কোন যুদ্ধ সংঘটিত হয়েছিল?</td>\n",
              "      <td>নেপোলিয়নের যুদ্ধ</td>\n",
              "      <td>এই পরিবর্তনগুলোর সঙ্গে ছিল ভয়াবহ বিশৃঙ্খলা, য...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>নিউটনের দর্শন যখন জীবনে প্রয়োগ করা হয় তখন কি...</td>\n",
              "      <td>প্রাকৃতিক দর্শন ব্যবহার করার ফলে কী হয়েছিল?</td>\n",
              "      <td>সামাজিক নীতি পরিবর্তনের জন্য বেশ কয়েকটি সফল প...</td>\n",
              "      <td>আলোকায়নের ধারণা অনুসরণ করে, সংস্কারকরা শিল্প ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10420</th>\n",
              "      <td>মন্টানায় রেলপথ কতদিন ধরে গুরুত্বপূর্ণ ছিল</td>\n",
              "      <td>কোন্ দশকে রেলপথ মন্টানায় পরিবহনকে এক গুরুত্বপ...</td>\n",
              "      <td>১৮৮০-এর দশক</td>\n",
              "      <td>১৮৮০-এর দশক থেকে রেলপথ মন্টানায় পরিবহনের একটি...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10459</th>\n",
              "      <td>কি বছরের মধ্যে আদিবাসী জনসংখ্যা ২৭.৯% বৃদ্ধি প...</td>\n",
              "      <td>কোন বছরগুলোতে মন্টানার আদিবাসী জনসংখ্যা ২৭.৯ শ...</td>\n",
              "      <td>১৯৮০ থেকে ১৯৯০</td>\n",
              "      <td>মার্কিন যুক্তরাষ্ট্রের অধিকাংশ রাজ্যের তুলনায়...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10498</th>\n",
              "      <td>যিনি ১৯০০-এর দশকের প্রথম দিকে মন্টানায় বসতি স...</td>\n",
              "      <td>মন্টানা প্রাইরিতে কে বসতি স্থাপন শুরু করেছিল?</td>\n",
              "      <td>জেমস জে. হিল</td>\n",
              "      <td>১৯০০ সালের প্রথম দিকে, গ্রেট নর্দানের জেমস জে....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11179</th>\n",
              "      <td>বিপন্ন প্রজাতির কত শতাংশ হাঁস-স্ট্যাম্প ফান্ড ...</td>\n",
              "      <td>হাঁসের স্ট্যাম্প তহবিল ব্যবহার করে বিপন্ন প্রজ...</td>\n",
              "      <td>এক তৃতীয়াংশ</td>\n",
              "      <td>১৯৩৪ সালের ১৬ মার্চ প্রেসিডেন্ট ফ্রাঙ্কলিন ডি....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11195</th>\n",
              "      <td>যে কারণে আরবের অরিক্স ধ্বংস হয়ে গেছে, গতির এক...</td>\n",
              "      <td>কোন দুটো বিষয় আরব ওরিক্সকে বিলুপ্ত করে দিয়েছিল?</td>\n",
              "      <td>অটোমোবাইল এবং উচ্চ-চালিত রাইফেলের ব্যবহার</td>\n",
              "      <td>আরব ওরিক্স, বৃহৎ হরিণের একটি প্রজাতি, এক সময় ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>66 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6477032-0b36-4aa2-b26f-815588d699d1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d6477032-0b36-4aa2-b26f-815588d699d1 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-d6477032-0b36-4aa2-b26f-815588d699d1');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# predicted"
      ],
      "metadata": {
        "id": "OzFRHf8jNkZG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Banglat5"
      ],
      "metadata": {
        "id": "f5StZ_0fOC7_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "S0er6F1rOCeh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dir_path = \"/content/gdrive/MyDrive/Related work QG/Results/banglat5-v1\""
      ],
      "metadata": {
        "id": "61Q0ilrmOE0l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_paths = os.listdir(dir_path)"
      ],
      "metadata": {
        "id": "LCD7IpWVULyF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_paths"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XOgp3mfNWzuy",
        "outputId": "cc372ab2-f45d-45d1-e148-919c41e84eb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['numbeams3_mT5small.xlsx',\n",
              " 'topk50_topp095_seq2_mt5_small.xlsx',\n",
              " 'topk90_topp095_mt5_small.xlsx',\n",
              " 'topk10_temp07_mt5_small.xlsx',\n",
              " 'numbeams5_mT5small.xlsx',\n",
              " 'numbeams7_mT5small.xlsx',\n",
              " 'numbeams10_mT5small.xlsx',\n",
              " 'topp1_temperature095_mt5_small (1).xlsx',\n",
              " 'greedy_mt5small.xlsx']"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfs = []\n",
        "for path in df_paths:\n",
        "  dfs.append(pd.read_excel(os.path.join(dir_path,path)))"
      ],
      "metadata": {
        "id": "JxUKJXHpUQZX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "error_dfs = []\n",
        "no_question_stats = {}\n",
        "for i in range(len(df_paths)):\n",
        "  no_question_predicted = dfs[i]['Predicted_question'].str.contains('?',regex = False)\n",
        "  false_counts = no_question_predicted.value_counts()[0]\n",
        "  no_question_stats[df_paths[i]] = false_counts\n",
        "  error_dfs.append(dfs[i][no_question_predicted==False])  "
      ],
      "metadata": {
        "id": "AEqkEAtvUjrc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_question_stats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yyEnhTKVHdE",
        "outputId": "3276c81a-b92d-40fe-9166-51985787474f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'numbeams3_mT5small.xlsx': 184,\n",
              " 'topk50_topp095_seq2_mt5_small.xlsx': 230,\n",
              " 'topk90_topp095_mt5_small.xlsx': 201,\n",
              " 'topk10_temp07_mt5_small.xlsx': 103,\n",
              " 'numbeams5_mT5small.xlsx': 225,\n",
              " 'numbeams7_mT5small.xlsx': 250,\n",
              " 'numbeams10_mT5small.xlsx': 297,\n",
              " 'topp1_temperature095_mt5_small (1).xlsx': 173,\n",
              " 'greedy_mt5small.xlsx': 154}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bangla_t5_errors = pd.concat(error_dfs,axis=0)"
      ],
      "metadata": {
        "id": "qofIH8m6VzaN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bangla_t5_errors.to_csv(\"/content/gdrive/MyDrive/Related work QG/Results/mt5t5errors.csv\",index=False)"
      ],
      "metadata": {
        "id": "cYFNDyqAWVpD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dir_path = \"/content/gdrive/MyDrive/Related work QG/Results/mt5-small-v1\""
      ],
      "metadata": {
        "id": "gTIJHyn-NzUH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tJCdET_TWvZs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Interrogative distribution"
      ],
      "metadata": {
        "id": "U-ycTZZ6gKqf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "interrogatives = ['কি','কেন','কোথায়','কিভাবে','কখন','কে','কার']"
      ],
      "metadata": {
        "id": "TyOwx4fKgMNJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bt5_greedy = pd.read_excel(\"/content/gdrive/MyDrive/Related work QG/Results/banglat5-v1/greedy_banglat5(3).xlsx\")"
      ],
      "metadata": {
        "id": "_RRIRCckgcJI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mt5_greedy = pd.read_excel(\"/content/gdrive/MyDrive/Related work QG/Results/mt5-small-v1/greedy_mt5small.xlsx\")"
      ],
      "metadata": {
        "id": "-q-mIWjsmKew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bt5_results = {'কি':0,'কেন':0,'কোথায়':0,'কিভাবে':0,'কখন':0,'কে':0,'কার':0}\n",
        "for question in bt5_greedy['Predicted_question']:\n",
        "  for interrogative in interrogatives:\n",
        "    if interrogative in question:\n",
        "      bt5_results[interrogative]+=1\n",
        "\n"
      ],
      "metadata": {
        "id": "3mKjaQBgmUON"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bt5_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5h3hsRPjnh6E",
        "outputId": "17ff4dd6-f53c-47fd-f88e-42e58ff00aab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'কি': 1224,\n",
              " 'কেন': 169,\n",
              " 'কোথায়': 0,\n",
              " 'কিভাবে': 4,\n",
              " 'কখন': 850,\n",
              " 'কে': 4210,\n",
              " 'কার': 1312}"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mt5_results = {'কি':0,'কেন':0,'কোথায়':0,'কিভাবে':0,'কখন':0,'কে':0,'কার':0}\n",
        "for question in mt5_greedy['Predicted_question']:\n",
        "  for interrogative in interrogatives:\n",
        "    if interrogative in question:\n",
        "      mt5_results[interrogative]+=1\n",
        "\n"
      ],
      "metadata": {
        "id": "avoo2xJsnjKI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mt5_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPGZgHovn2uL",
        "outputId": "614a62ee-ac63-42aa-f864-a8864e194ab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'কি': 782,\n",
              " 'কেন': 164,\n",
              " 'কোথায়': 0,\n",
              " 'কিভাবে': 0,\n",
              " 'কখন': 582,\n",
              " 'কে': 3298,\n",
              " 'কার': 1253}"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_interrogative = pd.DataFrame.from_dict({\"Interrogatives\":interrogatives,\"Number of Questions\":list(bt5_results.values())})"
      ],
      "metadata": {
        "id": "vQ6QHu-9n4EN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_interrogative['model'] = 'banglat5'"
      ],
      "metadata": {
        "id": "nqQWBzDeoRtb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_interrogative2 = pd.DataFrame.from_dict({\"Interrogatives\":interrogatives,\"Number of Questions\":list(mt5_results.values())})"
      ],
      "metadata": {
        "id": "KvYPAaXDoTzq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_interrogative2['model'] = 'mt5_small'"
      ],
      "metadata": {
        "id": "uJt-7bKYqYHW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_interrogative = pd.concat([df_interrogative,df_interrogative2],axis=0)"
      ],
      "metadata": {
        "id": "ef463-DCoYAO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_interrogative"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "rI_McvJOokcX",
        "outputId": "5ca84131-39c1-4712-fc61-16ae758ae39b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Interrogatives  Number of Questions      model\n",
              "0             কি                 1224   banglat5\n",
              "1            কেন                  169   banglat5\n",
              "2          কোথায়                    0   banglat5\n",
              "3         কিভাবে                    4   banglat5\n",
              "4            কখন                  850   banglat5\n",
              "5             কে                 4210   banglat5\n",
              "6            কার                 1312   banglat5\n",
              "0             কি                  782  mt5_small\n",
              "1            কেন                  164  mt5_small\n",
              "2          কোথায়                    0  mt5_small\n",
              "3         কিভাবে                    0  mt5_small\n",
              "4            কখন                  582  mt5_small\n",
              "5             কে                 3298  mt5_small\n",
              "6            কার                 1253  mt5_small"
            ],
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          },
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(bt5_greedy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b58iv4virL_O",
        "outputId": "39f7d0ad-d1a0-411c-d5b5-ba7f3e10d809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13538"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "rmRRtg_srlV4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_interrogative['Percentage of Questions'] = np.round(result_interrogative['Number of Questions']/len(bt5_greedy)*100,2)"
      ],
      "metadata": {
        "id": "zNURmd6jrUS6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_interrogative.to_excel(\"results.xlsx\")"
      ],
      "metadata": {
        "id": "xPy8GEV8reFh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vQCuLoJey5nM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.font_manager as fm\n"
      ],
      "metadata": {
        "id": "j2pE_zf0sf2L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prop = fm.FontProperties(fname=\"Siyamrupali.ttf\")"
      ],
      "metadata": {
        "id": "AQx4cNWSsqjr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " # set width of bars\n",
        "barWidth = 0.25\n",
        " \n",
        "# set heights of bars\n",
        "bars1 = list(bt5_results.values())\n",
        "bars2 = list(mt5_results.values())\n",
        " \n",
        "# Set position of bar on X axis\n",
        "r1 = np.arange(len(bars1))\n",
        "r2 = [x + barWidth for x in r1]\n",
        " \n",
        "# Make the plot\n",
        "plt.bar(r1, bars1, color='#7f6d5f', width=barWidth, edgecolor='white', label='banglat5')\n",
        "plt.bar(r2, bars2, color='#557f2d', width=barWidth, edgecolor='white', label='mt5_small')\n",
        " \n",
        "# Add xticks on the middle of the group bars\n",
        "plt.xlabel('group', fontweight='bold')\n",
        "plt.xticks([r + barWidth for r in range(len(bars1))], interrogatives,fontproperties=prop)\n",
        " \n",
        "# Create legend & Show graphic\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "GZL5XNcKxZre",
        "outputId": "bed67672-2550-4e34-81c1-f68eb95f2c15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f37081fe690>"
            ]
          },
          "metadata": {},
          "execution_count": 177
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qtCva2QNqx-D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Context Copying"
      ],
      "metadata": {
        "id": "pYMYVyvPMHrc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pylcs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wie06ZBXMIZ-",
        "outputId": "45dc0fe9-24d0-4223-93fc-c44069243ccd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pylcs\n",
            "  Downloading pylcs-0.0.7.tar.gz (5.1 kB)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pybind11>=2.2\n",
            "  Using cached pybind11-2.10.0-py3-none-any.whl (213 kB)\n",
            "Building wheels for collected packages: pylcs\n",
            "  Building wheel for pylcs (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pylcs: filename=pylcs-0.0.7-cp37-cp37m-linux_x86_64.whl size=784314 sha256=1bff823a81d0751ec9d929026258f6f1c411bcef520326948ef51c2916a79c39\n",
            "  Stored in directory: /root/.cache/pip/wheels/9e/13/8c/32d54c6d0a00ae8af6ee4506d4f46e85e8056590bdf02f0a14\n",
            "Successfully built pylcs\n",
            "Installing collected packages: pybind11, pylcs\n",
            "Successfully installed pybind11-2.10.0 pylcs-0.0.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pylcs"
      ],
      "metadata": {
        "id": "2F0_DZrCMsur"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "A = 'We are shannonai'\n",
        "B = 'We like shannonai'\n",
        "pylcs.lcs_sequence_length(A, B)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9lV-PmvMMBV",
        "outputId": "7dbd25e6-54a5-4ebe-fc77-8e6f300ed4a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bt5_greedy.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92rnAymJNrT6",
        "outputId": "5c5b3c3f-1d76-4d27-cd81-1e4a7522c9d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Reference_question', 'Predicted_question', 'Answer', 'Context'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bt5_lcs = []\n",
        "for i, row in bt5_greedy.iterrows():\n",
        "  bt5_lcs.append(pylcs.lcs_string_length(row['Context'],row['Predicted_question']))"
      ],
      "metadata": {
        "id": "mguvv_RnM8LO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bt5_greedy['LCS(string)'] = bt5_lcs"
      ],
      "metadata": {
        "id": "3BZxD1amMrpZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bt5_greedy['LCS(string)'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YtMFyKRVO0BT",
        "outputId": "11eb215d-fc2f-460f-f362-0d90e6f23ba6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22.834392081548234"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bt5_greedy.sort_values(by='LCS(string)',ascending=False).to_csv(\"/content/gdrive/MyDrive/Related work QG/Results/errors/lcs_bt5.csv\", index=False)"
      ],
      "metadata": {
        "id": "YtqQpd9IO_Sz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mt5_lcs = []\n",
        "for i, row in mt5_greedy.iterrows():\n",
        "  mt5_lcs.append(pylcs.lcs_sequence_length(row['Context'],row['Predicted_question']))"
      ],
      "metadata": {
        "id": "BckgGM1HQQk5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mt5_greedy['LCS(subsequence)'] = mt5_lcs"
      ],
      "metadata": {
        "id": "PExhD8D-PWJW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mt5_greedy.sort_values(by='LCS(subsequence)',ascending=False).to_csv(\"/content/gdrive/MyDrive/Related work QG/Results/errors/lcs_mt5.csv\", index=False)"
      ],
      "metadata": {
        "id": "pEUfASeoQgGt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bt5_greedy.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "GnrSVREQQrCr",
        "outputId": "761b4ae4-9ac5-4728-f164-c3834382d344"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       LCS(sequence)   LCS(string)\n",
              "count   13538.000000  13538.000000\n",
              "mean       47.977545     22.834392\n",
              "std        18.342991     11.934620\n",
              "min         6.000000      2.000000\n",
              "25%        35.000000     15.000000\n",
              "50%        45.000000     20.000000\n",
              "75%        58.000000     28.000000\n",
              "max       211.000000    106.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3824f386-8a6d-48fa-a478-eab0782d430b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "    }\n",
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              "\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>LCS(sequence)</th>\n",
              "      <th>LCS(string)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13538.000000</td>\n",
              "      <td>13538.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>47.977545</td>\n",
              "      <td>22.834392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>18.342991</td>\n",
              "      <td>11.934620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>211.000000</td>\n",
              "      <td>106.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3824f386-8a6d-48fa-a478-eab0782d430b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-3824f386-8a6d-48fa-a478-eab0782d430b 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-3824f386-8a6d-48fa-a478-eab0782d430b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mt5_greedy.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Po0ZAVaHRF5U",
        "outputId": "bd32af7e-54f8-4c80-cd5b-f449edd2a6c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        LCS(string)  LCS(sequence)  LCS(subsequence)\n",
              "count  13538.000000   13538.000000      13538.000000\n",
              "mean      29.566406      50.002807         50.002807\n",
              "std       15.369077      19.661312         19.661312\n",
              "min        3.000000       7.000000          7.000000\n",
              "25%       19.000000      36.000000         36.000000\n",
              "50%       26.000000      47.000000         47.000000\n",
              "75%       37.000000      61.000000         61.000000\n",
              "max      129.000000     142.000000        142.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-771b7728-e677-4b8c-a952-9959be43796d\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>LCS(string)</th>\n",
              "      <th>LCS(sequence)</th>\n",
              "      <th>LCS(subsequence)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13538.000000</td>\n",
              "      <td>13538.000000</td>\n",
              "      <td>13538.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>29.566406</td>\n",
              "      <td>50.002807</td>\n",
              "      <td>50.002807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>15.369077</td>\n",
              "      <td>19.661312</td>\n",
              "      <td>19.661312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>47.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>129.000000</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>142.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-771b7728-e677-4b8c-a952-9959be43796d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-771b7728-e677-4b8c-a952-9959be43796d 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-771b7728-e677-4b8c-a952-9959be43796d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DJC-BqEuRJGk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Human Evaluation"
      ],
      "metadata": {
        "id": "DGHjRpVXqkAB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "WQIND6_oqmgM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel(\"/content/Human_Evaluation.xlsx\")"
      ],
      "metadata": {
        "id": "26wWK_zzJybP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df = pd.read_excel(\"/content/Human Evaluation BanglaT5 Results.xlsx\")"
      ],
      "metadata": {
        "id": "rymhUMM2rIaV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "MBZahR1SrMOt",
        "outputId": "1827165f-f6d7-47ec-cf90-2997241b96ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Setting      Model  \\\n",
              "0  Topk-10,temp-0.7.  mt5_small   \n",
              "1  Topk-10,temp-0.7.  mt5_small   \n",
              "2  Topk-10,temp-0.7.  mt5_small   \n",
              "3  Topk-10,temp-0.7.  mt5_small   \n",
              "4  Topk-10,temp-0.7.  mt5_small   \n",
              "\n",
              "                                  Reference_question  \\\n",
              "0              হিব্রু বাইবেলের মোট কতগুলি খণ্ড আছে ?   \n",
              "1                               ঢাকা শহরের আয়তন কত ?   \n",
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              "3  পাকিস্তানি মুসলিম জাতীয়তাবাদী চৌধুরী রহমত আলি...   \n",
              "4                          পাল বংশের প্রথম রাজা কে ?   \n",
              "\n",
              "                                  Predicted_question           Answer  \\\n",
              "0           কী কাদের মধ্যে তানাখ শব্দটি ব্যবহার করে?            তিনটি   \n",
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              "3   কোন বছর চৌধুরী রহমত আলি কখন মুসলিম জাতীয়তাবাদী?  ১৬ নভেম্বর ১৮৯৫   \n",
              "4                           কোন যোদ্ধার পুত্র ছিলেন?            গোপাল   \n",
              "\n",
              "                                             Context  Answerability  \\\n",
              "0  হিব্রু বাইবেল (Hebrew Bible) বলতে ইহুদি ও খ্রি...            1.0   \n",
              "1  ঢাকা দক্ষিণ এশিয়ার রাষ্ট্র বাংলাদেশের রাজধানী...            1.0   \n",
              "2  দ্বিতীয় বিশ্বযুদ্ধ (English: World WarII, Sec...            1.0   \n",
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              "4  খালিমপুর তাম্রলিপি অনুসারে, প্রথম পাল রাজা গোপ...            1.0   \n",
              "\n",
              "   Correctness  Relevance _to_context  Relevance_to_answer  \n",
              "0          1.0                    1.0                  0.0  \n",
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              "2          1.0                    1.0                  0.0  \n",
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              "       Answerability  Correctness  Relevance _to_context  Relevance_to_answer\n",
              "count     900.000000   900.000000             900.000000           899.000000\n",
              "mean        0.890000     0.907778               0.907778             0.839822\n",
              "std         0.313064     0.289500               0.289500             0.366975\n",
              "min         0.000000     0.000000               0.000000             0.000000\n",
              "25%         1.000000     1.000000               1.000000             1.000000\n",
              "50%         1.000000     1.000000               1.000000             1.000000\n",
              "75%         1.000000     1.000000               1.000000             1.000000\n",
              "max         1.000000     1.000000               1.000000             1.000000"
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              "          document.querySelector('#df-004c7734-b12c-4bf3-ac1e-7b2006dd8973 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-004c7734-b12c-4bf3-ac1e-7b2006dd8973');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Model'] = df['Model'].map({\"banglat5model\":\"banglat5\",\"mt5_small\":\"mt5_small\"})"
      ],
      "metadata": {
        "id": "B7hbJDr5LpRT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mtick\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "uFX1LfKlrQ9z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = df.groupby([\"Model\",\"Setting\"]).mean()"
      ],
      "metadata": {
        "id": "Ad_jn3UzuO6I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "5Gu953reMWg1",
        "outputId": "a3104282-5237-446e-bfcc-52930039886a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             Answerability  Correctness  \\\n",
              "Model     Setting                                         \n",
              "banglat5  Beam-search(5)          0.966667     0.986667   \n",
              "          Greedy                  0.960000     0.973333   \n",
              "          Topk-10,temp-0.7.       0.886667     0.920000   \n",
              "mt5_small Beam-search(5)          0.906667     0.913333   \n",
              "          Greedy                  0.873333     0.893333   \n",
              "          Topk-10,temp-0.7.       0.746667     0.760000   \n",
              "\n",
              "                             Relevance _to_context  Relevance_to_answer  \n",
              "Model     Setting                                                        \n",
              "banglat5  Beam-search(5)                  0.973333             0.960000  \n",
              "          Greedy                          0.980000             0.960000  \n",
              "          Topk-10,temp-0.7.               0.953333             0.866667  \n",
              "mt5_small Beam-search(5)                  0.900000             0.813333  \n",
              "          Greedy                          0.893333             0.840000  \n",
              "          Topk-10,temp-0.7.               0.746667             0.597315  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Answerability</th>\n",
              "      <th>Correctness</th>\n",
              "      <th>Relevance _to_context</th>\n",
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              "      <th rowspan=\"3\" valign=\"top\">banglat5</th>\n",
              "      <th>Beam-search(5)</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.986667</td>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greedy</th>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topk-10,temp-0.7.</th>\n",
              "      <td>0.886667</td>\n",
              "      <td>0.920000</td>\n",
              "      <td>0.953333</td>\n",
              "      <td>0.866667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">mt5_small</th>\n",
              "      <th>Beam-search(5)</th>\n",
              "      <td>0.906667</td>\n",
              "      <td>0.913333</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.813333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greedy</th>\n",
              "      <td>0.873333</td>\n",
              "      <td>0.893333</td>\n",
              "      <td>0.893333</td>\n",
              "      <td>0.840000</td>\n",
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              "    <tr>\n",
              "      <th>Topk-10,temp-0.7.</th>\n",
              "      <td>0.746667</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>0.746667</td>\n",
              "      <td>0.597315</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bf7f0e07-e5d1-46b3-b15d-467a90ff8253');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "context_good_answer_fail = df[(df['Answerability']==1) & (df['Correctness'] == 1) & (df['Relevance _to_context'] == 1) & (df['Relevance_to_answer']==0)]"
      ],
      "metadata": {
        "id": "qpjQeOaWPMkh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(context_good_answer_fail)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6onuLlFUPnnv",
        "outputId": "ab9b2554-76de-48a2-ad11-0c5baa6966a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(58/900)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kYj-0zLWPqMi",
        "outputId": "8bba997d-d35b-41e4-b464-f37784bb4a33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.444444444444445"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.set(rc={'figure.figsize':(10,12)})\n",
        "sns.set_style(\"ticks\")\n",
        "g = sns.catplot(\n",
        "    data=df, col=\"Model\",\n",
        "    kind=\"bar\"\n",
        ")\n",
        "g.set_xticklabels(\n",
        "    labels=results.columns, rotation=30)\n",
        "g.savefig(\"human_results.png\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "T0uiyvvsLL-_",
        "outputId": "f66572b4-e4de-4416-a146-9d59c6c1ba6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.set_style(\"ticks\")\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "ax = sns.barplot(data=df,col='Model')\n",
        "ax.set(xlabel='Dimensions of Human Evaluation', ylabel='Questions',title='Results of Human Evaluation');\n",
        "sns.despine(bottom = True, left = True)\n",
        "plt.savefig(\"human_results.png\")\n",
        "#ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "yphASZhSsE4V",
        "outputId": "cde3128d-c0bb-4a30-fe00-dd0447e71c86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2396\u001b[0m                 \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'_nolegend_'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2397\u001b[0m                 )\n\u001b[0;32m-> 2398\u001b[0;31m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2399\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2400\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0morientation\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'vertical'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, props)\u001b[0m\n\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1006\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1008\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1006\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1008\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[0;34m(self, k, v)\u001b[0m\n\u001b[1;32m   1000\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1001\u001b[0m                     raise AttributeError('{!r} object has no property {!r}'\n\u001b[0;32m-> 1002\u001b[0;31m                                          .format(type(self).__name__, k))\n\u001b[0m\u001b[1;32m   1003\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Rectangle' object has no property 'col'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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KEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAXVFV87d+7MggUL0tzcnAULFmTXrl391lSr1bS1tWXWrFm59tprs27dusGeFQBg2KsrvpYtW5aFCxfmhRdeyMKFC7N06dJ+a5577rns3r07mzZtyjPPPJOVK1dmz549gz4wAMBwdtZACw4ePJiurq6sWbMmSdLS0pKHH344hw4dypgxY06s27BhQ26++eaMHDkyY8aMyaxZs7Jx48bceeedfY7X29ub3t7ePo/t3bs3SdLT0/O5TwgA4HT6T69Uq9VTev6A8dXd3Z0JEyakUqkkSSqVSsaPH5/u7u4+8dXd3Z1Jkyad+L6xsfGkMdXe3p5Vq1ad9Ge1trZ+5hMAABgKu3btyle+8pXP/LwB42uwLVq0KPPmzevz2O7du3PHHXfk6aefzvnnn196JD6nnp6etLa2Zu3atZk4ceJQj8NnYO+GN/s3fNm74W3v3r257bbbcsEFF5zS8weMr8bGxuzbty/VajWVSiXVajX79+9PY2Njv3XvvvtuLrvssiT9Xwn7j4aGhjQ0NJz0Z51//vmZPHnyqZwHXwATJ060f8OUvRve7N/wZe+Gt1GjRp3S8wa84H7s2LFpampKZ2dnkqSzszNNTU193nJMktmzZ2fdunU5fvx4Dh06lBdffDHNzc2nNBQAwJmqrt92XL58eTo6OtLc3JyOjo60tbUlSZYsWZLt27cnSebOnZvJkyfnuuuuy/z583PPPfec8stxAABnqrqu+Zo6depJP7dr9erVJ/5cqVRORBkAACdXWb58+fKhHiJJzj777Fx11VU5++yzh3oUToH9G77s3fBm/4Yveze8fZ79G1Gr1WqnYSYAAE7CvR0BAAoSXwAABRWNLzfoHt7q2b/HH388N9xwQ+bMmZPvfOc7+dOf/lR+UPqpZ+/+4+23387ll1+eFStWlBuQT1Xv/m3YsCFz5sxJS0tL5syZkwMHDpQdlJOqZ/8OHjyYu+66K3PmzMn111+f5cuX56OPPio/LH2sWLEiM2bMyLRp0/Lmm2+edM0pdUutoFtvvbW2fv36Wq1Wq61fv75266239lvzhz/8obZ48eJatVqtHTx4sDZ9+vTaO++8U3JMPkE9+7d58+bakSNHarVarfaPf/yjdsUVV9Q++OCDonPSXz17V6vVah999FHtlltuqf3gBz+o/eIXvyg5Ip+inv3btm1b7frrr6/t37+/VqvVar29vbUPP/yw6JycXD3798gjj5z4N3f06NHaTTfdVHv++eeLzkl/f/nLX2rvvvtu7Zvf/GZtx44dJ11zKt1S7JWv/9ygu6WlJcm/b9Dd1dWVQ4cO9Vn3STfoZmjVu3/Tp0/PueeemySZNm1aarVaDh8+XHxe/r969y5JnnzyyVxzzTWZMmVK4Sn5JPXu31NPPZXFixdn3LhxSZLRo0f7LbovgHr3b8SIEXn//fdz/PjxHD16NMeOHcuECROGYmQ+5sorr+x3R5//dirdUiy+Pu0G3f+9rp4bdFNWvfv3cevXr8+FF17ovmVDrN69e+ONN7Jly5bcfvvtQzAln6Te/XvrrbfyzjvvpLW1NfPmzcsTTzyRml9mH3L17t/dd9+dnTt35uqrrz7xdcUVVwzFyHxGp9ItLrjntHjttdfy6KOP5le/+tVQj0Idjh07loceeihtbW0n/iPB8FKtVrNjx46sWbMmv/3tb7N58+Y8++yzQz0Wddq4cWOmTZuWLVu2ZPPmzfnrX//qXZ8zWLH4+vgNupMMeIPu/+ju7vbKyRdAvfuXJK+//nruv//+PP7447noootKj8p/qWfv3nvvvezevTt33XVXZsyYkfb29vzud7/LQw89NFRj87/q/bc3adKkzJ49O6NGjcqXv/zlzJw5M9u2bRuKkfmYevevo6Mj3/72tzNy5MiMHj06M2bMyNatW4diZD6jU+mWYvHlBt3DW737t23bttx333157LHHcumllw7FqPyXevZu0qRJ2bp1a15++eW8/PLLWbRoUebPn5+HH354qMbmf9X7b6+lpSVbtmxJrVbLsWPH8uqrr+aSSy4ZipH5mHr3b/Lkydm8eXOS5OjRo3nllVdy8cUXF5+Xz+5UuqXoJ9y/9dZbefDBB9Pb25uGhoasWLEiF110UZYsWZLvf//7+frXv55qtZqf/vSn+fOf/5zk3zfvXrBgQakR+RT17N93v/vd7N27t8+For/85S8zbdq0IZycevbu41auXJkjR47kgQceGKKJ+bh69u/48eNZsWJFNm/enJEjR+bqq6/OAw88kJEjXV0y1OrZv927d2fZsmU5cOBAqtVqrrrqqvz4xz/OWWfVdQtmTpNHHnkkmzZtyoEDB/I///M/Oe+88/L8889/7m5xeyEAgIL8LxEAQEHiCwCgIPEFAFCQ+AIAKEh8AQAUJL4AAAoSXwAABYkvAICC/h+G83H7EiYjuQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Xo5PhHjR7v5J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "85GL6mXa6xzW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}