text_summarization_BERT_ROUGE.ipynb

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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.6/106.6 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.6/67.6 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m411.2/411.2 kB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.9/138.9 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.7/49.7 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.1/93.1 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m42.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers[sentencepiece] datasets sacrebleu rouge_score py7zr -q"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Library"
      ],
      "metadata": {
        "id": "CXMixEzbChcQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "dX2AZYlo7FKK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c9b9998-e98d-4229-8871-c8a563f38710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import os\n",
        "import torch\n",
        "import rouge\n",
        "\n",
        "from rouge import Rouge\n",
        "from transformers import BertTokenizer, EncoderDecoderModel\n",
        "from datasets import load_dataset, load_metric\n",
        "from transformers import pipeline, set_seed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R86CdFnDCZd1",
        "outputId": "87c50b20-b81f-4291-95d7-958994d3e41c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "LlbD4yQRUySI",
        "outputId": "5ff26cff-aa99-4842-f78f-1821e8a7da2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Dataset"
      ],
      "metadata": {
        "id": "Q4RgewwcCw2y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = os.getcwd()\n",
        "project_path = 'drive/MyDrive/IndonesiaAI/TextSummarization/'\n",
        "full_project_path = os.path.join(path, project_path)\n",
        "dataset_folder = 'dataset'\n",
        "model_folder = 'models'\n",
        "dataset_path = os.path.join(full_project_path, dataset_folder)\n",
        "model_path = os.path.join(full_project_path, model_folder)"
      ],
      "metadata": {
        "id": "DXdEp1Fvp4HF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(os.path.join(dataset_path, 'df_test_prep.csv'), index_col=0) # path nya masih local"
      ],
      "metadata": {
        "id": "YGO1tO7eDPDf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "-SoDQQHLDjef",
        "outputId": "271700a9-ecc9-4536-f057-a47416f115e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id                                                url  \\\n",
              "1  13020  https://www.liputan6.com/news/read/13020/bi-di...   \n",
              "3  13024  https://www.liputan6.com/news/read/13024/perub...   \n",
              "4  13025  https://www.liputan6.com/news/read/13025/puluh...   \n",
              "6  13027  https://www.liputan6.com/news/read/13027/ruu-p...   \n",
              "8  13030  https://www.liputan6.com/news/read/13030/pusat...   \n",
              "\n",
              "                                       clean_article  \\\n",
              "1  Liputan6.com, Jakarta: Bank Indonesia dinilai ...   \n",
              "3  Liputan6.com, Jakarta: Penghapusan beberapa pa...   \n",
              "4  Liputan6.com, Jakarta: Operasi Sadar Jaya yang...   \n",
              "6  Liputan6.com, Jakarta: Sejumlah pasal dalam Ra...   \n",
              "8  Liputan6.com, Bantul: Pusat desa kerajinan ata...   \n",
              "\n",
              "                                       clean_summary  \\\n",
              "1  Kendati Bank Sentral AS menurunkan suku bungan...   \n",
              "3  Revisi Kepmennaker Nomor 78 Tahun 2001, dinila...   \n",
              "4  Polisi menangkap 32 pengunjung Diskotik Mileni...   \n",
              "6  Praktisi penyiaran menyoroti sejumlah pasal da...   \n",
              "8  Karena ditinggalkan para perajin, kondisi Desa...   \n",
              "\n",
              "                                  extractive_summary  \\\n",
              "1  Liputan6.com, Jakarta: Bank Indonesia dinilai ...   \n",
              "3  Liputan6.com, Jakarta: Penghapusan beberapa pa...   \n",
              "4  Dari operasi tersebut, polisi menangkap 32 pen...   \n",
              "6  RUU tersebut dipandang tak berpihak pada kepen...   \n",
              "8  Sejumlah benda antik yang dianggap menjadi cik...   \n",
              "\n",
              "                                  clean_article_prep  \\\n",
              "1  bank indonesia dinilai masih akan menghadapi s...   \n",
              "3  penghapusan beberapa pasal menyangkut hak buru...   \n",
              "4  operasi sadar jaya yang dilancarkan selasa mal...   \n",
              "6  sejumlah pasal dalam rancangan undang undang p...   \n",
              "8  bantul pusat desa kerajinan atau pasar kriya d...   \n",
              "\n",
              "                                  clean_summary_prep  \n",
              "1  kendati bank sentral as menurunkan suku bungan...  \n",
              "3  revisi kepmennaker nomor 78 tahun 2001 dinilai...  \n",
              "4  polisi menangkap 32 pengunjung diskotik mileni...  \n",
              "6  praktisi penyiaran menyoroti sejumlah pasal da...  \n",
              "8  karena ditinggalkan para perajin kondisi desa ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a50919e2-7214-4685-bdce-7ae4092b9e00\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>url</th>\n",
              "      <th>clean_article</th>\n",
              "      <th>clean_summary</th>\n",
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              "      <td>13020</td>\n",
              "      <td>https://www.liputan6.com/news/read/13020/bi-di...</td>\n",
              "      <td>Liputan6.com, Jakarta: Bank Indonesia dinilai ...</td>\n",
              "      <td>Kendati Bank Sentral AS menurunkan suku bungan...</td>\n",
              "      <td>Liputan6.com, Jakarta: Bank Indonesia dinilai ...</td>\n",
              "      <td>bank indonesia dinilai masih akan menghadapi s...</td>\n",
              "      <td>kendati bank sentral as menurunkan suku bungan...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13024</td>\n",
              "      <td>https://www.liputan6.com/news/read/13024/perub...</td>\n",
              "      <td>Liputan6.com, Jakarta: Penghapusan beberapa pa...</td>\n",
              "      <td>Revisi Kepmennaker Nomor 78 Tahun 2001, dinila...</td>\n",
              "      <td>Liputan6.com, Jakarta: Penghapusan beberapa pa...</td>\n",
              "      <td>penghapusan beberapa pasal menyangkut hak buru...</td>\n",
              "      <td>revisi kepmennaker nomor 78 tahun 2001 dinilai...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13025</td>\n",
              "      <td>https://www.liputan6.com/news/read/13025/puluh...</td>\n",
              "      <td>Liputan6.com, Jakarta: Operasi Sadar Jaya yang...</td>\n",
              "      <td>Polisi menangkap 32 pengunjung Diskotik Mileni...</td>\n",
              "      <td>Dari operasi tersebut, polisi menangkap 32 pen...</td>\n",
              "      <td>operasi sadar jaya yang dilancarkan selasa mal...</td>\n",
              "      <td>polisi menangkap 32 pengunjung diskotik mileni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>13027</td>\n",
              "      <td>https://www.liputan6.com/news/read/13027/ruu-p...</td>\n",
              "      <td>Liputan6.com, Jakarta: Sejumlah pasal dalam Ra...</td>\n",
              "      <td>Praktisi penyiaran menyoroti sejumlah pasal da...</td>\n",
              "      <td>RUU tersebut dipandang tak berpihak pada kepen...</td>\n",
              "      <td>sejumlah pasal dalam rancangan undang undang p...</td>\n",
              "      <td>praktisi penyiaran menyoroti sejumlah pasal da...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>13030</td>\n",
              "      <td>https://www.liputan6.com/news/read/13030/pusat...</td>\n",
              "      <td>Liputan6.com, Bantul: Pusat desa kerajinan ata...</td>\n",
              "      <td>Karena ditinggalkan para perajin, kondisi Desa...</td>\n",
              "      <td>Sejumlah benda antik yang dianggap menjadi cik...</td>\n",
              "      <td>bantul pusat desa kerajinan atau pasar kriya d...</td>\n",
              "      <td>karena ditinggalkan para perajin kondisi desa ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a50919e2-7214-4685-bdce-7ae4092b9e00')\"\n",
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              "  </svg>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a50919e2-7214-4685-bdce-7ae4092b9e00 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-a50919e2-7214-4685-bdce-7ae4092b9e00');\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",
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              "\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7929ad8a-8929-4b3a-9f7e-fdeb7c7403c3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 3862,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3807,\n        \"min\": 13020,\n        \"max\": 26403,\n        \"num_unique_values\": 3862,\n        \"samples\": [\n          21320,\n          25212,\n          24827\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"url\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3862,\n        \"samples\": [\n          \"https://www.liputan6.com/news/read/21320/penggunaan-dana-pengungsi-maluku-tak-jelas\",\n          \"https://www.liputan6.com/news/read/25212/kecelakaan-bus-karena-pengemudi-lengah\",\n          \"https://www.liputan6.com/news/read/24827/jalur-kereta-api-cikampek-normal-kembali\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clean_article\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3862,\n        \"samples\": [\n          \"Liputan6.com, Ambon: Sebagian besar dana kemanusiaan untuk pengungsi di Maluku belum jelas penggunaanya. Buktinya, hingga kini, kehidupan pengungsi di daerah tersebut sangat memprihatinkan. Padahal, tak sedikit dana yang dikucurkan pemerintah pusat untuk keperluan mereka. Dana lauk pauk mereka saja yang disalurkan melalui Dinas Sosial mencapai Rp 12 miliar. Demikian diungkapkan Gubernur Maluku Saleh Latuconsina, di Ambon, baru-baru ini. Menanggapi hal itu, Lembaga Pengkajian dan Pengawasan Pemerintahan dan Albertas, sebuah lembaga swadaya masyarakat di Maluku mendesak pemerintah pusat mengaudit penggunaan dana pengungsi. Tujuannya, untuk mengetahui peruntukan dana tersebut. Sementara itu, berdasarkan pemantauan SCTV di Ambon, sejumlah rumah yang dibangun untuk para pengungsi tak bisa ditempati lantaran berada di kawasan rawan konflik. Data Satkorlak Maluku juga menyebutkan jumlah pegungsi di Maluku, kini mencapai 329. 818 jiwa. (AWD/Sahlan Heluth).\",\n          \"Liputan6.com, Subang: Tabrakan Bus Terang Jaya dengan truk yang menewaskan 11 orang di daerah Kerajan Barat, Ciasem, Jawa Barat, Senin pagi tadi, dipastikan karena human error. Karena itu, sopir bus Tulus Prasetyo yang sempat melarikan diri, tengah diperiksa di Kepolisian Resor Subang, Jabar. \\\"Statusnya tersangka,\\\" kata Ajun Komisaris Besar Polisi Bambang Sugeng di Subang, Senin (10/12). Berdasarkan pengakuan sejumlah saksi dan korban yang selamat, tabrakan berawal dari bus yang menyalip sebuah kendaraan. Akibatnya kondisi kendaraan bernomor polisi B 7022 WC tak stabil. Pada saat itu, dari arah berlawanan sebuah truk melaju dengan kecepatan tinggi. Bus yang tengah oleng itu membentur truk. Sementara itu, sebuah sepeda motor yang berdekatan dengan dua kendaraan tersebut terkena bantingan bus hingga terpental. Pengemudi sepeda motor malang itu tewas seketika. [Baca: Sebelas Orang Tewas dalam Kecelakaan Bus]. Berdasarkan data tersebut, menurut Bambang, kecelakaan murni kesalahan pengemudi yang ugal-ugalan. Karena itu, dia mengimbau agar sopir bus, khususnya angkutan Lebaran lebih berhati-hati. (DEN/Tim Liputan6 SCTV).\",\n          \"Liputan6.com, Cikampek: Lima rangkaian kereta api dari arah timur dan arah Jakarta melintas ruas Stasiun Tanjung Rasa-Cikampek, Jawa Barat, Selasa (4/12) sejak pukul 07. 00 WIB hingga siang. Tapi kereta harus memperlambat laju perjalanannya karena bantalan dan rel yang renggang akibat ledakan bom rakitan [baca: Bom Meledak di Rel KA Cikampek], sedang diperbaiki petugas PT Kereta Api Indonesia. Kepolisian Wilayah Purwakarta sejak Senin malam sudah mengidentifikasi sumber ledakan dan jenis bahan peledak. Tapi mereka belum bisa mengumumkannya kepada khalayak. Sejauh ini polisi sudah memerika 24 orang saksi. Sebagian besar dari mereka adalah pekerja pembangunan rel yang tinggal tak jauh dari tempat kejadian perkara. Belum satu tersangka ditetapkan polisi. (COK/Apriliana dan Dwiguntoro).\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clean_summary\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3860,\n        \"samples\": [\n          \"Polisi telah memanggil sembilan orang saksi untuk dimintai keterangan seputar insiden penembakan terhadap Wakil Bendahara GP Ansor Aris Junaidi. Polisi mengawasi dengan ketat Aris Junaidi.\",\n          \"Penyelundupan ribuan ton crude palm oil ke luar negeri berhasil dihentikan. Markas Pangkalan Utama TNI Angkatan Laut Belawan menahan nakhoda dan belasan anak buah kapal dalam kasus tersebut.\",\n          \"Forum Buruh Bersatu Sumsel menilai RUU Penyelesaian Perselisihan Industri lebih memihak kepada pemodal dan mendukung kapitalisme dunia. Pemerintah diminta mencabut RUU tersebut.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"extractive_summary\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3860,\n        \"samples\": [\n          \"Hingga kini, polisi telah memeriksa sembilan orang saksi, termasuk istri Aris. Sementara itu, kini Aris Junaidi dalam perlindungan polisi.\",\n          \"Liputan6.com, Medan: Awak Kapal Republik Indonesia (KRI) Teuku Umar berhasil menggagalkan penyelundupan crude palm oil (CPO) sebanyak 3. 400 ton ke luar negeri. Untuk pemeriksaan lebih lanjut, nahkoda beserta 13 anak buah kapal di tahan di Markas Pangkalan Utama TNI AL (Lantamal) Belawan, Medan.\",\n          \"Buruh Sumsel menolak RUU PPI karena lebih memihak kepada pemodal. Forum Buruh Bersatu mengimbau masyarakat menggelar protes menolak RUU tersebut.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clean_article_prep\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3862,\n        \"samples\": [\n          \"ambon sebagian besar dana kemanusiaan untuk pengungsi di maluku belum jelas penggunaanya buktinya hingga kini kehidupan pengungsi di daerah tersebut sangat memprihatinkan padahal tak sedikit dana yang dikucurkan pemerintah pusat untuk keperluan mereka dana lauk pauk mereka saja yang disalurkan melalui dinas sosial mencapai rp 12 miliar demikian diungkapkan gubernur maluku saleh latuconsina di ambon baru baru ini menanggapi hal itu lembaga pengkajian dan pengawasan pemerintahan dan albertas sebuah lembaga swadaya masyarakat di maluku mendesak pemerintah pusat mengaudit penggunaan dana pengungsi tujuannya untuk mengetahui peruntukan dana tersebut sementara itu berdasarkan pemantauan sctv di ambon sejumlah rumah yang dibangun untuk para pengungsi tak bisa ditempati lantaran berada di kawasan rawan konflik data satkorlak maluku juga menyebutkan jumlah pegungsi di maluku kini mencapai 329818 jiwa\",\n          \"subang tabrakan bus terang jaya dengan truk yang menewaskan 11 orang di daerah kerajan barat ciasem jawa barat senin pagi tadi dipastikan karena human error karena itu sopir bus tulus prasetyo yang sempat melarikan diri tengah diperiksa di kepolisian resor subang jabar statusnya tersangka kata ajun komisaris besar polisi bambang sugeng di subang senin berdasarkan pengakuan sejumlah saksi dan korban yang selamat tabrakan berawal dari bus yang menyalip sebuah kendaraan akibatnya kondisi kendaraan bernomor polisi 7022 wc tak stabil pada saat itu dari arah berlawanan sebuah truk melaju dengan kecepatan tinggi bus yang tengah oleng itu membentur truk sementara itu sebuah sepeda motor yang berdekatan dengan dua kendaraan tersebut terkena bantingan bus hingga terpental pengemudi sepeda motor malang itu tewas seketika baca sebelas orang tewas dalam kecelakaan bus berdasarkan data tersebut menurut bambang kecelakaan murni kesalahan pengemudi yang ugal ugalan karena itu dia mengimbau agar sopir bus khususnya angkutan lebaran lebih berhati hati\",\n          \"cikampek lima rangkaian kereta api dari arah timur dan arah jakarta melintas ruas stasiun tanjung rasa cikampek jawa barat selasa sejak pukul 0700 wib hingga siang tapi kereta harus memperlambat laju perjalanannya karena bantalan dan rel yang renggang akibat ledakan bom rakitan baca bom meledak di rel ka cikampek sedang diperbaiki petugas pt kereta api indonesia kepolisian wilayah purwakarta sejak senin malam sudah mengidentifikasi sumber ledakan dan jenis bahan peledak tapi mereka belum bisa mengumumkannya kepada khalayak sejauh ini polisi sudah memerika 24 orang saksi sebagian besar dari mereka adalah pekerja pembangunan rel yang tinggal tak jauh dari tempat kejadian perkara belum satu tersangka ditetapkan polisi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clean_summary_prep\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3860,\n        \"samples\": [\n          \"polisi telah memanggil sembilan orang saksi untuk dimintai keterangan seputar insiden penembakan terhadap wakil bendahara gp ansor aris junaidi polisi mengawasi dengan ketat aris junaidi\",\n          \"penyelundupan ribuan ton crude palm oil ke luar negeri berhasil dihentikan markas pangkalan utama tni angkatan laut belawan menahan nakhoda dan belasan anak buah kapal dalam kasus tersebut\",\n          \"forum buruh bersatu sumsel menilai ruu penyelesaian perselisihan industri lebih memihak kepada pemodal dan mendukung kapitalisme dunia pemerintah diminta mencabut ruu tersebut\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.drop(columns=['url','extractive_summary','clean_article', 'clean_summary'], axis=1, inplace = True)\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "dwMagLllEZ-2",
        "outputId": "4777fb43-8993-493c-d782-438b6fa5fbf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id                                 clean_article_prep  \\\n",
              "1  13020  bank indonesia dinilai masih akan menghadapi s...   \n",
              "3  13024  penghapusan beberapa pasal menyangkut hak buru...   \n",
              "4  13025  operasi sadar jaya yang dilancarkan selasa mal...   \n",
              "6  13027  sejumlah pasal dalam rancangan undang undang p...   \n",
              "8  13030  bantul pusat desa kerajinan atau pasar kriya d...   \n",
              "\n",
              "                                  clean_summary_prep  \n",
              "1  kendati bank sentral as menurunkan suku bungan...  \n",
              "3  revisi kepmennaker nomor 78 tahun 2001 dinilai...  \n",
              "4  polisi menangkap 32 pengunjung diskotik mileni...  \n",
              "6  praktisi penyiaran menyoroti sejumlah pasal da...  \n",
              "8  karena ditinggalkan para perajin kondisi desa ...  "
            ],
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              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 3862,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3807,\n        \"min\": 13020,\n        \"max\": 26403,\n        \"num_unique_values\": 3862,\n        \"samples\": [\n          21320,\n          25212,\n          24827\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clean_article_prep\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3862,\n        \"samples\": [\n          \"ambon sebagian besar dana kemanusiaan untuk pengungsi di maluku belum jelas penggunaanya buktinya hingga kini kehidupan pengungsi di daerah tersebut sangat memprihatinkan padahal tak sedikit dana yang dikucurkan pemerintah pusat untuk keperluan mereka dana lauk pauk mereka saja yang disalurkan melalui dinas sosial mencapai rp 12 miliar demikian diungkapkan gubernur maluku saleh latuconsina di ambon baru baru ini menanggapi hal itu lembaga pengkajian dan pengawasan pemerintahan dan albertas sebuah lembaga swadaya masyarakat di maluku mendesak pemerintah pusat mengaudit penggunaan dana pengungsi tujuannya untuk mengetahui peruntukan dana tersebut sementara itu berdasarkan pemantauan sctv di ambon sejumlah rumah yang dibangun untuk para pengungsi tak bisa ditempati lantaran berada di kawasan rawan konflik data satkorlak maluku juga menyebutkan jumlah pegungsi di maluku kini mencapai 329818 jiwa\",\n          \"subang tabrakan bus terang jaya dengan truk yang menewaskan 11 orang di daerah kerajan barat ciasem jawa barat senin pagi tadi dipastikan karena human error karena itu sopir bus tulus prasetyo yang sempat melarikan diri tengah diperiksa di kepolisian resor subang jabar statusnya tersangka kata ajun komisaris besar polisi bambang sugeng di subang senin berdasarkan pengakuan sejumlah saksi dan korban yang selamat tabrakan berawal dari bus yang menyalip sebuah kendaraan akibatnya kondisi kendaraan bernomor polisi 7022 wc tak stabil pada saat itu dari arah berlawanan sebuah truk melaju dengan kecepatan tinggi bus yang tengah oleng itu membentur truk sementara itu sebuah sepeda motor yang berdekatan dengan dua kendaraan tersebut terkena bantingan bus hingga terpental pengemudi sepeda motor malang itu tewas seketika baca sebelas orang tewas dalam kecelakaan bus berdasarkan data tersebut menurut bambang kecelakaan murni kesalahan pengemudi yang ugal ugalan karena itu dia mengimbau agar sopir bus khususnya angkutan lebaran lebih berhati hati\",\n          \"cikampek lima rangkaian kereta api dari arah timur dan arah jakarta melintas ruas stasiun tanjung rasa cikampek jawa barat selasa sejak pukul 0700 wib hingga siang tapi kereta harus memperlambat laju perjalanannya karena bantalan dan rel yang renggang akibat ledakan bom rakitan baca bom meledak di rel ka cikampek sedang diperbaiki petugas pt kereta api indonesia kepolisian wilayah purwakarta sejak senin malam sudah mengidentifikasi sumber ledakan dan jenis bahan peledak tapi mereka belum bisa mengumumkannya kepada khalayak sejauh ini polisi sudah memerika 24 orang saksi sebagian besar dari mereka adalah pekerja pembangunan rel yang tinggal tak jauh dari tempat kejadian perkara belum satu tersangka ditetapkan polisi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clean_summary_prep\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3860,\n        \"samples\": [\n          \"polisi telah memanggil sembilan orang saksi untuk dimintai keterangan seputar insiden penembakan terhadap wakil bendahara gp ansor aris junaidi polisi mengawasi dengan ketat aris junaidi\",\n          \"penyelundupan ribuan ton crude palm oil ke luar negeri berhasil dihentikan markas pangkalan utama tni angkatan laut belawan menahan nakhoda dan belasan anak buah kapal dalam kasus tersebut\",\n          \"forum buruh bersatu sumsel menilai ruu penyelesaian perselisihan industri lebih memihak kepada pemodal dan mendukung kapitalisme dunia pemerintah diminta mencabut ruu tersebut\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install Dataset -q"
      ],
      "metadata": {
        "id": "CUc1k4USFPbu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# rubah pandas csv menjadi dataframe\n",
        "from datasets import Dataset\n",
        "\n",
        "dataset = Dataset.from_pandas(dataset)"
      ],
      "metadata": {
        "id": "5CMKefyeFHuU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0g9Uo1aKyUv",
        "outputId": "2e79d7a4-b3a7-49c4-d9f8-730084336b78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'clean_article_prep', 'clean_summary_prep', '__index_level_0__'],\n",
              "    num_rows: 3862\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'clean_article_prep' is the correct column name\n",
        "sample_text = dataset['clean_article_prep'][0]  # This might not work as expected with `datasets.Dataset`\n",
        "print(sample_text)  # Check what this prints to debug\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JRiZl7nLMwKO",
        "outputId": "7d67ab04-eabc-4b89-e98d-2a421212e694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bank indonesia dinilai masih akan menghadapi situasi sulit kendati bank sentral amerika serikat terus menurunkan tingkat suku bunga yang dimiliki penilaian itu dikemukakan pengamat ekonomi didiek rachbini di jakarta baru baru ini menurut perhitungan didiek dalam tahun ini the fed telah lima kali menurunkan nilai suku bunga yang mereka miliki bahkan didiek memperkirakan tingkat suku bunga the fed akan diturunkan hingga menjadi empat persen dengan keadaan itu tambah didiek di atas kertas dapat dimanfaatkan bi untuk meningkatkan suku bunga bi sebagai upaya mempertahankan nilai tukar rupiah namun demikian didiek pesimistis hal itu akan tercapai mengingat kondisi bangsa masih carut marut jika keadaan terus seperti ini tak tertutup kemungkinan bi akan tetap memberlakukan nilai suku bunga tinggi ujar didiek sementara itu the fed terpaksa menurunkan tingkat suku bunga karena pertembuhan ekonomi di negeri paman sam terus melemah padahal selama ini as menjadi pasar ekspor penting untuk indonesia\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model\n",
        "\n",
        "model-model yg akan di bandingkan:\n",
        "\n",
        "\n",
        "*   Alfahluzi/bert2bert-dropout-0.3-lr-5e-05-ds-canonical\n",
        "*   cahya/bert2bert-indonesian-summarization\n",
        "*   Alfahluzi/bert2bert-extreme\n",
        "*   model_batch_4_lr_1e-5\n",
        "*   model_batch_4_lr_5e-5\n",
        "*   model_batch_2_lr_1e-5\n",
        "*   model_batch_2_lr_5e-5\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Aqz5jwQ4CpKz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Helper Function to Calculate Rouge"
      ],
      "metadata": {
        "id": "QS75gedEdK8t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rouge"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_vn8I4kQddz",
        "outputId": "3409eb61-eb3e-4b30-f39b-13d46d132aba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rouge\n",
            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from rouge) (1.16.0)\n",
            "Installing collected packages: rouge\n",
            "Successfully installed rouge-1.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_mean_rouge(dataframe, model_name, sample_size=None):\n",
        "    \"\"\"\n",
        "    Calculate aggregated ROUGE scores for predictions made by a BERT model.\n",
        "\n",
        "    Args:\n",
        "    - dataframe (DataFrame): DataFrame containing clean_article_prep (X data) and clean_summary_prep (Y data).\n",
        "    - model_name (str): Name of the BERT model.\n",
        "\n",
        "    Returns:\n",
        "    - DataFrame: DataFrame containing model name, aggregated ROUGE1, ROUGE2, ROUGEL, and ROUGELsum scores.\n",
        "    \"\"\"\n",
        "    if sample_size==None:\n",
        "      dataframe\n",
        "    else:\n",
        "      dataframe = dataframe.sample(sample_size, random_state=42)\n",
        "\n",
        "    # Initialize ROUGE calculator\n",
        "    rouge = Rouge()\n",
        "\n",
        "    tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "    model = EncoderDecoderModel.from_pretrained(model_name)\n",
        "\n",
        "    # Initialize lists to store ROUGE scores\n",
        "    rouge1_scores = []\n",
        "    rouge2_scores = []\n",
        "    rougeL_scores = []\n",
        "    rougeLsum_scores = []\n",
        "\n",
        "    # Iterate over rows of the DataFrame\n",
        "    for index, row in dataframe.iterrows():\n",
        "        # Get X and Y values from the DataFrame\n",
        "        x_text = row['clean_article_prep']\n",
        "        y_text = row['clean_summary_prep']\n",
        "\n",
        "        # Generate prediction for X data using the BERT model\n",
        "        # Tokenize the input text\n",
        "        inputs = tokenizer(x_text, padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n",
        "        input_ids = inputs.input_ids\n",
        "        attention_mask = inputs.attention_mask\n",
        "\n",
        "        # Convert input_ids and attention_mask to PyTorch tensors\n",
        "        input_ids = torch.tensor(input_ids)\n",
        "        attention_mask = torch.tensor(attention_mask)\n",
        "\n",
        "        outputs = model.generate(\n",
        "            input_ids,\n",
        "            attention_mask=attention_mask,\n",
        "            min_length=20,\n",
        "            max_length=128,\n",
        "            num_beams=10,\n",
        "            repetition_penalty=2.5,\n",
        "            length_penalty=1.0,\n",
        "            early_stopping=True,\n",
        "            no_repeat_ngram_size=2,\n",
        "            use_cache=True,\n",
        "            do_sample = True,\n",
        "            temperature = 0.8,\n",
        "            top_k = 50,\n",
        "            top_p = 0.95)\n",
        "\n",
        "        # Decode the generated summaries\n",
        "        output_str = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "\n",
        "        # Calculate ROUGE scores\n",
        "        scores = rouge.get_scores(output_str, y_text)[0]\n",
        "\n",
        "        # Extract ROUGE scores\n",
        "        rouge1 = scores['rouge-1']['f']\n",
        "        rouge2 = scores['rouge-2']['f']\n",
        "        rougeL = scores['rouge-l']['f']\n",
        "        rougeLsum = (rouge1 + rouge2 + rougeL) / 3  # ROUGE-Lsum is the average of ROUGE-1, ROUGE-2, and ROUGE-L\n",
        "\n",
        "        # Append scores to respective lists\n",
        "        rouge1_scores.append(rouge1)\n",
        "        rouge2_scores.append(rouge2)\n",
        "        rougeL_scores.append(rougeL)\n",
        "        rougeLsum_scores.append(rougeLsum)\n",
        "\n",
        "    # Calculate mean scores\n",
        "    mean_rouge1 = sum(rouge1_scores) / len(rouge1_scores)\n",
        "    mean_rouge2 = sum(rouge2_scores) / len(rouge2_scores)\n",
        "    mean_rougeL = sum(rougeL_scores) / len(rougeL_scores)\n",
        "    mean_rougeLsum = sum(rougeLsum_scores) / len(rougeLsum_scores)\n",
        "\n",
        "    # Create DataFrame with aggregated scores\n",
        "    df = pd.DataFrame({\n",
        "        'Model Name': [model_name],\n",
        "        'Mean ROUGE1': [mean_rouge1],\n",
        "        'Mean ROUGE2': [mean_rouge2],\n",
        "        'Mean ROUGEL': [mean_rougeL],\n",
        "        'Mean ROUGELsum': [mean_rougeLsum]\n",
        "    })\n",
        "\n",
        "    return df"
      ],
      "metadata": {
        "id": "d9THWcgqw3ls"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mean ROUGE"
      ],
      "metadata": {
        "id": "-MPQp-W1KShU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## cahya/bert2bert-indonesian-summarization"
      ],
      "metadata": {
        "id": "kFhAh0kf59wO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rouge_1 = calculate_mean_rouge(dataset, \"cahya/bert2bert-indonesian-summarization\", 50)\n",
        "rouge_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "Pi5uuDg_ylck",
        "outputId": "e89cbd1d-0030-47f8-8e94-11fdde80fa4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-53-ebfdb3a50ddc>:132: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-53-ebfdb3a50ddc>:133: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Model Name  Mean ROUGE1  Mean ROUGE2  \\\n",
              "0  cahya/bert2bert-indonesian-summarization 0.3121267801 0.1118384747   \n",
              "\n",
              "   Mean ROUGEL  Mean ROUGELsum  \n",
              "0 0.2527776395    0.2255809648  "
            ],
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    {
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      "source": [
        "## Alfahluzi/bert2bert-extreme"
      ],
      "metadata": {
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        "rouge_2"
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            "<ipython-input-53-ebfdb3a50ddc>:132: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-53-ebfdb3a50ddc>:133: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n"
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              "                    Model Name  Mean ROUGE1  Mean ROUGE2  Mean ROUGEL  \\\n",
              "0  Alfahluzi/bert2bert-extreme 0.0656776834 0.0000000000 0.0498435744   \n",
              "\n",
              "   Mean ROUGELsum  \n",
              "0    0.0385070859  "
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              "        const dataTable =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
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            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Alfahluzi/bert2bert-dropout-0.3-lr-5e-05-ds-canonical"
      ],
      "metadata": {
        "id": "pj9FrnQK9lYT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rouge_3 = calculate_mean_rouge(dataset, \"Alfahluzi/bert2bert-dropout-0.3-lr-5e-05-ds-canonical\", 50)\n",
        "rouge_3"
      ],
      "metadata": {
        "id": "CMoqy5iv9vtO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90529333-d2eb-4640-98f2-2bf7082edd43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-57-811135e0310a>:26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-57-811135e0310a>:26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-57-811135e0310a>:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n",
            "<ipython-input-57-811135e0310a>:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n",
            "<ipython-input-57-811135e0310a>:26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-57-811135e0310a>:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n",
            "<ipython-input-57-811135e0310a>:26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-57-811135e0310a>:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n",
            "<ipython-input-57-811135e0310a>:26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-57-811135e0310a>:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n",
            "<ipython-input-57-811135e0310a>:26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-57-811135e0310a>:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n",
            "<ipython-input-57-811135e0310a>:26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-57-811135e0310a>:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n",
            "<ipython-input-57-811135e0310a>:26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-57-811135e0310a>:27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## model_batch_4_lr_1e-5"
      ],
      "metadata": {
        "id": "KLjOID6zSDYT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rouge_4 = calculate_mean_rouge(dataset, os.path.join(model_path, 'model_batch_4_lr_1e-5'), 50)\n",
        "rouge_4"
      ],
      "metadata": {
        "id": "KuvPB9JyTvEi",
        "colab": {
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          "height": 239
        },
        "outputId": "57d306bd-d168-4f64-f999-4e43d1f16304"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-ebfdb3a50ddc>:132: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-13-ebfdb3a50ddc>:133: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n",
            "<ipython-input-13-ebfdb3a50ddc>:132: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-13-ebfdb3a50ddc>:133: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "                                          Model Name  Mean ROUGE1  \\\n",
              "0  /content/drive/MyDrive/IndonesiaAI/TextSummari...     0.320739   \n",
              "\n",
              "   Mean ROUGE2  Mean ROUGEL  Mean ROUGELsum  \n",
              "0     0.117325     0.245667         0.22791  "
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              "      <th>Mean ROUGE2</th>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d429225c-30c7-44f2-b9c0-7924121777f1');\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",
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              "\n",
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              "summary": "{\n  \"name\": \"rouge_4\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Model Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"/content/drive/MyDrive/IndonesiaAI/TextSummarization/models/model_batch_4_lr_1e-5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean ROUGE1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.32073893774107154,\n        \"max\": 0.32073893774107154,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.32073893774107154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean ROUGE2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.11732490145136035,\n        \"max\": 0.11732490145136035,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.11732490145136035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean ROUGEL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.24566721219216134,\n        \"max\": 0.24566721219216134,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.24566721219216134\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean ROUGELsum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.22791035046153105,\n        \"max\": 0.22791035046153105,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.22791035046153105\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## model_batch_4_lr_5e-5"
      ],
      "metadata": {
        "id": "zimnS8WlSM1Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rouge_5 = calculate_mean_rouge(dataset, os.path.join(model_path, 'model_batch_4_lr_5e-5'), 50)\n",
        "rouge_5"
      ],
      "metadata": {
        "id": "LbMJSOZkd-Lt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
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      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "<ipython-input-13-ebfdb3a50ddc>:132: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-13-ebfdb3a50ddc>:133: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          Model Name  Mean ROUGE1  \\\n",
              "0  /content/drive/MyDrive/IndonesiaAI/TextSummari...     0.333851   \n",
              "\n",
              "   Mean ROUGE2  Mean ROUGEL  Mean ROUGELsum  \n",
              "0     0.120635     0.251822        0.235436  "
            ],
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              "      <th>Model Name</th>\n",
              "      <th>Mean ROUGE1</th>\n",
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              "\n",
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              "\n",
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              "\n",
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              "      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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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-23dd1c0c-45a9-41bd-95f8-c965d9fddfaa 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-23dd1c0c-45a9-41bd-95f8-c965d9fddfaa');\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",
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              "\n",
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              "  </div>\n"
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              "variable_name": "rouge_5",
              "summary": "{\n  \"name\": \"rouge_5\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Model Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"/content/drive/MyDrive/IndonesiaAI/TextSummarization/models/model_batch_4_lr_5e-5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean ROUGE1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.33385094689518835,\n        \"max\": 0.33385094689518835,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.33385094689518835\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean ROUGE2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.12063507223020559,\n        \"max\": 0.12063507223020559,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.12063507223020559\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean ROUGEL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.25182237572921673,\n        \"max\": 0.25182237572921673,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.25182237572921673\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean ROUGELsum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.23543613161820356,\n        \"max\": 0.23543613161820356,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.23543613161820356\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## model_batch_2_lr_1e-5"
      ],
      "metadata": {
        "id": "lGBUTjFr7wmK"
      }
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    {
      "cell_type": "code",
      "source": [
        "rouge_6 = calculate_mean_rouge(dataset, os.path.join(model_path, 'model_batch_2_lr_1e-5'), 50)\n",
        "rouge_6"
      ],
      "metadata": {
        "id": "TAAluG_57upR",
        "colab": {
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          "height": 170
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        "outputId": "3535ef7e-ff6c-4f49-be52-5733f49725ee"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-ebfdb3a50ddc>:132: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-13-ebfdb3a50ddc>:133: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n"
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              "                                          Model Name  Mean ROUGE1  \\\n",
              "0  /content/drive/MyDrive/IndonesiaAI/TextSummari...     0.342705   \n",
              "\n",
              "   Mean ROUGE2  Mean ROUGEL  Mean ROUGELsum  \n",
              "0     0.126596     0.262253        0.243851  "
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            }
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## model_batch_2_lr_5e-5"
      ],
      "metadata": {
        "id": "hTfqhwJ2703n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rouge_7 = calculate_mean_rouge(dataset, os.path.join(model_path, 'model_batch_2_lr_5e-5'), 50)\n",
        "rouge_7"
      ],
      "metadata": {
        "id": "4UgfRA7G76iX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "44518db5-e475-4514-b77a-0d8f62edc08e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-ebfdb3a50ddc>:132: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(input_ids)\n",
            "<ipython-input-13-ebfdb3a50ddc>:133: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(attention_mask)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          Model Name  Mean ROUGE1  \\\n",
              "0  /content/drive/MyDrive/IndonesiaAI/TextSummari...     0.328523   \n",
              "\n",
              "   Mean ROUGE2  Mean ROUGEL  Mean ROUGELsum  \n",
              "0     0.117366      0.25087        0.232253  "
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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}