th2enzh_data_exploration.ipynb

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      "source": [
        "!pip install -q pythainlp\n",
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            "\u001b[K     |████████████████████████████████| 11.0 MB 4.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 743 kB 46.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 432 kB 5.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 170 kB 62.8 MB/s \n",
            "\u001b[?25h  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import stanza\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from numpy import save\n",
        "from tqdm.notebook import tqdm \n",
        "import matplotlib.pyplot as plt\n",
        "from pythainlp import word_tokenize\n",
        "\n",
        "sns.set_theme()"
      ],
      "metadata": {
        "id": "BfebQWZSkAVa"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Downloading English and Chinese model...\")\n",
        "stanza.download('en')\n",
        "stanza.download('zh') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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            "ffb4b693ab504a8797ff6775aa41e9db"
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        },
        "id": "Mg9DNoEqklPo",
        "outputId": "4c8b42ab-03a0-4604-bb23-7cd0cae859eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading English and Chinese model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e627cfc3371b48c4ab595d936f50ab14",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.3.0.json:   0%|   …"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2021-12-16 19:13:40 INFO: Downloading default packages for language: en (English)...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fc92cc845f51444e8fc8f27d74b8db42",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading https://huggingface.co/stanfordnlp/stanza-en/resolve/v1.3.0/models/default.zip:   0%|          | 0…"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2021-12-16 19:13:55 INFO: Finished downloading models and saved to /root/stanza_resources.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c98faf81e977428a8e741e817f7a5bdd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.3.0.json:   0%|   …"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2021-12-16 19:13:55 INFO: \"zh\" is an alias for \"zh-hans\"\n",
            "2021-12-16 19:13:55 INFO: Downloading default packages for language: zh-hans (Simplified_Chinese)...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "73d03c90a5ab4a729263460449b554f0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading https://huggingface.co/stanfordnlp/stanza-zh-hans/resolve/v1.3.0/models/default.zip:   0%|        …"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2021-12-16 19:14:17 INFO: Finished downloading models and saved to /root/stanza_resources.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FofhcAlajyx7"
      },
      "outputs": [],
      "source": [
        "th2en_df = pd.read_csv('/content/drive/MyDrive/Final_Datasets/final_crosslingual_sum_datasets/th2en_full.csv', encoding='utf-8')\n",
        "th2zh_df = pd.read_csv('/content/drive/MyDrive/Final_Datasets/final_crosslingual_sum_datasets/th2zh_full.csv', encoding='utf-8')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/Final_Datasets/final_crosslingual_sum_datasets/testset.csv', encoding='utf-8')\n",
        "validation_df = pd.read_csv('/content/drive/MyDrive/Final_Datasets/final_crosslingual_sum_datasets/validation.csv', encoding='utf-8')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Data Fields"
      ],
      "metadata": {
        "id": "Gk6YX6d7rxUY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1) Thai-to-English Dataset (TH2EN)"
      ],
      "metadata": {
        "id": "scttcRqIsKu_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'TH2EN contains: {th2en_df.columns}, with {len(th2en_df)} rows.')\n",
        "th2en_df.sample(n=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "84QD-ZQUsbuL",
        "outputId": "279b1cba-3bfb-4ee1-ae5c-e9f150694482"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TH2EN contains: Index(['th_body', 'th_sum', 'en_body', 'en_sum', 'en2th', 'url'], dtype='object'), with 310926 rows.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-fe79edcb-cd98-4ef5-a708-5d275bf6abea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>th_body</th>\n",
              "      <th>th_sum</th>\n",
              "      <th>en_body</th>\n",
              "      <th>en_sum</th>\n",
              "      <th>en2th</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>287150</th>\n",
              "      <td>ตำรวจ สภ.นาโยง จ.ตรัง ตรวจสอบที่เกิดเหตุยิงกัน...</td>\n",
              "      <td>เกิดเหตุยิงกันบนถนนเพชรเกษม จ.ตรัง เมื่อช่วงกล...</td>\n",
              "      <td>Police, security. Na Yong, Chiang Mai Trang ex...</td>\n",
              "      <td>A fire broke out on Petchkasem Road in Trang p...</td>\n",
              "      <td>เหตุไฟไหม้ที่ถนนเพชรเกษม จังหวัดตรัง กลางดึก (...</td>\n",
              "      <td>https://news.thaipbs.or.th/content/252243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1641</th>\n",
              "      <td>สยามบรมราชกุมารี พร้อมเงินรางวัลรวม 1.77 ล้าน...</td>\n",
              "      <td>เปิดเวทีให้เยาวชนไทยได้แสดงความสามารถด้านต่างๆ...</td>\n",
              "      <td>Siam Boromrajakri with a total prize of 1.77 m...</td>\n",
              "      <td>Open the stage for Thai youth to show their ta...</td>\n",
              "      <td>เปิดเวทีให้เยาวชนไทยได้แสดงความสามารถของตนในรู...</td>\n",
              "      <td>https://www.thairath.co.th/lifestyle/life/1403202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221142</th>\n",
              "      <td>เมื่อวันที่ 1 มี.ค. นายพรชัย ฐีระเวช รองผู้อำน...</td>\n",
              "      <td>คลัง ย้ำเปิดลงทะเบียนคนจนรอบใหม่ 3 เม.ย.-15 พ....</td>\n",
              "      <td>On March 1. c. Mr. Pornchai Theravej, Deputy D...</td>\n",
              "      <td>Archives reiterated the opening of new registr...</td>\n",
              "      <td>หอจดหมายเหตุย้ำเปิดการลงทะเบียนใหม่สำหรับคนยาก...</td>\n",
              "      <td>https://www.thairath.co.th/content/871330</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe79edcb-cd98-4ef5-a708-5d275bf6abea')\"\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-fe79edcb-cd98-4ef5-a708-5d275bf6abea 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-fe79edcb-cd98-4ef5-a708-5d275bf6abea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                  th_body  ...                                                url\n",
              "287150  ตำรวจ สภ.นาโยง จ.ตรัง ตรวจสอบที่เกิดเหตุยิงกัน...  ...          https://news.thaipbs.or.th/content/252243\n",
              "1641     สยามบรมราชกุมารี พร้อมเงินรางวัลรวม 1.77 ล้าน...  ...  https://www.thairath.co.th/lifestyle/life/1403202\n",
              "221142  เมื่อวันที่ 1 มี.ค. นายพรชัย ฐีระเวช รองผู้อำน...  ...          https://www.thairath.co.th/content/871330\n",
              "\n",
              "[3 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2) Thai-to-Simplified Chinese Dataset (TH2ZH)"
      ],
      "metadata": {
        "id": "d9JJOszhtVzi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'TH2ZH contains: {th2zh_df.columns}, with {len(th2zh_df)} rows.')\n",
        "th2zh_df.sample(n=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "-Ym0_EbjtjnV",
        "outputId": "a7e4b5ec-cdd0-4421-fcc7-c4a95faf9327"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TH2ZH contains: Index(['th_body', 'th_sum', 'zh_body', 'zh_sum', 'zh2th', 'url'], dtype='object'), with 310926 rows.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e557d6f9-0272-432b-9eaa-8a3e0e8f2882\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>th_body</th>\n",
              "      <th>th_sum</th>\n",
              "      <th>zh_body</th>\n",
              "      <th>zh_sum</th>\n",
              "      <th>zh2th</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>163076</th>\n",
              "      <td>การตรวจสอบยาแก้หวัดที่หายจากโรงพยาบาลกมลาไสย จ...</td>\n",
              "      <td>ผู้ต้องสงสัยที่ถูกซัดทอดรับซื้อยาแก้หวัด สูตร...</td>\n",
              "      <td>检查从加拉信省卡马拉赛医院回收的 30 多万片感冒药。 警方发现，至少有 5 名医院工作人员...</td>\n",
              "      <td>从加拉信省 Kamasai 医院药剂师转移到购买一种普通感冒药物的嫌疑人，随着警方打击近 4...</td>\n",
              "      <td>การย้ายจากเภสัชกรที่โรงพยาบาลคามาไซ จังหวัดการ...</td>\n",
              "      <td>https://news.thaipbs.or.th/content/74105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38538</th>\n",
              "      <td>กลุ่มหมอกควันที่เกิดจากไฟป่า บริเวณริมทางหลวง ...</td>\n",
              "      <td>ปัญหาหมอกควันหลายจังหวัดภาคเหนือยังน่าเป็นห่ว...</td>\n",
              "      <td>烟雾引起的公路上的野火。 南-帕府覆盖在该地区。 南已经降低了知名度，即使是艾纳莱分区行政组...</td>\n",
              "      <td>北部许多省份的烟雾问题也令人担忧，尽管空气质量测量显示某些省份的灰尘含量减少，但也发现隐形和...</td>\n",
              "      <td>ปัญหาควันยังเป็นเรื่องกังวลในหลายจังหวัดภาคเหน...</td>\n",
              "      <td>https://news.thaipbs.or.th/content/155169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300884</th>\n",
              "      <td>นายวุฒิชาติ กัลยาณมิตร ผู้ว่าการการรถไฟแห่งประ...</td>\n",
              "      <td>วุฒิชาติ เผย รฟท.จัดรถไฟฟรี 160 ขบวนต่อวัน อำน...</td>\n",
              "      <td>泰国铁路局长 Wutthicat Kalyanamit 先生透露, 内阁批准将自由铁路措施的...</td>\n",
              "      <td>Wutthachart 公布 SRT 免费 160 列车，每天根据公共行政部之后的国家政策，...</td>\n",
              "      <td>วุฒิชาตเผยแพร่รถไฟฟรี 160 ขบวน ซึ่งอำนวยความสะ...</td>\n",
              "      <td>https://www.thairath.co.th/content/539250</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e557d6f9-0272-432b-9eaa-8a3e0e8f2882')\"\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-e557d6f9-0272-432b-9eaa-8a3e0e8f2882 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-e557d6f9-0272-432b-9eaa-8a3e0e8f2882');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                  th_body  ...                                        url\n",
              "163076  การตรวจสอบยาแก้หวัดที่หายจากโรงพยาบาลกมลาไสย จ...  ...   https://news.thaipbs.or.th/content/74105\n",
              "38538   กลุ่มหมอกควันที่เกิดจากไฟป่า บริเวณริมทางหลวง ...  ...  https://news.thaipbs.or.th/content/155169\n",
              "300884  นายวุฒิชาติ กัลยาณมิตร ผู้ว่าการการรถไฟแห่งประ...  ...  https://www.thairath.co.th/content/539250\n",
              "\n",
              "[3 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3) Validation Set"
      ],
      "metadata": {
        "id": "LkNFCKAguf5I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Validation set contains: {validation_df.columns}, with {len(validation_df )} rows.')\n",
        "validation_df.sample(n=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "SWuIKCevuhyu",
        "outputId": "98655465-3867-42e2-e0e2-e74053c124e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation set contains: Index(['th_body', 'th_sum', 'en_body', 'en_sum', 'zh_body', 'zh_sum', 'en2th',\n",
            "       'zh2th', 'en_gg_sum', 'zh_gg_sum', 'url'],\n",
            "      dtype='object'), with 3000 rows.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1c1acec7-2d23-4cef-ac2c-fd355607d3d7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>th_body</th>\n",
              "      <th>th_sum</th>\n",
              "      <th>en_body</th>\n",
              "      <th>en_sum</th>\n",
              "      <th>zh_body</th>\n",
              "      <th>zh_sum</th>\n",
              "      <th>en2th</th>\n",
              "      <th>zh2th</th>\n",
              "      <th>en_gg_sum</th>\n",
              "      <th>zh_gg_sum</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1384</th>\n",
              "      <td>หากพูดถึงละครรสแซ่บนาทีนี้ต้องยกให้ละครเรื่อง ...</td>\n",
              "      <td>หากพูดถึงละครรสแซ่บนาทีนี้ต้องยกให้ละครเรื่อง ...</td>\n",
              "      <td>If you talk about this delicious drama, you ha...</td>\n",
              "      <td>If you talk about this miniseries, you have to...</td>\n",
              "      <td>如果你谈论这个美味的戏剧，你必须给电视剧 “斯瓦特” 8 频道，那里的屏幕是激烈的戏剧一代代...</td>\n",
              "      <td>如果你谈论这个短剧，你必须给电视剧 “特警” 8 频道，其中屏幕是激烈的戏剧，从一代代。</td>\n",
              "      <td>หากพูดถึงกระทรวงนี้ ต้องให้ละครเรื่อง “สวาท” ท...</td>\n",
              "      <td>หากพูดถึงบทละครนี้ต้องมอบละครโทรทัศน์เรื่อง “ส...</td>\n",
              "      <td>If talking about a hot taste drama, this minut...</td>\n",
              "      <td>如果要在这一分钟谈论热辣的戏剧，则必须在第8频道播出电视剧“ Ruea Saisawad”，...</td>\n",
              "      <td>https://www.thairath.co.th/entertain/news/1776470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>333</th>\n",
              "      <td>กรมอนามัยชูวิถีใหม่กันเชื้อ แย้มสัปดาห์หน้าปลด...</td>\n",
              "      <td>วิษณุ สะอึก เห็นคนแห่เที่ยวบางแสน กระทบแผนผ่อน...</td>\n",
              "      <td>The Department of Health prevented the infecti...</td>\n",
              "      <td>Vishnu Sauk saw people flocking to Bang Saen, ...</td>\n",
              "      <td>卫生署预防感染下周四期纳龙柴威胁要关闭几十万海滩旅行和 6 做法的游客纳龙柴昆伦。 如果游客...</td>\n",
              "      <td>维什努·索克看到人们涌向 Bang Saen，影响了 4 期间的计划和 B.E. 答：紧急情...</td>\n",
              "      <td>พระวิษณุสุขเห็นคนแห่กันไปบางแสน กระทบแผนช่วงที...</td>\n",
              "      <td>พระวิษณุถุงเท้าเห็นคนแห่กันไปบางแสน กระทบต่อแผ...</td>\n",
              "      <td>Vishnu hiccup to see people flocking to Bangsa...</td>\n",
              "      <td>毗湿奴打ic看到人们涌向Bangsaen 影响4个月的分期付款计划和紧急法令 至于泼水节，没...</td>\n",
              "      <td>https://www.thairath.co.th/news/local/1862239</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c1acec7-2d23-4cef-ac2c-fd355607d3d7')\"\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-1c1acec7-2d23-4cef-ac2c-fd355607d3d7 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-1c1acec7-2d23-4cef-ac2c-fd355607d3d7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                th_body  ...                                                url\n",
              "1384  หากพูดถึงละครรสแซ่บนาทีนี้ต้องยกให้ละครเรื่อง ...  ...  https://www.thairath.co.th/entertain/news/1776470\n",
              "333   กรมอนามัยชูวิถีใหม่กันเชื้อ แย้มสัปดาห์หน้าปลด...  ...      https://www.thairath.co.th/news/local/1862239\n",
              "\n",
              "[2 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.4) Test Set"
      ],
      "metadata": {
        "id": "vg1YaZIku6hb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'test set contains: {test_df.columns}, with {len(test_df )} rows.')\n",
        "test_df.sample(n=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "8JPCCBm1u8PP",
        "outputId": "39fc4ba1-3012-43ab-f207-0c7c9cf53f36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test set contains: Index(['th_body', 'th_sum', 'en_body', 'en_sum', 'zh_body', 'zh_sum', 'en2th',\n",
            "       'zh2th', 'en_gg_sum', 'zh_gg_sum', 'th_title', 'en_title', 'zh_title',\n",
            "       'url'],\n",
            "      dtype='object'), with 3000 rows.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5a193038-8285-48ab-9706-41423c66a817\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>th_body</th>\n",
              "      <th>th_sum</th>\n",
              "      <th>en_body</th>\n",
              "      <th>en_sum</th>\n",
              "      <th>zh_body</th>\n",
              "      <th>zh_sum</th>\n",
              "      <th>en2th</th>\n",
              "      <th>zh2th</th>\n",
              "      <th>en_gg_sum</th>\n",
              "      <th>zh_gg_sum</th>\n",
              "      <th>th_title</th>\n",
              "      <th>en_title</th>\n",
              "      <th>zh_title</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>462</th>\n",
              "      <td>สั่งทีมซักฟอก ปชป.ไล่ขยี้ภท. รับผิด กดบัตรป้อง...</td>\n",
              "      <td>สนธิรัตน์ รอ ชวน ชี้ ขาดผลสอบ ส.ส.รัฐบาลกดบัตร...</td>\n",
              "      <td>Order a DEA washing team. Chasing down the wor...</td>\n",
              "      <td>Sookrat Roo Chuan pointed out the lack of test...</td>\n",
              "      <td>订购一个 DEA 洗衣队。 追逐世界 责任按早期保护卡有关没有意图打倒 63 Nipit 的...</td>\n",
              "      <td>苏克拉特鲁川指出缺乏测试结果。 星期六。 政府在整个议会会议室的生日派对上按了可互换卡。 重...</td>\n",
              "      <td>สุขรัตน์ รูชวนชี้ให้เห็นว่าขาดผลการทดสอบ ส. รั...</td>\n",
              "      <td>สุคราตลุชนชี้ว่าขาดผลการทดสอบ ในปี พ. วันเสาร์...</td>\n",
              "      <td>Sonthirat awaits to suggest the lack of examin...</td>\n",
              "      <td>桑西拉特（Sonthirat）等待提出缺乏考试结果的建议，政府议员则改为按要求出示证件。 在...</td>\n",
              "      <td>รุมกินโต๊ะบิ๊กตู่ แผนเพื่อไทยเล่นให้น่วม</td>\n",
              "      <td>Swarm around Big Tu Plans for Thailand to play...</td>\n",
              "      <td>群聚大屠杀泰国计划一起玩</td>\n",
              "      <td>https://www.thairath.co.th/news/politic/1756895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>สาวใหญ่วัย 40 ที่พิษณุโลก ออกโรงแทนเพื่อน ตบตี...</td>\n",
              "      <td>สาวใหญ่วัย 40 ที่พิษณุโลก ออกโรงแทนเพื่อน ตบตี...</td>\n",
              "      <td>Big girl in 40 years at Phitsanulok instead of...</td>\n",
              "      <td>Big girl in 40 years at Phitsanulok instead of...</td>\n",
              "      <td>大女孩在 40 年在彭世洛，而不是她的朋友。g. 14 岁后一个朋友聊天聊天，而孩子不想任何...</td>\n",
              "      <td>大女孩在 40 年在彭世洛，而不是她的朋友。g. 14 岁后，他的朋友聊天聊天，而孩子什么都...</td>\n",
              "      <td>สาวใหญ่ในรอบ 40 ปี ที่พิษณุโลกแทนเพื่อน กรัม อ...</td>\n",
              "      <td>สาวใหญ่ในรอบ 40 ปีที่จังหวัดพิษณุโลก ไม่ใช่เพื...</td>\n",
              "      <td>A 40-year-old girl in Phitsanulok premiere on ...</td>\n",
              "      <td>一位丈夫聊天后，一名40岁女孩在彭世洛代表14岁的女友首映。 孩子说话的时候，他什么也没想。...</td>\n",
              "      <td>สาวใหญ่วัย 40 ออกโรงแทน ตบตี ด.ญ.วัย 14 หลังผั...</td>\n",
              "      <td>40-year-old girl premiere instead of slapping ...</td>\n",
              "      <td>40岁的女孩首映，而不是在与丈夫聊天后对14岁的女孩打耳光</td>\n",
              "      <td>https://www.thairath.co.th/news/society/1763743</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a193038-8285-48ab-9706-41423c66a817')\"\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-5a193038-8285-48ab-9706-41423c66a817 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-5a193038-8285-48ab-9706-41423c66a817');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                               th_body  ...                                              url\n",
              "462  สั่งทีมซักฟอก ปชป.ไล่ขยี้ภท. รับผิด กดบัตรป้อง...  ...  https://www.thairath.co.th/news/politic/1756895\n",
              "252  สาวใหญ่วัย 40 ที่พิษณุโลก ออกโรงแทนเพื่อน ตบตี...  ...  https://www.thairath.co.th/news/society/1763743\n",
              "\n",
              "[2 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Word Count Distribution"
      ],
      "metadata": {
        "id": "X-JiSqB1wBQZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "zh_nlp = stanza.Pipeline('zh', processors='tokenize', verbose=False, use_gpu=True)\n",
        "en_nlp = stanza.Pipeline('en', processors='tokenize', verbose=False, use_gpu=True)\n",
        "\n",
        "def tokenize_zh(text):\n",
        "  zh_doc = zh_nlp(text)\n",
        "  tokenized_words = []\n",
        "  for i, sent in enumerate(zh_doc.sentences):\n",
        "    for word in sent.words:\n",
        "      tokenized_words.append(word.text)   \n",
        "  return {'num_words': zh_doc.num_words, 'tokenized_text': tokenized_words}\n",
        "\n",
        "def tokenize_en(text):\n",
        "  en_doc = en_nlp(text)\n",
        "  tokenized_words = []\n",
        "  for i, sent in enumerate(en_doc.sentences):\n",
        "    for word in sent.words:\n",
        "      tokenized_words.append(word.text)   \n",
        "  return  {'num_words': en_doc.num_words, 'tokenized_text': tokenized_words}\n",
        "\n",
        "def tokenize_th(text):\n",
        "  th_doc = word_tokenize(text, keep_whitespace=False)\n",
        "  return {'num_words': len(th_doc), 'tokenized_text': th_doc}"
      ],
      "metadata": {
        "id": "WSywATIawqUs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "th_body_wc_train = []\n",
        "zh_body_wc_train = []\n",
        "en_body_wc_train = []\n",
        "th_sum_wc_train = []\n",
        "zh_sum_wc_train = []\n",
        "en_sum_wc_train = []\n",
        "\n",
        "for index, row in tqdm(th2en_df.iterrows(), total=th2en_df.shape[0]):\n",
        "  th_body_wc_train.append(tokenize_th(row['th_body'])['num_words'])\n",
        "  zh_body_wc_train.append(tokenize_zh(th2zh_df.iloc[index]['zh_body'])\\\n",
        "                          ['num_words'])\n",
        "  en_body_wc_train.append(tokenize_en(row['en_body'])['num_words'])\n",
        "\n",
        "  th_sum_wc_train.append(tokenize_th(row['th_sum'])['num_words'])\n",
        "  zh_sum_wc_train.append(tokenize_zh(th2zh_df.iloc[index]['zh_sum'])\\\n",
        "                          ['num_words'])\n",
        "  en_sum_wc_train.append(tokenize_en(row['th_sum'])['num_words'])\n",
        "  \n",
        "save('th_body.npy', np.array(th_body_wc_train))\n",
        "save('zh_body.npy', np.array(zh_body_wc_train))\n",
        "save('en_body.npy', np.array(en_body_wc_train))\n",
        "save('th_sum.npy', np.array(th_sum_wc_train))\n",
        "save('zh_sum.npy', np.array(zh_sum_wc_train))\n",
        "save('en_sum.npy', np.array(en_sum_wc_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432,
          "referenced_widgets": [
            "7515b92a384345a28bb96a5aaf4f4c21",
            "bb274294af904e75a56abbf169db881b",
            "567854ffb1fb4538bab04165ec524fac",
            "f809eb6e01d44396a028bc5b5fed3139",
            "adcaf1d9524d4eb485a5ace2123a79c0",
            "42f14bb08f09431e9e61d42ad4683cdc",
            "fb518cd4717f481eb37e9596fa8b243b",
            "84571d11060f4ce0b072dc4b0b002fa7",
            "9f468cd57d8d44468b56b727cadd69df",
            "e224e95774764bc08d6025dd810822e1",
            "ee9959c489934075831799dac7344d1d"
          ]
        },
        "id": "XWzYEZhKa21m",
        "outputId": "fc52edea-1ca4-4956-d818-3d5797bc3945"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7515b92a384345a28bb96a5aaf4f4c21",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/310926 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-611febcf1b66>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m   \u001b[0mth_body_wc_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_th\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'th_body'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'num_words'\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     10\u001b[0m   \u001b[0mzh_body_wc_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_zh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mth2zh_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zh_body'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m                          \u001b[0;34m[\u001b[0m\u001b[0;34m'num_words'\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---> 11\u001b[0;31m   \u001b[0men_body_wc_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_en\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'en_body'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'num_words'\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     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m   \u001b[0mth_sum_wc_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_th\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'th_sum'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'num_words'\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<ipython-input-13-dd0f716ee01c>\u001b[0m in \u001b[0;36mtokenize_en\u001b[0;34m(text)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtokenize_en\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\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---> 13\u001b[0;31m   \u001b[0men_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0men_nlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\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     14\u001b[0m   \u001b[0mtokenized_words\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[1;32m     15\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0men_doc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msentences\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/stanza/pipeline/core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, doc)\u001b[0m\n\u001b[1;32m    229\u001b[0m         assert any([isinstance(doc, str), isinstance(doc, list),\n\u001b[1;32m    230\u001b[0m                     isinstance(doc, Document)]), 'input should be either str, list or Document'\n\u001b[0;32m--> 231\u001b[0;31m         \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\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    232\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stanza/pipeline/core.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, doc)\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocessors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessor_name\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    224\u001b[0m                 \u001b[0mprocess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocessors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mprocessor_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbulk_process\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbulk\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocessors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mprocessor_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m                 \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\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    226\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stanza/pipeline/tokenize_processor.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, document)\u001b[0m\n\u001b[1;32m     88\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'max_seqlen'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTokenizeProcessor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMAX_SEQ_LENGTH_DEFAULT\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     89\u001b[0m                                                \u001b[0morig_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraw_text\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m                                                no_ssplit=self.config.get('no_ssplit', False))\n\u001b[0m\u001b[1;32m     91\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDocument\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocument\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stanza/models/tokenization/utils.py\u001b[0m in \u001b[0;36moutput_predictions\u001b[0;34m(output_file, trainer, data_generator, vocab, mwt_dict, max_seqlen, orig_text, no_ssplit, use_regex_tokens)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0mN\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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    271\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mN\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0meval_limit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m             \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\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    273\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m             \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatchparas\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/stanza/models/tokenization/trainer.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\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     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\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     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\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   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\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[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/stanza/models/tokenization/model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, feats)\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0memb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0memb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeats\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m         \u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrnn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0memb\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     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'conv_res'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\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   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\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[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/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m    690\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbatch_sizes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m             result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n\u001b[0;32m--> 692\u001b[0;31m                               self.dropout, self.training, self.bidirectional, self.batch_first)\n\u001b[0m\u001b[1;32m    693\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m             result = _VF.lstm(input, batch_sizes, hx, self._flat_weights, self.bias,\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1) Word count distribution of `{th/en/zh}_body`\n",
        "\n"
      ],
      "metadata": {
        "id": "hGO8ErOlVWuW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_th_body = np.load('/content/th_body.npy')\n",
        "train_th_sum = np.load('/content/th_sum.npy')\n",
        "train_en_body = np.load('/content/en_body.npy')\n",
        "train_en_sum = np.load('/content/en_sum.npy')\n",
        "train_zh_body = np.load('/content/zh_body.npy')\n",
        "train_zh_sum = np.load('/content/zh_sum.npy')\n",
        "\n",
        "validation_wc_df = pd.read_csv('/content/validation_wc.csv')\n",
        "test_wc_df = pd.read_csv('/content/testset_wc.csv')"
      ],
      "metadata": {
        "id": "YfsY5Ek6VU_w"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "These numpy files and csv files can be downloaded at [https://drive.google.com/drive/folders/1ksJn41TWZHzektJRD0LfHvj4jRtO73Gy](https://drive.google.com/drive/folders/1ksJn41TWZHzektJRD0LfHvj4jRtO73Gy?usp=sharing)."
      ],
      "metadata": {
        "id": "vIQwJ62QoyPG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_wc_df.sample(n=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "mIeUDZepeO1E",
        "outputId": "42b7aace-23b1-45ae-b943-b5858384f881"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th>2514</th>\n",
              "      <td>370.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>474.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>334.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>https://www.thairath.co.th/news/society/1777601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2198</th>\n",
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              "      <td>14.0</td>\n",
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              "      <td>14.0</td>\n",
              "      <td>https://www.thairath.co.th/entertain/news/1815616</td>\n",
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              "    <tr>\n",
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              "      <td>1947.0</td>\n",
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              "      <td>1555.0</td>\n",
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              "      <td>34.0</td>\n",
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              "      <td>19.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>https://www.thairath.co.th/news/auto/news/1777063</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      th_body  ...                                                url\n",
              "2514    370.0  ...    https://www.thairath.co.th/news/society/1777601\n",
              "2198    308.0  ...  https://www.thairath.co.th/entertain/news/1815616\n",
              "1216   1947.0  ...  https://www.thairath.co.th/news/auto/news/1777063\n",
              "\n",
              "[3 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_wc_df.sample(n=3)"
      ],
      "metadata": {
        "id": "GNWJVTvdeTGU",
        "outputId": "849a6b4a-f1ad-4cbe-d904-6e8c4b140791",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f9317093-b02d-4dd4-a791-19b954a282ab\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>th_body</th>\n",
              "      <th>th_sum</th>\n",
              "      <th>en_body</th>\n",
              "      <th>en_sum</th>\n",
              "      <th>zh_body</th>\n",
              "      <th>zh_sum</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>355.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>348.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>https://www.thairath.co.th/news/local/north/18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2356</th>\n",
              "      <td>776.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>639.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>590.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>https://www.thairath.co.th/news/society/1796313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2213</th>\n",
              "      <td>472.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>422.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>385.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>https://www.thairath.co.th/entertain/news/1836624</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9317093-b02d-4dd4-a791-19b954a282ab')\"\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-f9317093-b02d-4dd4-a791-19b954a282ab 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-f9317093-b02d-4dd4-a791-19b954a282ab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      th_body  ...                                                url\n",
              "77      355.0  ...  https://www.thairath.co.th/news/local/north/18...\n",
              "2356    776.0  ...    https://www.thairath.co.th/news/society/1796313\n",
              "2213    472.0  ...  https://www.thairath.co.th/entertain/news/1836624\n",
              "\n",
              "[3 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(3, 1, figsize=(10, 12), sharex=True)\n",
        "\n",
        "axes[0].set_title('th_body')\n",
        "sns.kdeplot(train_th_body, shade=True, ax=axes[0], label=\"Train\")\n",
        "axes[0].axvline(np.average(train_th_body), color='r', linestyle='--')\n",
        "axes[0].text(np.average(train_th_body) + 5, 0.0025, f'avg. {int(np.average(train_th_body))} words')\n",
        "\n",
        "axes[1].set_title('en_body')\n",
        "sns.kdeplot(train_en_body, shade=True, ax=axes[1])\n",
        "axes[1].axvline(np.average(train_en_body), color='r', linestyle='--')\n",
        "axes[1].text(np.average(train_en_body) + 5, 0.0025, f'avg. {int(np.average(train_en_body))} words')\n",
        "\n",
        "axes[2].set_title('zh_body')\n",
        "sns.kdeplot(train_zh_body, shade=True, ax=axes[2])\n",
        "axes[2].axvline(np.average(train_zh_body), color='r', linestyle='--')\n",
        "axes[2].text(np.average(train_zh_body) + 5, 0.0025, f'avg. {int(np.average(train_zh_body))} words')\n",
        "\n",
        "plt.xlabel(\"Word Count\")\n",
        "plt.xlim(0,1200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "BN2wMEL-7W-L",
        "outputId": "6b2fff7e-f7b9-4991-bf43-c6a2344c6ab0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1200.0)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x864 with 3 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2) Word count distribution of `{th/en/zh}_sum`"
      ],
      "metadata": {
        "id": "gNnKAZp1dqyM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(3, 1, figsize=(10, 12), sharex=True)\n",
        "\n",
        "axes[0].set_title('th_sum')\n",
        "sns.kdeplot(train_th_sum, shade=True, ax=axes[0])\n",
        "axes[0].axvline(np.average(train_th_sum), color='r', linestyle='--')\n",
        "axes[0].text(np.average(train_th_sum) + 1.5, 0.04, f'avg. {int(np.average(train_th_sum))} words')\n",
        "\n",
        "axes[1].set_title('en_sum')\n",
        "sns.kdeplot(train_en_sum, shade=True, ax=axes[1])\n",
        "axes[1].axvline(np.average(train_en_sum), color='r', linestyle='--')\n",
        "axes[1].text(np.average(train_en_sum) + 1.5, 0.03, f'avg. {int(np.average(train_en_sum))} words')\n",
        "\n",
        "axes[2].set_title('zh_sum')\n",
        "sns.kdeplot(train_zh_sum, shade=True, ax=axes[2])\n",
        "axes[2].axvline(np.average(train_zh_sum), color='r', linestyle='--')\n",
        "axes[2].text(np.average(train_zh_sum) + 1.5, 0.035, f'avg. {int(np.average(train_zh_sum))} words')\n",
        "\n",
        "plt.xlabel(\"Word Count\")\n",
        "plt.xlim(0,150)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "-0_NYn30a2uy",
        "outputId": "57995ddb-7b9b-42d6-f3e1-e9aef1584ac4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 150.0)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x864 with 3 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3) Word Counts of Test and Validation Sets"
      ],
      "metadata": {
        "id": "DAbNxiagiTMP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from prettytable import PrettyTable\n",
        "\n",
        "x = PrettyTable()\n",
        "x.field_names = ['', 'Validation', 'Test']\n",
        "x.add_row(['th_body', int(validation_wc_df['th_body'].mean()), int(test_wc_df['th_body'].mean())])\n",
        "x.add_row(['en_body', int(validation_wc_df['en_body'].mean()), int(test_wc_df['en_body'].mean())])\n",
        "x.add_row(['zh_body', int(validation_wc_df['zh_body'].mean()), int(test_wc_df['zh_body'].mean())])\n",
        "x.add_row(['th_sum', int(validation_wc_df['th_sum'].mean()), int(test_wc_df['th_sum'].mean())])\n",
        "x.add_row(['en_sum', int(validation_wc_df['en_sum'].mean()), int(test_wc_df['en_sum'].mean())])\n",
        "x.add_row(['zh_sum', int(validation_wc_df['zh_sum'].mean()), int(test_wc_df['zh_sum'].mean())])\n",
        "x.add_row(['en_gg_sum', 'N/A', int(test_wc_df['en_gg_sum'].mean())])\n",
        "x.add_row(['zh_gg_sum', 'N/A', int(test_wc_df['zh_gg_sum'].mean())])\n",
        "x.add_row(['th_title', 'N/A', int(test_wc_df['th_title'].mean())])\n",
        "x.add_row(['en_title', 'N/A', int(test_wc_df['en_title'].mean())])\n",
        "x.add_row(['zh_title', 'N/A', int(test_wc_df['zh_title'].mean())])\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "id": "tO3rx5Fik9XS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e407f9c-8a42-4e2e-fc64-9ac9da835c0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+------------+------+\n",
            "|           | Validation | Test |\n",
            "+-----------+------------+------+\n",
            "|  th_body  |    555     | 554  |\n",
            "|  en_body  |    517     | 513  |\n",
            "|  zh_body  |    460     | 457  |\n",
            "|   th_sum  |     34     |  34  |\n",
            "|   en_sum  |     37     |  37  |\n",
            "|   zh_sum  |     34     |  34  |\n",
            "| en_gg_sum |    N/A     |  40  |\n",
            "| zh_gg_sum |    N/A     |  36  |\n",
            "|  th_title |    N/A     |  15  |\n",
            "|  en_title |    N/A     |  19  |\n",
            "|  zh_title |    N/A     |  17  |\n",
            "+-----------+------------+------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3) Translation Length Difference between Our Main Translation Service VS. Google Translation \n",
        "\n",
        "Google Translation generally generated longer translation than the main translation service that we used."
      ],
      "metadata": {
        "id": "mGJ12dBW3iHU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### TH2EN"
      ],
      "metadata": {
        "id": "NNbA01I69iQP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=[10, 5])\n",
        "ax = sns.distplot(test_wc_df['en_gg_sum'], label='Google Translation')\n",
        "ax = sns.distplot(test_wc_df['en_sum'], label='Main Translation Service')\n",
        "plt.xlabel('Word Counts')\n",
        "plt.legend() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "NpOksNmV7AMx",
        "outputId": "00e81637-57d9-4438-ae8a-bb19a3673232"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe34d4fc490>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### TH2ZH"
      ],
      "metadata": {
        "id": "F1RUB3Pe9m7e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=[10, 5])\n",
        "ax = sns.distplot(test_wc_df['zh_gg_sum'], label='Google Translation')\n",
        "ax = sns.distplot(test_wc_df['zh_sum'], label='Main Translation Service')\n",
        "plt.xlabel('Word Counts')\n",
        "plt.legend() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "YZQUt5Zl9mj3",
        "outputId": "8a423cd5-6ebe-4f7a-8174-e15521d4f474"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe34d4fcb50>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}