T5-dataset-1.ipynb

{
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      "metadata": {
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        "id": "47Ru129KrfJn",
        "outputId": "6c29af59-8d02-442f-9194-b48b8202debf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Usage:   \n",
            "  pip3 install [options] <requirement specifier> [package-index-options] ...\n",
            "  pip3 install [options] -r <requirements file> [package-index-options] ...\n",
            "  pip3 install [options] [-e] <vcs project url> ...\n",
            "  pip3 install [options] [-e] <local project path> ...\n",
            "  pip3 install [options] <archive url/path> ...\n",
            "\n",
            "-f option requires 1 argument\n",
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          ]
        }
      ],
      "source": [
        "!pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio==0.8.2 -f\n",
        "!pip install datasets\n",
        "!pip install transformers\n",
        "!pip install rouge\n",
        "!pip install accelerate -U"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JpMPXc3M5n2R"
      },
      "outputs": [],
      "source": [
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J5WR-wjI5qFw"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hx26girQrnjg"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer, DataCollatorForSeq2Seq, AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xPBRrtq0r3I8"
      },
      "source": [
        "loading dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KW-wTwnprw_9"
      },
      "outputs": [],
      "source": [
        "# Load the \"ms2_sparse_max\" dataset\n",
        "ms2_dataset = load_dataset(\"medical_meadow_cord19\", split=\"test\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2kn2KPrzsCyY",
        "outputId": "630ee79a-b0a0-47a5-dbd2-bc9e1550ba55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     review_id                                               pmid  \\\n",
              "0     24894943           [19632716, 20573926, 23724913, 21903745]   \n",
              "1     25367179  [19784660, 2390908, 16703429, 15496622, 179367...   \n",
              "2     27841442  [9550512, 12920254, 15929503, 19321521, 151942...   \n",
              "3     28764600  [3527357, 4679833, 23842842, 4226213, 19250152...   \n",
              "4     26262909  [15592289, 11110329, 10907623, 18768245, 13750...   \n",
              "...        ...                                                ...   \n",
              "1662  23881744  [16263773, 9831573, 109596, 16615955, 19573436...   \n",
              "1663  28780119            [23402113, 19428896, 3196245, 16595307]   \n",
              "1664  29724255  [3355143, 5339014, 21548817, 27352492, 2062605...   \n",
              "1665  24756870  [12971705, 12920254, 11812553, 22544817, 33492...   \n",
              "1666  30151019  [23188543, 27219115, 5362830, 6041915, 2154881...   \n",
              "\n",
              "                                                  title  \\\n",
              "0     [Radiotherapy plus chemotherapy with or withou...   \n",
              "1     [Effects of preoperative chemoradiotherapy on ...   \n",
              "2     [Effect of a therapeutic intervention for the ...   \n",
              "3     [Internet-based psychoeducation for bipolar di...   \n",
              "4     [Sleep in late pregnancy predicts length of la...   \n",
              "...                                                 ...   \n",
              "1662  [Effect of intravenous amino acids on glutamin...   \n",
              "1663  [Efficacy of a Recombinant HVT-H5 Vaccine Agai...   \n",
              "1664  [Acupuncture for the Treatment of Dry Eye: A M...   \n",
              "1665  [A study on additional early physiotherapy aft...   \n",
              "1666  [A Phase II, Randomized, Controlled Trial of A...   \n",
              "\n",
              "                                               abstract target  \\\n",
              "0     [BACKGROUND Results from phase II studies in p...          \n",
              "1     [Purpose Deterioration of anorectal function a...          \n",
              "2     [BACKGROUND AND PURPOSE Arm function recovery ...          \n",
              "3     [Background In a recent exploratory r and omis...          \n",
              "4     [OBJECTIVE The purpose of this study was to te...          \n",
              "...                                                 ...    ...   \n",
              "1662  [Glutamine may be a conditionally essential am...          \n",
              "1663  [SUMMARY . The swift evolution rate of avian i...          \n",
              "1664  [Purpose To evaluate the effects of acupunctur...          \n",
              "1665  [Objective : To investigate whether additional...          \n",
              "1666  [Purpose Postoperative ileus ( POI ) prolongs ...          \n",
              "\n",
              "                                             background  \n",
              "0     INTRODUCTION / BACKGROUND An individual patien...  \n",
              "1     Background Low rectal cancer is conventionally...  \n",
              "2     BACKGROUND Repetitive task training ( RTT ) in...  \n",
              "3     There is a gap in knowledge of women 's percep...  \n",
              "4     BACKGROUND Leg cramps are a common problem in ...  \n",
              "...                                                 ...  \n",
              "1662  BACKGROUND Observational studies in preterm ne...  \n",
              "1663  Indonesia has implemented multiple strategies ...  \n",
              "1664  Abstract s Background In a former meta- analys...  \n",
              "1665  BACKGROUND Various approaches to physical reha...  \n",
              "1666  Delays in recovery of intestinal function foll...  \n",
              "\n",
              "[1667 rows x 6 columns]"
            ],
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              "      <th></th>\n",
              "      <th>review_id</th>\n",
              "      <th>pmid</th>\n",
              "      <th>title</th>\n",
              "      <th>abstract</th>\n",
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              "      <th>0</th>\n",
              "      <td>24894943</td>\n",
              "      <td>[19632716, 20573926, 23724913, 21903745]</td>\n",
              "      <td>[Radiotherapy plus chemotherapy with or withou...</td>\n",
              "      <td>[BACKGROUND Results from phase II studies in p...</td>\n",
              "      <td></td>\n",
              "      <td>INTRODUCTION / BACKGROUND An individual patien...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25367179</td>\n",
              "      <td>[19784660, 2390908, 16703429, 15496622, 179367...</td>\n",
              "      <td>[Effects of preoperative chemoradiotherapy on ...</td>\n",
              "      <td>[Purpose Deterioration of anorectal function a...</td>\n",
              "      <td></td>\n",
              "      <td>Background Low rectal cancer is conventionally...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>27841442</td>\n",
              "      <td>[9550512, 12920254, 15929503, 19321521, 151942...</td>\n",
              "      <td>[Effect of a therapeutic intervention for the ...</td>\n",
              "      <td>[BACKGROUND AND PURPOSE Arm function recovery ...</td>\n",
              "      <td></td>\n",
              "      <td>BACKGROUND Repetitive task training ( RTT ) in...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28764600</td>\n",
              "      <td>[3527357, 4679833, 23842842, 4226213, 19250152...</td>\n",
              "      <td>[Internet-based psychoeducation for bipolar di...</td>\n",
              "      <td>[Background In a recent exploratory r and omis...</td>\n",
              "      <td></td>\n",
              "      <td>There is a gap in knowledge of women 's percep...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>26262909</td>\n",
              "      <td>[15592289, 11110329, 10907623, 18768245, 13750...</td>\n",
              "      <td>[Sleep in late pregnancy predicts length of la...</td>\n",
              "      <td>[OBJECTIVE The purpose of this study was to te...</td>\n",
              "      <td></td>\n",
              "      <td>BACKGROUND Leg cramps are a common problem in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1662</th>\n",
              "      <td>23881744</td>\n",
              "      <td>[16263773, 9831573, 109596, 16615955, 19573436...</td>\n",
              "      <td>[Effect of intravenous amino acids on glutamin...</td>\n",
              "      <td>[Glutamine may be a conditionally essential am...</td>\n",
              "      <td></td>\n",
              "      <td>BACKGROUND Observational studies in preterm ne...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1663</th>\n",
              "      <td>28780119</td>\n",
              "      <td>[23402113, 19428896, 3196245, 16595307]</td>\n",
              "      <td>[Efficacy of a Recombinant HVT-H5 Vaccine Agai...</td>\n",
              "      <td>[SUMMARY . The swift evolution rate of avian i...</td>\n",
              "      <td></td>\n",
              "      <td>Indonesia has implemented multiple strategies ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1664</th>\n",
              "      <td>29724255</td>\n",
              "      <td>[3355143, 5339014, 21548817, 27352492, 2062605...</td>\n",
              "      <td>[Acupuncture for the Treatment of Dry Eye: A M...</td>\n",
              "      <td>[Purpose To evaluate the effects of acupunctur...</td>\n",
              "      <td></td>\n",
              "      <td>Abstract s Background In a former meta- analys...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1665</th>\n",
              "      <td>24756870</td>\n",
              "      <td>[12971705, 12920254, 11812553, 22544817, 33492...</td>\n",
              "      <td>[A study on additional early physiotherapy aft...</td>\n",
              "      <td>[Objective : To investigate whether additional...</td>\n",
              "      <td></td>\n",
              "      <td>BACKGROUND Various approaches to physical reha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1666</th>\n",
              "      <td>30151019</td>\n",
              "      <td>[23188543, 27219115, 5362830, 6041915, 2154881...</td>\n",
              "      <td>[A Phase II, Randomized, Controlled Trial of A...</td>\n",
              "      <td>[Purpose Postoperative ileus ( POI ) prolongs ...</td>\n",
              "      <td></td>\n",
              "      <td>Delays in recovery of intestinal function foll...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1667 rows × 6 columns</p>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-734ae437-14b7-480b-8f6c-73e50282fb05 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-734ae437-14b7-480b-8f6c-73e50282fb05');\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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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79d0e5e1-9fd8-4752-9c77-4408ac4457a1')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-79d0e5e1-9fd8-4752-9c77-4408ac4457a1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# @title Default title text\n",
        "ms2_dataset.to_pandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jfbi62sY4kg5"
      },
      "outputs": [],
      "source": [
        "# Access a specific table from the dataset (replace 'table_index' with the actual index)\n",
        "table_index = 0  # Replace with the index of the table you want to access\n",
        "table_data = ms2_dataset[table_index]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "keYR7Dyw4mGg"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LNERosf74rFQ"
      },
      "outputs": [],
      "source": [
        "# Convert the table data to a Pandas DataFrame\n",
        "df = pd.DataFrame(table_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RXZZVUgV6gHY"
      },
      "outputs": [],
      "source": [
        "from transformers import T5ForConditionalGeneration, T5Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WW1JeOG-bCM",
        "outputId": "0379bc88-5c11-415e-d5fb-95267830d350"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.99)\n"
          ]
        }
      ],
      "source": [
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240,
          "referenced_widgets": [
            "c55073bc02a84a3f991e2a616b1417f7",
            "3bb55b17fbf0417bb08a3912475798c9",
            "cb178c9b8cd045aa95b52d1dab8a071e",
            "28f72cce91c24fea9dee159b46be5090",
            "daf6fe0e950243959cfb2ec36ed51fab",
            "7c7d533eaa534a049ff20aad78741aa2",
            "7f85395e453843c6b1efc1ad9610cc30",
            "9bb7333a5dd644b29a035e44adab8e44",
            "772e7ffdca744466a9c19708134cee0e",
            "32fd52cc0f8e4a38abfaefc12b6d729e",
            "cde89642afdf46a89d1b961e0bbd7f46",
            "099187ac4515403bb2647c2217e3f1cb",
            "b49dd5a4bacd4665ae5cfd78962d709e",
            "d03714d35bb647bf8b0744b633bf2126",
            "475389c2d0a5426b9ee699980cbb5476",
            "365b5cb578cf47269f64f3c285183955",
            "1b4e23e4a6a24cc8bfbf6cc8e3e32bd1",
            "5a97e8a4a276420abe5143d6b71d8e08",
            "d6edddc874304eb8ac28c9d94cb7689c",
            "fdd82f6f52f84924afbcf71eb5fb4a4b",
            "9f8101e09ea64605948b9726aefe2aff",
            "f0a01a1a13c24f368b61069507b09678"
          ]
        },
        "id": "EFHU7I_K6hfY",
        "outputId": "e0c6bde7-928c-478a-bfaf-492e4e0c3f37"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c55073bc02a84a3f991e2a616b1417f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "099187ac4515403bb2647c2217e3f1cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5.py:240: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n",
            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "from transformers import T5Tokenizer\n",
        "\n",
        "model_name = \"t5-base\"\n",
        "model = T5ForConditionalGeneration.from_pretrained(model_name)\n",
        "tokenizer = T5Tokenizer.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S4gvXIBr_NAk"
      },
      "outputs": [],
      "source": [
        "def generate_summary(input_text):\n",
        "    input_ids = tokenizer.encode(\"summarize: \" + input_text, return_tensors=\"pt\", max_length=512, truncation=True)\n",
        "    summary_ids = model.generate(input_ids, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)\n",
        "    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)\n",
        "    return summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOY7KMUw_jmw",
        "outputId": "832856c7-8867-4a30-c861-6d6f7e2bf607"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'review_id': Value(dtype='string', id=None), 'pmid': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'title': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'abstract': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'target': Value(dtype='string', id=None), 'background': Value(dtype='string', id=None)}\n"
          ]
        }
      ],
      "source": [
        "print(ms2_dataset.features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QS5xi0CqhLIa"
      },
      "outputs": [],
      "source": [
        "summaries = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D0T7BKhy_WZB"
      },
      "outputs": [],
      "source": [
        "def process_item(item):\n",
        "    abstracts = item['abstract']\n",
        "    text_to_summarize = \" \".join(abstracts)\n",
        "    summary = generate_summary(text_to_summarize)\n",
        "    return summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UVzp1mWVA-Rx"
      },
      "outputs": [],
      "source": [
        "for i, summary in enumerate(summaries):\n",
        "    print(f\"Summary {i + 1}:\\n{summary}\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7KTPWXf-h3_C"
      },
      "outputs": [],
      "source": [
        "from IPython.display import display\n",
        "\n",
        "# Iterate through the summaries and display each one\n",
        "for i, summary in enumerate(summaries):\n",
        "    display(f\"Summary {i + 1}:\\n{summary}\\n\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5QKJv6gGNG3Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97eaaa29-fe79-4cab-e168-7a58efd17a23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rouge-score\n",
            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from rouge-score) (1.4.0)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from rouge-score) (3.8.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rouge-score) (1.23.5)\n",
            "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from rouge-score) (1.16.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score) (1.3.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score) (2023.6.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score) (4.66.1)\n",
            "Building wheels for collected packages: rouge-score\n",
            "  Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24932 sha256=e4c04320d004705b35c90df14dd7604c93a9520a0c3355209f24b6a051e3e920\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "Successfully built rouge-score\n",
            "Installing collected packages: rouge-score\n",
            "Successfully installed rouge-score-0.1.2\n"
          ]
        }
      ],
      "source": [
        "!pip install rouge-score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RLcdTrg7NMOy"
      },
      "outputs": [],
      "source": [
        "from rouge_score import rouge_scorer\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DOmJnjxDNT7m"
      },
      "outputs": [],
      "source": [
        "def calculate_rouge_scores(generated_summaries, reference_summaries):\n",
        "    scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)\n",
        "    rouge_scores = {\n",
        "        \"rouge1\": {\"f\": [], \"p\": [], \"r\": []},\n",
        "        \"rouge2\": {\"f\": [], \"p\": [], \"r\": []},\n",
        "        \"rougeL\": {\"f\": [], \"p\": [], \"r\": []}\n",
        "    }\n",
        "\n",
        "    for gen_summary, ref_summary in zip(generated_summaries, reference_summaries):\n",
        "        scores = scorer.score(gen_summary, ref_summary)\n",
        "        for rouge_type in rouge_scores.keys():\n",
        "            rouge_scores[rouge_type][\"f\"].append(scores[rouge_type].fmeasure)\n",
        "            rouge_scores[rouge_type][\"p\"].append(scores[rouge_type].precision)\n",
        "            rouge_scores[rouge_type][\"r\"].append(scores[rouge_type].recall)\n",
        "\n",
        "    return rouge_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n97mXwCwNYLr"
      },
      "outputs": [],
      "source": [
        "# Example usage\n",
        "generated_summaries = [\"Generated summary 1\", \"Generated summary 2\", \"Generated summary 3\"]\n",
        "reference_summaries = [\"Reference summary 1\", \"Reference summary 2\", \"Reference summary 3\"]\n",
        "\n",
        "rouge_scores = calculate_rouge_scores(generated_summaries, reference_summaries)\n",
        "\n",
        "average_rouge_scores = {}\n",
        "\n",
        "for rouge_type, metrics in rouge_scores.items():\n",
        "    average_rouge_scores[rouge_type] = {}\n",
        "    for metric, values in metrics.items():\n",
        "        avg_score = sum(values) / len(values)\n",
        "        average_rouge_scores[rouge_type][metric] = avg_score\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eghp0hddQYUv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f195dea-0ee5-446e-e98b-0648a61e9353"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average ROUGE Scores for ROUGE-1:\n",
            "f: 0.6667\n",
            "p: 0.6667\n",
            "r: 0.6667\n"
          ]
        }
      ],
      "source": [
        "print(\"Average ROUGE Scores for ROUGE-1:\")\n",
        "for metric, avg_score in average_rouge_scores[\"rouge1\"].items():\n",
        "    print(f\"{metric}: {avg_score:.4f}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XXOXhyVXQePo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "947140ad-d424-4234-9dde-75c6e2453b10"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Average ROUGE Scores for ROUGE-2:\n",
            "f: 0.5000\n",
            "p: 0.5000\n",
            "r: 0.5000\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nAverage ROUGE Scores for ROUGE-2:\")\n",
        "for metric, avg_score in average_rouge_scores[\"rouge2\"].items():\n",
        "    print(f\"{metric}: {avg_score:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uBwVM7X4Qgvr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a876505b-23b7-402d-c922-84c07030da9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Average ROUGE Scores for ROUGE-L:\n",
            "f: 0.6667\n",
            "p: 0.6667\n",
            "r: 0.6667\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nAverage ROUGE Scores for ROUGE-L:\")\n",
        "for metric, avg_score in average_rouge_scores[\"rougeL\"].items():\n",
        "    print(f\"{metric}: {avg_score:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AOWSVSTETFUH"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GjMA-GffTLPm"
      },
      "outputs": [],
      "source": [
        "# Create histograms for ROUGE-1, ROUGE-2, and ROUGE-L\n",
        "def create_histogram(scores, rouge_type):\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    plt.hist(scores, bins=20, alpha=0.5, color='blue', edgecolor='black')\n",
        "    plt.title(f\"Histogram of ROUGE-{rouge_type} Scores\")\n",
        "    plt.xlabel(f\"ROUGE-{rouge_type} Score\")\n",
        "    plt.ylabel(\"Frequency\")\n",
        "    plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n_YUnF32TQBE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "e66f3320-5999-4fdd-9688-98036862d5de"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create histograms for ROUGE-1\n",
        "rouge1_scores = [score for score in average_rouge_scores[\"rouge1\"].values()]\n",
        "create_histogram(rouge1_scores, \"1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EZ5tyjGbTS3_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "6f3272e4-6368-4a2c-dce9-b1e48ab0202b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create histograms for ROUGE-2\n",
        "rouge2_scores = [score for score in average_rouge_scores[\"rouge2\"].values()]\n",
        "create_histogram(rouge2_scores, \"2\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aw0jMQ-xTbp4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "6512197f-8a2d-4296-d6af-34e8d6b6a9ac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create histograms for ROUGE-L\n",
        "rougeL_scores = [score for score in average_rouge_scores[\"rougeL\"].values()]\n",
        "create_histogram(rougeL_scores, \"L\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A-Wgd7BET1SC"
      },
      "outputs": [],
      "source": [
        "# Combine the ROUGE scores for ROUGE-1, ROUGE-2, and ROUGE-L\n",
        "rouge1_scores = [score for score in average_rouge_scores[\"rouge1\"].values()]\n",
        "rouge2_scores = [score for score in average_rouge_scores[\"rouge2\"].values()]\n",
        "rougeL_scores = [score for score in average_rouge_scores[\"rougeL\"].values()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C13ZRkwOT5O2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "55fa6e4b-2838-4896-e1f0-15ffc1417e5a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a histogram with different colors for each metric\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.hist(rouge1_scores, bins=20, alpha=0.5, color='blue', label='ROUGE-1', edgecolor='black')\n",
        "plt.hist(rouge2_scores, bins=20, alpha=0.5, color='green', label='ROUGE-2', edgecolor='black')\n",
        "plt.hist(rougeL_scores, bins=20, alpha=0.5, color='red', label='ROUGE-L', edgecolor='black')\n",
        "\n",
        "plt.title(\"Histogram of ROUGE Scores\")\n",
        "plt.xlabel(\"ROUGE Score\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T1JUGb-1C90J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a11206b-c712-45a3-b939-6808824086f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.14.6)\n",
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            "Installing collected packages: bert-score\n",
            "Successfully installed bert-score-0.3.13\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install bert-score\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "ms2_dataset = load_dataset(\"allenai/ms2_sparse_max\", split=\"test\")\n"
      ],
      "metadata": {
        "id": "aVteuleTYbIZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from bert_score import score\n"
      ],
      "metadata": {
        "id": "jNh1eF1KYfHb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"allenai/ms2_sparse_max\", split=\"test\")\n"
      ],
      "metadata": {
        "id": "gicZ7NvfY3AU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for example in dataset:\n",
        "    print(example)\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Or0Vn4zmY0Lm",
        "outputId": "8fe0c93f-b109-4e86-f548-e0dbedb6ce44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'review_id': '24894943', 'pmid': ['19632716', '20573926', '23724913', '21903745'], 'title': ['Radiotherapy plus chemotherapy with or without surgical resection for stage III non-small-cell lung cancer: a phase III randomised controlled trial', 'Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR.', 'Crizotinib versus chemotherapy in advanced ALK-positive lung cancer.', 'Sequential vs. concurrent chemoradiation for stage III non-small cell lung cancer: randomized phase III trial RTOG 9410.'], 'abstract': ['BACKGROUND Results from phase II studies in patients with stage IIIA non-small-cell lung cancer with ipsilateral mediastinal nodal metastases ( N2 ) have shown the feasibility of resection after concurrent chemotherapy and radiotherapy with promising rates of survival . We therefore did this phase III trial to compare concurrent chemotherapy and radiotherapy followed by resection with st and ard concurrent chemotherapy and definitive radiotherapy without resection . METHODS Patients with stage T1 - 3pN2M0 non-small-cell lung cancer were r and omly assigned in a 1:1 ratio to  concurrent induction chemotherapy  ( two cycles of cisplatin [ 50 mg/m(2 ) on days 1 , 8 , 29 , and 36 ] and etoposide [ 50 mg/m(2 ) on days 1 - 5 and 29 - 33 ] ) plus radiotherapy ( 45 Gy ) in multiple academic and community hospitals . If no progression , patients in group 1 underwent resection and those in group 2 continued radiotherapy uninterrupted up to 61 Gy . Two additional cycles of cisplatin and etoposide were given in both groups . The primary endpoint was overall survival ( OS ) . Analysis was by intention to treat . This study is registered with Clinical Trials.gov , number NCT00002550 . FINDINGS 202 patients ( median age 59 years , range 31 - 77 ) were assigned to group 1 and 194 ( 61 years , 32 - 78 ) to group 2 .  Median OS was 23.6 months ( IQR 9.0-not reached ) in group 1 versus 22.2 months ( 9.4 - 52.7 ) in group 2 ( hazard ratio [ HR ] 0.87 [ 0.70 - 1.10 ] ; p=0.24 ) . Number of patients alive at 5 years was 37 ( point estimate 27 % ) in group 1 and 24 ( point estimate 20 % ) in group 2 ( odds ratio 0.63 [ 0.36 - 1.10 ] ; p=0.10 ) . With N0 status at thoracotomy , the median OS was 34.4 months ( IQR 15.7-not reached ; 19 [ point estimate 41 % ] patients alive at 5 years ) . Progression-free survival ( PFS ) was better in group 1 than in group 2 , median 12.8 months ( 5.3 - 42.2 ) vs 10.5 months ( 4.8 - 20.6 ) , HR 0.77 [ 0.62 - 0.96 ] ; p=0.017 ) ; the number of patients without disease progression at 5 years was 32 ( point estimate 22 % ) versus 13 ( point estimate 11 % ) , respectively . Neutropenia and oesophagitis were the main grade 3 or 4 toxicities associated with chemotherapy plus radiotherapy in group 1 ( 77 [ 38 % ] and 20 [ 10 % ] , respectively ) and group 2 ( 80 [ 41 % ] and 44 [ 23 % ] , respectively ) . In group 1 , 16 ( 8 % ) deaths were treatment related versus four ( 2 % ) in group 2 . In an exploratory analysis , OS was improved for patients who underwent lobectomy , but not pneumonectomy , versus chemotherapy plus radiotherapy . INTERPRETATION Chemotherapy plus radiotherapy with or without resection ( preferably lobectomy ) are options for patients with stage IIIA(N2 ) non-small-cell lung cancer . FUNDING National Cancer Institute , Canadian Cancer Society , and National Cancer Institute of Canada', 'BACKGROUND Non-small-cell lung cancer with sensitive mutations of the epidermal growth factor receptor ( EGFR ) is highly responsive to EGFR tyrosine kinase inhibitors such as gefitinib , but little is known about how its efficacy and safety profile compares with that of st and ard chemotherapy . METHODS We r and omly assigned 230 patients with metastatic , non-small-cell lung cancer and EGFR mutations who had not previously received chemotherapy to receive gefitinib or carboplatin-paclitaxel .  The primary end point was progression-free survival ; secondary end points included overall survival , response rate , and toxic effects . RESULTS In the planned interim analysis of data for the first 200 patients , progression-free survival was significantly longer in the gefitinib group than in the st and ard-chemotherapy group ( hazard ratio for death or disease progression with gefitinib , 0.36 ; P<0.001 ) , result ing in early termination of the study . The gefitinib group had a significantly longer median progression-free survival ( 10.8 months , vs. 5.4 months in the chemotherapy group ; hazard ratio , 0.30 ; 95 % confidence interval , 0.22 to 0.41 ; P<0.001 ) , as well as a higher response rate ( 73.7 % vs. 30.7 % , P<0.001 ) . The median overall survival was 30.5 months in the gefitinib group and 23.6 months in the chemotherapy group ( P=0.31 ) . The most common adverse events in the gefitinib group were rash ( 71.1 % ) and elevated aminotransferase levels ( 55.3 % ) , and in the chemotherapy group , neutropenia ( 77.0 % ) , anemia ( 64.6 % ) , appetite loss ( 56.6 % ) , and sensory neuropathy ( 54.9 % ) . One patient receiving gefitinib  died from interstitial lung disease . CONCLUSIONS First-line gefitinib for patients with advanced non-small-cell lung cancer who were selected on the basis of EGFR mutations improved progression-free survival , with acceptable toxicity , as compared with st and ard chemotherapy . ( UMIN-CTR number , C000000376 .', 'BACKGROUND In single-group studies , chromosomal rearrangements of the anaplastic lymphoma kinase gene ( ALK ) have been associated with marked clinical responses to crizotinib , an oral tyrosine kinase inhibitor targeting ALK . Whether crizotinib is superior to st and ard chemotherapy with respect to efficacy is unknown . METHODS We conducted a phase 3 , open-label trial comparing crizotinib with chemotherapy in 347 patients with locally advanced or metastatic ALK-positive lung cancer who had received one prior platinum-based regimen . Patients were r and omly assigned to receive oral treatment with crizotinib ( 250 mg ) twice daily or intravenous chemotherapy with either pemetrexed ( 500 mg per square meter of body-surface area ) or docetaxel ( 75 mg per square meter ) every 3 weeks . Patients in the chemotherapy group who had disease progression were permitted to cross over to crizotinib as part of a separate study . The primary end point was progression-free survival . RESULTS The median progression-free survival was 7.7 months in the crizotinib group and 3.0 months in the chemotherapy group ( hazard ratio for progression or death with crizotinib , 0.49 ; 95 % confidence interval [ CI ] , 0.37 to 0.64 ; P<0.001 ) . The response rates were 65 % ( 95 % CI , 58 to 72 ) with crizotinib , as compared with 20 % ( 95 % CI , 14 to 26 ) with chemotherapy ( P<0.001 ) . An interim analysis of overall survival showed no significant improvement with crizotinib as compared with chemotherapy ( hazard ratio for death in the crizotinib group , 1.02 ; 95 % CI , 0.68 to 1.54 ; P=0.54 ) . Common adverse events associated with crizotinib were visual disorder , gastrointestinal side effects , and elevated liver aminotransferase levels , whereas common adverse events with chemotherapy were fatigue , alopecia , and dyspnea . Patients reported greater reductions in symptoms of lung cancer and greater improvement in global quality of life with crizotinib than with chemotherapy . CONCLUSIONS Crizotinib is superior to st and ard chemotherapy in patients with previously treated , advanced non-small-cell lung cancer with ALK rearrangement . ( Funded by Pfizer ; Clinical Trials.gov number , NCT00932893 . )', 'BACKGROUND The combination of chemotherapy with thoracic radiotherapy ( TRT ) compared with TRT alone has been shown to confer a survival advantage for good performance status patients with stage III non-small cell lung cancer . However , it is not known whether sequential or concurrent delivery of these therapies is the optimal combination strategy . METHODS A total of 610 patients were r and omly assigned to two concurrent regimens and one sequential chemotherapy and TRT regimen in a three-arm phase III trial . The sequential arm included cisplatin at 100 mg/m2 on days 1 and 29 and vinblastine at 5 mg/m2 per week for 5 weeks with 63 Gy TRT delivered as once-daily fractions beginning on day 50 . Arm 2 used the same chemotherapy regimen as arm 1 with 63 Gy TRT delivered as once-daily fractions beginning on day 1 [ corrected ] . Arm 3 used cisplatin at 50 mg/m2 on days 1 , 8 , 29 , and 36 with oral etoposide at 50 mg twice daily for 10 weeks on days 1 , 2 , 5 , and 6 with 69.6 Gy delivered as 1.2 Gy twice-daily fractions beginning on day 1 . The primary endpoint was overall survival , and secondary endpoints included tumor response and time to tumor progression . Kaplan-Meier analyses were used to assess survival , and toxic effects were examined using the Wilcoxon rank sum test . All statistical tests were two-sided . RESULTS Median survival times were 14.6 , 17.0 , and 15.6 months for arms 1 - 3 , respectively . Five-year survival was statistically significantly higher for patients treated with the concurrent regimen with once-daily TRT compared with the sequential treatment ( 5-year survival : sequential , arm 1 , 10 % [ 20 patients ] , 95 % confidence interval [ CI ] = 7 % to 15 % ; concurrent , arm 2 , 16 % [ 31 patients ] , 95 % CI = 11 % to 22 % , P = .046 ; concurrent , arm 3 , 13 % [ 22 patients ] , 95 % CI = 9 % to 18 % ) . With a median follow-up time of 11 years , the rates of acute grade 3 - 5 nonhematologic toxic effects were higher with concurrent than sequential therapy , but late toxic effects were similar . CONCLUSION Concurrent delivery of cisplatin-based chemotherapy with TRT confers a long-term survival benefit compared with the sequential delivery of these therapies'], 'target': '', 'background': 'INTRODUCTION / BACKGROUND An individual patient data meta analysis was performed to determine clinical outcomes , and to propose a risk stratification system , related to the comprehensive treatment of patients with oligometastatic NSCLC .'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reference_sentences = []  # Populate this list with reference sentences\n",
        "\n",
        "# You can loop through your dataset and extract reference sentences from it\n",
        "for example in ms2_dataset:\n",
        "    reference_sentences.append(example['review_id'])\n"
      ],
      "metadata": {
        "id": "jgoIIL9YYlNh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hypotheses = [example['review_id'] for example in ms2_dataset]\n",
        "\n",
        "P, R, F1 = score(hypotheses, reference_sentences, lang='en', verbose=True)\n",
        "\n",
        "# P, R, and F1 will contain precision, recall, and F1 score values respectively for each example in your dataset.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345,
          "referenced_widgets": [
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            "de2074d936dc4c2b8d58c956d80f83df",
            "a0168496c9604f5abe4b426807b99f5a",
            "0ad5f7dcf9ef42769eefce9f2a68bfac",
            "9d9c5c63823b4fe7b1f48e9ae611d490",
            "a4c161d0187547729389f81b103be9cf",
            "154783ae64634899af9779e4c9f74181",
            "ea106311f55140a7a1c6a19e66228c26",
            "4699735db4c84765adc7f01465ca2c87",
            "2cbf5b2688cd41d78c9eb7fda493fca9",
            "331c9988ba624de0ab07cbb54341ce64",
            "9260c61ebfcb4dbe8269c048bbc0220d",
            "271c7ed0ae0548ac870f20035a9ffd8e",
            "9f0f07e888104637b4d9a6c7de225f5e",
            "3b3424e98b664208ac82b78accf8c1de",
            "bb13009301904a8f91f77d3cbc48d5a5",
            "d308c43e9f8442b78fbb09f0747987b6",
            "0654401a9adc436eb7e650f19b6cc140",
            "736f45d733dd42fa8bd1ec81b5357122",
            "a7b9b5bd1f5d40b88c900f6692d934e2",
            "df4d6c32309f43d4ab405160c692b808",
            "3ce5cc08c9b749d390c3b8ac331cf880",
            "ee09a2fc67bd4d80be0901058e6654bb"
          ]
        },
        "id": "ZZN_9HUyZOWt",
        "outputId": "e29d3c8b-83d3-4e1b-bc7e-c4fe0d9a3967"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25fbf9825a554fd08a67e6d7319fc617"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/1.42G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "cbb5f4b8da924f78be7a3581843a0d5c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "calculating scores...\n",
            "computing bert embedding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de2074d936dc4c2b8d58c956d80f83df"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "computing greedy matching.\n"
          ]
        },
        {
          "output_type": "display_data",
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              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "271c7ed0ae0548ac870f20035a9ffd8e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done in 96.88 seconds, 17.21 sentences/sec\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, example in enumerate(ms2_dataset):\n",
        "    print(f\"Example {i + 1} - Precision: {P[i]:.4f}, Recall: {R[i]:.4f}, F1 Score: {F1[i]:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bbx9crhRaJ4s",
        "outputId": "4bf39cb7-a9a8-44ab-d9ad-c8b4f47c3af3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Example 1 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 2 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 3 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 4 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 5 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 6 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 7 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 8 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 9 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 10 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 11 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 12 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 13 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 14 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 15 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 16 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 17 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 18 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 19 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 20 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 21 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 22 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 23 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 24 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 25 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 26 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 27 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 28 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 29 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 30 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 31 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 32 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 33 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 34 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 35 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 36 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 37 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 38 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 39 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 40 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 41 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 42 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 43 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 44 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 45 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 46 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 47 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 48 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 49 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 50 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 51 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 52 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 53 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 54 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 55 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 56 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 57 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 58 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 59 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 60 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 61 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 62 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 63 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 64 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 65 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 66 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 67 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 68 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 69 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 70 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 71 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 72 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 73 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 74 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 75 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 76 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 77 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 78 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 79 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 80 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 81 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 82 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 83 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 84 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 85 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 86 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 87 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 88 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 89 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 90 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 91 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 92 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 93 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 94 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 95 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 96 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 97 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 98 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 99 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 100 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 101 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 102 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 103 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 104 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 105 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 106 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 107 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 108 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 109 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 110 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 111 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 112 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 113 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 114 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 115 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 116 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 117 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 118 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 119 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 120 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 121 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 122 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 123 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 124 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 125 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 126 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 127 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 128 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 129 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 130 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 131 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 132 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 133 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 134 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 135 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 136 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 137 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 138 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 139 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 140 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 141 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 142 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 143 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 144 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 145 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 146 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 147 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 148 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 149 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 150 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 151 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 152 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 153 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 154 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 155 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 156 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 157 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 158 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 159 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 160 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 161 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 162 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 163 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 164 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 165 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 166 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 167 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 168 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 169 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 170 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 171 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 172 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 173 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 174 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 175 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 176 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 177 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 178 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 179 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 180 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 181 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 182 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 183 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 184 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 185 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 186 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 187 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 188 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 189 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 190 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 191 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 192 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 193 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 194 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 195 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 196 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 197 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 198 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 199 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 200 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 201 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 202 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 203 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 204 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 205 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 206 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 207 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 208 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 209 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 210 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 211 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 212 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 213 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 214 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 215 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 216 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 217 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 218 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 219 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 220 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 221 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 222 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 223 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 224 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 225 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 226 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 227 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 228 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 229 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 230 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 231 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 232 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 233 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 234 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 235 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 236 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 237 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 238 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 239 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 240 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 241 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 242 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 243 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 244 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 245 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 246 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 247 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 248 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 249 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 250 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 251 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 252 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 253 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 254 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 255 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 256 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 257 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 258 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 259 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 260 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 261 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 262 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 263 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 264 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 265 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 266 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 267 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 268 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 269 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 270 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 271 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 272 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 273 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 274 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 275 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 276 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 277 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 278 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 279 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 280 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 281 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 282 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 283 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 284 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 285 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 286 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 287 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 288 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 289 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 290 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 291 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 292 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 293 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 294 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 295 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 296 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 297 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 298 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 299 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 300 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 301 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 302 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 303 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 304 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 305 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 306 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 307 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 308 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 309 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 310 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 311 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 312 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 313 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 314 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 315 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 316 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 317 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 318 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 319 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 320 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 321 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 322 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 323 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 324 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 325 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 326 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 327 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 328 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 329 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 330 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 331 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 332 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 333 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 334 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 335 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 336 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 337 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 338 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 339 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 340 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 341 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 342 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 343 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 344 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 345 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 346 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 347 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 348 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 349 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 350 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 351 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 352 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 353 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 354 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 355 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 356 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 357 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 358 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 359 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 360 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 361 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 362 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 363 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 364 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 365 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 366 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 367 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 368 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 369 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 370 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 371 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 372 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 373 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 374 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 375 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 376 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 377 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 378 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 379 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 380 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 381 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 382 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 383 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 384 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 385 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 386 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 387 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 388 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 389 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 390 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 391 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 392 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 393 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 394 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 395 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 396 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 397 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 398 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 399 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 400 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 401 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 402 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 403 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 404 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 405 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 406 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 407 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 408 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 409 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 410 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 411 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 412 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 413 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 414 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 415 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 416 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 417 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 418 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 419 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 420 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 421 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 422 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 423 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 424 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 425 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 426 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 427 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 428 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 429 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 430 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 431 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 432 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 433 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 434 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 435 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 436 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 437 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 438 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 439 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 440 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 441 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 442 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 443 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 444 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 445 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 446 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 447 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 448 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 449 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 450 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 451 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 452 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 453 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 454 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 455 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 456 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 457 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 458 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 459 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 460 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 461 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 462 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 463 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 464 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 465 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 466 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 467 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 468 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 469 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 470 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 471 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 472 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 473 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 474 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 475 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 476 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 477 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 478 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 479 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 480 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 481 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 482 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 483 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 484 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 485 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 486 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 487 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 488 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 489 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 490 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 491 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 492 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 493 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 494 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 495 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 496 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 497 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 498 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 499 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 500 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 501 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 502 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 503 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 504 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 505 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 506 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 507 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 508 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 509 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 510 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 511 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 512 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 513 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 514 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 515 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 516 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 517 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 518 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 519 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 520 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 521 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 522 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 523 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 524 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 525 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 526 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 527 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 528 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 529 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 530 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 531 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 532 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 533 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 534 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 535 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 536 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 537 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 538 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 539 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 540 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 541 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 542 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 543 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 544 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 545 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 546 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 547 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 548 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 549 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 550 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 551 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 552 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 553 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 554 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 555 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 556 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 557 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 558 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 559 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 560 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 561 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 562 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 563 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 564 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 565 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 566 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 567 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 568 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 569 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 570 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 571 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 572 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 573 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 574 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 575 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 576 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 577 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 578 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 579 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 580 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 581 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 582 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 583 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 584 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 585 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 586 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 587 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 588 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 589 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 590 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 591 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 592 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 593 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 594 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 595 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 596 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 597 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 598 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 599 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 600 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 601 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 602 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 603 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 604 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 605 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 606 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 607 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 608 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 609 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 610 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 611 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 612 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 613 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 614 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 615 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 616 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 617 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 618 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 619 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 620 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 621 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 622 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 623 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 624 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 625 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 626 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 627 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 628 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 629 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 630 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 631 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 632 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 633 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 634 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 635 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 636 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 637 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 638 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 639 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 640 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 641 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 642 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 643 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 644 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 645 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 646 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 647 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 648 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 649 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 650 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 651 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 652 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 653 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 654 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 655 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 656 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 657 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 658 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 659 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 660 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 661 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 662 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 663 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 664 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 665 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 666 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 667 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 668 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 669 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 670 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 671 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 672 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 673 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 674 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 675 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 676 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 677 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 678 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 679 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 680 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 681 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 682 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 683 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 684 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 685 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 686 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 687 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 688 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 689 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 690 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 691 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 692 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 693 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 694 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 695 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 696 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 697 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 698 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 699 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 700 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 701 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 702 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 703 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 704 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 705 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 706 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 707 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 708 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 709 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 710 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 711 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 712 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 713 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 714 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 715 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 716 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 717 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 718 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 719 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 720 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 721 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 722 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 723 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 724 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 725 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 726 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 727 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 728 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 729 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 730 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 731 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 732 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 733 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 734 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 735 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 736 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 737 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 738 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 739 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 740 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 741 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 742 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 743 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 744 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 745 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 746 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 747 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 748 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 749 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 750 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 751 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 752 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 753 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 754 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 755 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 756 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 757 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 758 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 759 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 760 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 761 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 762 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 763 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 764 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 765 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 766 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 767 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 768 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 769 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 770 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 771 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 772 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 773 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 774 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 775 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 776 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 777 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 778 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 779 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 780 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 781 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 782 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 783 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 784 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 785 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 786 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 787 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 788 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 789 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 790 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 791 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 792 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 793 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 794 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 795 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 796 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 797 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 798 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 799 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 800 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 801 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 802 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 803 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 804 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 805 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 806 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 807 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 808 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 809 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 810 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 811 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 812 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 813 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 814 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 815 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 816 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 817 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 818 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 819 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 820 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 821 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 822 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 823 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 824 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 825 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 826 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 827 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 828 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 829 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 830 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 831 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 832 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 833 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 834 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 835 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 836 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 837 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 838 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 839 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 840 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 841 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 842 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 843 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 844 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 845 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 846 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 847 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 848 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 849 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 850 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 851 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 852 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 853 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 854 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 855 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 856 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 857 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 858 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 859 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 860 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 861 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 862 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 863 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 864 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 865 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 866 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 867 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 868 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 869 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 870 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 871 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 872 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 873 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 874 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 875 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 876 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 877 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 878 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 879 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 880 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 881 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 882 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 883 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 884 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 885 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 886 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 887 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 888 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 889 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 890 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 891 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 892 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 893 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 894 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 895 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 896 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 897 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 898 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 899 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 900 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 901 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 902 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 903 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 904 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 905 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 906 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 907 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 908 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 909 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 910 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 911 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 912 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 913 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 914 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 915 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 916 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 917 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 918 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 919 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 920 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 921 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 922 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 923 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 924 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 925 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 926 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 927 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 928 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 929 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 930 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 931 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 932 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 933 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 934 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 935 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 936 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 937 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 938 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 939 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 940 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 941 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 942 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 943 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 944 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 945 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 946 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 947 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 948 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 949 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 950 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 951 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 952 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 953 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 954 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 955 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 956 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 957 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 958 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 959 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 960 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 961 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 962 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 963 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 964 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 965 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 966 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 967 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 968 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 969 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 970 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 971 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 972 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 973 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 974 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 975 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 976 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 977 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 978 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 979 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 980 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 981 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 982 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 983 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 984 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 985 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 986 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 987 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 988 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 989 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 990 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 991 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 992 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 993 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 994 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 995 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 996 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 997 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 998 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 999 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1000 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1001 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1002 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1003 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1004 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1005 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1006 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1007 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1008 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1009 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1010 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1011 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1012 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1013 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1014 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1015 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1016 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1017 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1018 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1019 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1020 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1021 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1022 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1023 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1024 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1025 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1026 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1027 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1028 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1029 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1030 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1031 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1032 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1033 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1034 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1035 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1036 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1037 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1038 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1039 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1040 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1041 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1042 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1043 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1044 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1045 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1046 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1047 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1048 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1049 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1050 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1051 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1052 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1053 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1054 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1055 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1056 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1057 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1058 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1059 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1060 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1061 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1062 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1063 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1064 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1065 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1066 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1067 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1068 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1069 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1070 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1071 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1072 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1073 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1074 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1075 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1076 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1077 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1078 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1079 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1080 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1081 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1082 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1083 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1084 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1085 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1086 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1087 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1088 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1089 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1090 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1091 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1092 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1093 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1094 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1095 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1096 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1097 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1098 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1099 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1100 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1101 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1102 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1103 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1104 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1105 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1106 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1107 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1108 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1109 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1110 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1111 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1112 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1113 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1114 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1115 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1116 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1117 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1118 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1119 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1120 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1121 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1122 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1123 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1124 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1125 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1126 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1127 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1128 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1129 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1130 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1131 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1132 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1133 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1134 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1135 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1136 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1137 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1138 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1139 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1140 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1141 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1142 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1143 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1144 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1145 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1146 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1147 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1148 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1149 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1150 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1151 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1152 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1153 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1154 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1155 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1156 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1157 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1158 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1159 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1160 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1161 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1162 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1163 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1164 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1165 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1166 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1167 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1168 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1169 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1170 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1171 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1172 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1173 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1174 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1175 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1176 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1177 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1178 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1179 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1180 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1181 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1182 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1183 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1184 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1185 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1186 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1187 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1188 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1189 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1190 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1191 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1192 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1193 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1194 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1195 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1196 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1197 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1198 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1199 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1200 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1201 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1202 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1203 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1204 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1205 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1206 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1207 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1208 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1209 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1210 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1211 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1212 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1213 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1214 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1215 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1216 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1217 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1218 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1219 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1220 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1221 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1222 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1223 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1224 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1225 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1226 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1227 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1228 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1229 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1230 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1231 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1232 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1233 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1234 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1235 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1236 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1237 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1238 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1239 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1240 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1241 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1242 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1243 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1244 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1245 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1246 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1247 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1248 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1249 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1250 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1251 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1252 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1253 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1254 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1255 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1256 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1257 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1258 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1259 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1260 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1261 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1262 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1263 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1264 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1265 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1266 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1267 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1268 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1269 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1270 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1271 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1272 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1273 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1274 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1275 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1276 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1277 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1278 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1279 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1280 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1281 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1282 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1283 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1284 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1285 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1286 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1287 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1288 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1289 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1290 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1291 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1292 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1293 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1294 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1295 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1296 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1297 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1298 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1299 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1300 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1301 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1302 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1303 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1304 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1305 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1306 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1307 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1308 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1309 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1310 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1311 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1312 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1313 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1314 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1315 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1316 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1317 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1318 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1319 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1320 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1321 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1322 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1323 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1324 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1325 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1326 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1327 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1328 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1329 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1330 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1331 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1332 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1333 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1334 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1335 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1336 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1337 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1338 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1339 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1340 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1341 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1342 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1343 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1344 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1345 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1346 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1347 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1348 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1349 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1350 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1351 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1352 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1353 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1354 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1355 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1356 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1357 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1358 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1359 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1360 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1361 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1362 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1363 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1364 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1365 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1366 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1367 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1368 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1369 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1370 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1371 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1372 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1373 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1374 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1375 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1376 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1377 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1378 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1379 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1380 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1381 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1382 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1383 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1384 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1385 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1386 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1387 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1388 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1389 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1390 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1391 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1392 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1393 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1394 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1395 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1396 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1397 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1398 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1399 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1400 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1401 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1402 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1403 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1404 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1405 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1406 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1407 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1408 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1409 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1410 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1411 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1412 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1413 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1414 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1415 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1416 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1417 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1418 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1419 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1420 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1421 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1422 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1423 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1424 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1425 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1426 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1427 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1428 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1429 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1430 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1431 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1432 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1433 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1434 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1435 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1436 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1437 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1438 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1439 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1440 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1441 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1442 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1443 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1444 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1445 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1446 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1447 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1448 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1449 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1450 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1451 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1452 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1453 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1454 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1455 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1456 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1457 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1458 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1459 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1460 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1461 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1462 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1463 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1464 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1465 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1466 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1467 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1468 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1469 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1470 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1471 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1472 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1473 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1474 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1475 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1476 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1477 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1478 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1479 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1480 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1481 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1482 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1483 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1484 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1485 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1486 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1487 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1488 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1489 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1490 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1491 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1492 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1493 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1494 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1495 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1496 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1497 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1498 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1499 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1500 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1501 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1502 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1503 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1504 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1505 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1506 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1507 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1508 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1509 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1510 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1511 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1512 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1513 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1514 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1515 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1516 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1517 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1518 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1519 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1520 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1521 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1522 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1523 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1524 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1525 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1526 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1527 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1528 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1529 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1530 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1531 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1532 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1533 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1534 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1535 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1536 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1537 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1538 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1539 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1540 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1541 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1542 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1543 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1544 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1545 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1546 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1547 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1548 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1549 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1550 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1551 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1552 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1553 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1554 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1555 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1556 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1557 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1558 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1559 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1560 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1561 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1562 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1563 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1564 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1565 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1566 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1567 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1568 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1569 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1570 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1571 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1572 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1573 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1574 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1575 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1576 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1577 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1578 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1579 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1580 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1581 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1582 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1583 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1584 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1585 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1586 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1587 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1588 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1589 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1590 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1591 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1592 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1593 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1594 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1595 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1596 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1597 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1598 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1599 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1600 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1601 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1602 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1603 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1604 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1605 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1606 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1607 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1608 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1609 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1610 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1611 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1612 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1613 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1614 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1615 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1616 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1617 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1618 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1619 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1620 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1621 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1622 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1623 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1624 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1625 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1626 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1627 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1628 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1629 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1630 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1631 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1632 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1633 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1634 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1635 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1636 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1637 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1638 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1639 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1640 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1641 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1642 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1643 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1644 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1645 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1646 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1647 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1648 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1649 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1650 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1651 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1652 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1653 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1654 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1655 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1656 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1657 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1658 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1659 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1660 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1661 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1662 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1663 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1664 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1665 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1666 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n",
            "Example 1667 - Precision: 1.0000, Recall: 1.0000, F1 Score: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install transformers\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "No1wPsDbbKuV",
        "outputId": "1d530f6d-e346-4758-f8e1-185bc4baab10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.17.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.15,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.14.1)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, TrainingArguments"
      ],
      "metadata": {
        "id": "zQmc_NVfbNbN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = T5ForConditionalGeneration.from_pretrained(\"t5-small\")\n",
        "tokenizer = T5Tokenizer.from_pretrained(\"t5-small\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226,
          "referenced_widgets": [
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        },
        "id": "xWB8mmklbY2z",
        "outputId": "4b039ce6-6f19-42b1-e774-977481a31137"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            }
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              "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
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          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
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      "source": [
        "def preprocess_function(examples):\n",
        "    inputs = tokenizer(examples['input_text'], return_tensors='pt', padding=True, truncation=True)\n",
        "    targets = tokenizer(examples['target_text'], return_tensors='pt', padding=True, truncation=True)\n",
        "    return {\n",
        "        'input_ids': inputs['input_ids'],\n",
        "        'attention_mask': inputs['attention_mask'],\n",
        "        'labels': targets['input_ids'],\n",
        "    }"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "training_args = TrainingArguments(\n",
        "    per_device_train_batch_size=4,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    output_dir=\"./t5-fine-tuned-model\",\n",
        "    save_total_limit=5,\n",
        "    save_steps=500,\n",
        "    eval_steps=500,\n",
        "    num_train_epochs=3,\n",
        "    report_to=\"tensorboard\",\n",
        ")"
      ],
      "metadata": {
        "id": "9ZKljZGAbn8S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset"
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        "id": "StiUNCD0cAE4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    data_collator=preprocess_function,\n",
        ")"
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            "\u001b[0;31mNameError\u001b[0m: name 'train_dataset' is not defined"
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