Code/KPA_KeyPointGeneration.ipynb

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        "\n",
        "> **University of Pisa - M.Sc. Computer Science, Artificial Intelligence**  \n",
        "> **Human Language Technologies - a.a. 2021/22**\n",
        ">\n",
        "> *September, 2022*\n",
        ">\n",
        ">**Authors** \n",
        "- Irene Pisani *i.pisani1@studenti.unipi.it* (560104)\n",
        "- Alice Bergonzini *a.bergonzini1@studenti.unipi.it* (560680)\n",
        "\n",
        "\n",
        "###### **FINAL PROJECT on KEY POINT ANALYSIS (KPA)**\n",
        "# **Track 1: Key Point Generation**\n",
        "\n",
        "***Abstract.*** This work aims to describe simple approaches for solving *Key Point Matching* (KPM) and *Key Point Generation* (KPG) tracks proposed at Argument Mining 2021 in the context of the shared task on *Quantitative Summarization and Key Point Analysis* (KPA). \\\n",
        "The presented methods rely on the fine-tuning of some state-of-the-art pre-trained language models both for KPM and KPG subtasks. \\\n",
        "Regarding the KPM task all the models explored were validated using the Hold-Out validation technique and their results were compared to analyze their effectiveness within the task.  Leveraging DeBERTa pre-trained transformer, our best model yields to competitive performance since it achieved  on the test set a mAP Strict and mAP Relaxed score of, respectively, 0,7035 and 0,8857. \\\n",
        " For the KPG task, a simple baseline based on abstractive summarization approach was provided; our system takes advantage of the pre-trained Google mT5 transformer to generate several points that are finally properly selected."
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "## **Settings**\n",
        "\n",
        "- Define Colab GPU to use\n",
        "- Download TR, VL e TS set from offial IBM reporsitory\n",
        "- Install required tools\n",
        "- Import useful libraries\n",
        "- Load from check point Google mT5 tokenizer and Google mT5 seq2seq model"
      ],
      "metadata": {
        "id": "hl8HkGl63Km8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZpHMz8uBcjl",
        "outputId": "7160c58a-68c4-4217-ce6f-17cf1b8ac22e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Aug 27 17:38:11 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! git clone \"https://github.com/IBM/KPA_2021_shared_task\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6v9rEK6BdUT",
        "outputId": "5a290405-3a69-492c-bd04-30f596bb15fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'KPA_2021_shared_task'...\n",
            "remote: Enumerating objects: 44, done.\u001b[K\n",
            "remote: Counting objects: 100% (44/44), done.\u001b[K\n",
            "remote: Compressing objects: 100% (40/40), done.\u001b[K\n",
            "remote: Total 44 (delta 14), reused 26 (delta 4), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (44/44), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "!pip install datasets -q\n",
        "!pip install transformers -q\n",
        "!pip install sentencepiece -q\n",
        "!pip install rouge_score -q\n"
      ],
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      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 365 kB 8.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 120 kB 82.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 115 kB 76.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 73.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 72.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 4.7 MB 7.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 56.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 8.7 MB/s \n",
            "\u001b[?25h  Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import os\n",
        "\n",
        "import nltk\n",
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, default_data_collator, Seq2SeqTrainer, Seq2SeqTrainingArguments, default_data_collator\n",
        "from datasets import load_dataset, load_metric"
      ],
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        "id": "e5Wozup1BivP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')\n",
        "metric = load_metric('rouge')"
      ],
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          "height": 84,
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        "id": "8rbhoFGpGEBE",
        "outputId": "f9d55d1b-1573-4443-a127-85ff56155caa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.16k [00:00<?, ?B/s]"
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      "cell_type": "code",
      "source": [
        "# define device to use: GPU - cuda\n",
        "device = 'cuda' \n",
        "\n",
        "# load from checkpoint model and tokenizer \n",
        "tokenizer = AutoTokenizer.from_pretrained('google/mt5-small')       # import pre-trained MT5 tokenizer \n",
        "model = AutoModelForSeq2SeqLM.from_pretrained('google/mt5-small')   # import pre-trained MT5 model"
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          "output_type": "display_data",
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    {
      "cell_type": "markdown",
      "source": [
        "## **Dataset**\n",
        "\n",
        "- Read TR, VL, TS set\n",
        "- Prepare datasets for passing them to the model"
      ],
      "metadata": {
        "id": "Foq6h6nA4VGP"
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def load_kpa_data(gold_data_dir, subset):\n",
        "\n",
        "    # load arguments set, keypoint set and label set from given directory\n",
        "    arguments_file = os.path.join(gold_data_dir, f\"arguments_{subset}.csv\")\n",
        "    key_points_file = os.path.join(gold_data_dir, f\"key_points_{subset}.csv\")\n",
        "    labels_file = os.path.join(gold_data_dir, f\"labels_{subset}.csv\")\n",
        "\n",
        "    # read arguments set, keypoint set and label set in csv format as pandas dataframes\n",
        "    arguments_df = pd.read_csv(arguments_file)\n",
        "    key_points_df = pd.read_csv(key_points_file)\n",
        "    labels_file_df = pd.read_csv(labels_file)\n",
        "    \n",
        "    # label set will be ignored\n",
        "    # argument set anf keypoint set will be combined as follow\n",
        "\n",
        "    # define required list to store <arguments, keypoint> pairs informations\n",
        "    argument, keypoint, argument_id, keypoint_id, stance, topic = [], [], [],[], [], []\n",
        "\n",
        "    # for each argument and for each key point under the same topic and stance \n",
        "    # ---->  create a pair <arguments, keypoint> with all relative info (such as topic, stance, id, text, etc... )\n",
        "    for arg, arg_id,topic_arg,stance_arg  in zip(arguments_df['argument'],arguments_df['arg_id'],arguments_df['topic'],arguments_df['stance']):\n",
        "      for kp,kp_id,topic_kp,stance_kp in zip(key_points_df['key_point'],key_points_df['key_point_id'],key_points_df['topic'],key_points_df['stance']):\n",
        "        if (topic_arg == topic_kp and stance_arg == stance_kp):\n",
        "          \n",
        "          argument.append(arg)\n",
        "          argument_id.append(arg_id)\n",
        "          keypoint.append(kp)\n",
        "          keypoint_id.append(kp_id)\n",
        "          topic.append(topic_arg)\n",
        "          stance.append(stance_arg)\n",
        "\n",
        "    # use all the generated pair to create a final dataset (a dataframe)\n",
        "    dataset_df = pd.DataFrame({'arg_id':argument_id,\n",
        "                               'key_point_id':keypoint_id,\n",
        "                               'argument':argument,\n",
        "                               'keypoint':keypoint,\n",
        "                               'topic' : topic,\n",
        "                               'stance': stance})\n",
        "    # add a supplemntar column to store the concatenation of argument and topic\n",
        "    dataset_df[\"arg_topic\"] = dataset_df[\"argument\"] + \" \" + dataset_df[\"topic\"]\n",
        "\n",
        "    # return final dataset\n",
        "    return dataset_df, arguments_df, key_points_df, labels_file_df\n",
        "\n",
        "dataset_directory = \"/content/KPA_2021_shared_task/kpm_data\"  # directory for dataset used for training and validation set\n",
        "testset_directory = \"/content/KPA_2021_shared_task/test_data\" # directory for dataset used for testing set \n",
        "\n",
        "# get dataset for training, evaluation and test\n",
        "tr_data, _, _, _ = load_kpa_data(gold_data_dir = dataset_directory, subset = \"train\")\n",
        "vl_data, _, _, _ = load_kpa_data(gold_data_dir = dataset_directory, subset = \"dev\")\n",
        "ts_data, _, _, _ = load_kpa_data(gold_data_dir = testset_directory, subset=\"test\")\n",
        "\n",
        "# show TR, VL, TS set \n",
        "print(\"\\nTRAINING SET: \"+str(tr_data.shape))\n",
        "display(tr_data.head())\n",
        "print(\"\\nVALIDATION SET: \"+str(vl_data.shape))\n",
        "display(vl_data.head())\n",
        "print(\"\\nTEST SET: \"+str(ts_data.shape))\n",
        "display(ts_data.head())\n",
        "\n",
        "# save TR, VL, TS set in csv format\n",
        "dataset_path = \"/content/\"\n",
        "tr_data.to_csv(dataset_path + 'KPAgen_tr.csv')\n",
        "vl_data.to_csv(dataset_path + 'KPAgen_vl.csv')\n",
        "ts_data.to_csv(dataset_path + 'KPAgen_ts.csv')\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "id": "CLOBG2xeGedT",
        "outputId": "a4637374-5101-4e8a-8b92-fe336e4bc395"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "\n",
            "TRAINING SET: (24454, 7)\n"
          ]
        },
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              "    arg_id key_point_id                                           argument  \\\n",
              "0  arg_0_0       kp_0_0  `people reach their limit when it comes to the...   \n",
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              "    arg_id key_point_id                                           argument  \\\n",
              "0  arg_4_0       kp_4_0  having a school uniform can reduce bullying as...   \n",
              "1  arg_4_0       kp_4_1  having a school uniform can reduce bullying as...   \n",
              "2  arg_4_0       kp_4_2  having a school uniform can reduce bullying as...   \n",
              "3  arg_4_0       kp_4_3  having a school uniform can reduce bullying as...   \n",
              "4  arg_4_0       kp_4_4  having a school uniform can reduce bullying as...   \n",
              "\n",
              "                                            keypoint  \\\n",
              "0  Children can still express themselves using ot...   \n",
              "1                    School uniform reduces bullying   \n",
              "2  School uniforms encourage discipline or focus ...   \n",
              "3                        School uniforms saves costs   \n",
              "4   School uniforms create a sense of equality/unity   \n",
              "\n",
              "                                         topic  stance  \\\n",
              "0  We should abandon the use of school uniform      -1   \n",
              "1  We should abandon the use of school uniform      -1   \n",
              "2  We should abandon the use of school uniform      -1   \n",
              "3  We should abandon the use of school uniform      -1   \n",
              "4  We should abandon the use of school uniform      -1   \n",
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              "                                           arg_topic  \n",
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              "1  having a school uniform can reduce bullying as...  \n",
              "2  having a school uniform can reduce bullying as...  \n",
              "3  having a school uniform can reduce bullying as...  \n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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          "metadata": {}
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          "name": "stdout",
          "text": [
            "\n",
            "TEST SET: (3923, 7)\n"
          ]
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              "    arg_id key_point_id                                           argument  \\\n",
              "0  arg_0_0       kp_0_0  Routine child vaccinations isn't mandatory sin...   \n",
              "1  arg_0_0       kp_0_1  Routine child vaccinations isn't mandatory sin...   \n",
              "2  arg_0_0       kp_0_2  Routine child vaccinations isn't mandatory sin...   \n",
              "3  arg_0_0       kp_0_3  Routine child vaccinations isn't mandatory sin...   \n",
              "4  arg_0_1       kp_0_0  Routine child vaccinations should not be manda...   \n",
              "\n",
              "                                            keypoint  \\\n",
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              "1     Mandatory vaccination contradicts basic rights   \n",
              "2        The parents and not the state should decide   \n",
              "3  Routine child vaccinations are not necessary t...   \n",
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              "\n",
              "                                            topic  stance  \\\n",
              "0  Routine child vaccinations should be mandatory      -1   \n",
              "1  Routine child vaccinations should be mandatory      -1   \n",
              "2  Routine child vaccinations should be mandatory      -1   \n",
              "3  Routine child vaccinations should be mandatory      -1   \n",
              "4  Routine child vaccinations should be mandatory      -1   \n",
              "\n",
              "                                           arg_topic  \n",
              "0  Routine child vaccinations isn't mandatory sin...  \n",
              "1  Routine child vaccinations isn't mandatory sin...  \n",
              "2  Routine child vaccinations isn't mandatory sin...  \n",
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    {
      "cell_type": "code",
      "source": [
        "# get the column names for input/target\n",
        "dataset_columns = ('argument', 'keypoint')\n",
        "argument_column = dataset_columns[0]\n",
        "key_point_column = dataset_columns[1]\n",
        "\n",
        "# load the dataset previously saved\n",
        "dataset_path = \"/content/\"\n",
        "tr_dataset = load_dataset('csv', data_files = dataset_path + '/KPAgen_tr.csv')[\"train\"]\n",
        "vl_dataset = load_dataset('csv', data_files = dataset_path + '/KPAgen_vl.csv')[\"train\"]\n",
        "ts_dataset = load_dataset('csv', data_files = dataset_path + '/KPAgen_ts.csv')[\"train\"]\n",
        "\n",
        "# create a dictionary to wrap together TR, VL, TS set \n",
        "data = {'train': tr_dataset, 'validation': vl_dataset, 'test': ts_dataset}\n",
        "print(data)\n"
      ],
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      "execution_count": null,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration default-0e76fbb02310889f\n",
            "WARNING:datasets.builder:Reusing dataset csv (/root/.cache/huggingface/datasets/csv/default-0e76fbb02310889f/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a)\n"
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              "  0%|          | 0/1 [00:00<?, ?it/s]"
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            "WARNING:datasets.builder:Using custom data configuration default-0c8ec60eda2473a2\n",
            "WARNING:datasets.builder:Reusing dataset csv (/root/.cache/huggingface/datasets/csv/default-0c8ec60eda2473a2/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a)\n"
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              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration default-ed26533af00391d2\n",
            "WARNING:datasets.builder:Reusing dataset csv (/root/.cache/huggingface/datasets/csv/default-ed26533af00391d2/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a)\n"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'train': Dataset({\n",
            "    features: ['Unnamed: 0', 'arg_id', 'key_point_id', 'argument', 'keypoint', 'topic', 'stance', 'arg_topic'],\n",
            "    num_rows: 24454\n",
            "}), 'validation': Dataset({\n",
            "    features: ['Unnamed: 0', 'arg_id', 'key_point_id', 'argument', 'keypoint', 'topic', 'stance', 'arg_topic'],\n",
            "    num_rows: 4211\n",
            "}), 'test': Dataset({\n",
            "    features: ['Unnamed: 0', 'arg_id', 'key_point_id', 'argument', 'keypoint', 'topic', 'stance', 'arg_topic'],\n",
            "    num_rows: 3923\n",
            "})}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Utilities functions**\n",
        "\n",
        "- preprocessing: preprocess dataset with mT5 tokenizer\n",
        "- postprocessing: process data to compute required evaluation metric\n",
        "- compute metrics: compute rouge score to evaluate generated summaries"
      ],
      "metadata": {
        "id": "9GvR1SQc7ALO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_input_length = 300\n",
        "max_target_length = 60\n",
        "padding = \"max_length\" \n",
        "\n",
        "def preprocess_function(data_set):\n",
        "\n",
        "    inputs = data_set[argument_column]   # get input column\n",
        "    targets = data_set[key_point_column]  # get target column\n",
        "    \n",
        "    # add useful prefix to input, to tell the model which task has to perform\n",
        "    #prefix = \"summarize: \"\n",
        "    #inputs = [prefix + inp for inp in inputs]\n",
        "\n",
        "    # execute input tokenization\n",
        "    model_inputs = tokenizer(inputs, \n",
        "                             max_length = max_input_length,\n",
        "                             padding = padding,\n",
        "                             truncation = True)\n",
        "\n",
        "    # execute target tokenizatiion\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(targets, \n",
        "                           max_length = max_target_length,\n",
        "                           padding = padding,\n",
        "                           truncation =True)\n",
        "    labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "    \n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "\n",
        "def postprocess_text(preds, labels):\n",
        "    \n",
        "    # get predictions and labels and split them in different sentence\n",
        "    preds = [pred.strip() for pred in preds]\n",
        "    labels = [label.strip() for label in labels]\n",
        "    preds = [\"\\n\".join(nltk.sent_tokenize(pred)) for pred in preds]\n",
        "    labels = [\"\\n\".join(nltk.sent_tokenize(label)) for label in labels]\n",
        "\n",
        "    return preds, labels\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "\n",
        "    preds, labels = eval_preds\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "    # Decode generated summaries into text\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "    \n",
        "    # Replace -100 in the labels as we can't decode them\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    \n",
        "    # Decode reference summaries into text\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    # post-processing: ROUGE expects a newline after each sentence\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "    \n",
        "    # Compute ROUGE scores\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
        "    \n",
        "    # Extract the median scores\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    result = {k: round(v, 4) for k, v in result.items()}\n",
        "    return result\n",
        "\n"
      ],
      "metadata": {
        "id": "EV0mQVc7OLTO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **MT5 fine-tuning, evaluation, prediction: Hugging Face Trainer API**\n",
        "\n",
        "- model finetuning on TR set\n",
        "- model evaluation on VL set\n",
        "- get model prediction (generated summaries/keypoint) on TS set"
      ],
      "metadata": {
        "id": "d7fY35Qj8BDT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# apply preprocessing procedure on TR, VL e TS set\n",
        "train_dataset = tr_dataset.map(preprocess_function, batched=True)\n",
        "eval_dataset = vl_dataset.map(preprocess_function, batched=True)\n",
        "test_dataset = ts_dataset.map(preprocess_function,batched=True)\n",
        "\n",
        "# define datacollators objects to use for creating batches\n",
        "data_collator = default_data_collator\n",
        "\n",
        "# define training arguments\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir    = '/content/',\n",
        "    learning_rate = 1e-5,\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    num_train_epochs    = 3,\n",
        "    per_device_train_batch_size = 16,\n",
        "    per_device_eval_batch_size  = 16,\n",
        "    warmup_steps = 500,\n",
        "    weight_decay = 0.01,\n",
        "    predict_with_generate = True\n",
        ")\n",
        "\n",
        "# initialize Trainer object\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model = model,\n",
        "    args  = training_args,\n",
        "    train_dataset = train_dataset,\n",
        "    eval_dataset  = eval_dataset,\n",
        "    tokenizer     = tokenizer,\n",
        "    data_collator = data_collator,\n",
        "    compute_metrics = compute_metrics\n",
        ")\n",
        "\n",
        "model.cuda()      # pass model to GPU\n",
        "checkpoint = ''   # define checkpoint \n",
        "\n",
        "# runs fine-tuning and save fine-tuned model\n",
        "train_result = trainer.train(resume_from_checkpoint = None) \n",
        "trainer.save_model()\n",
        "\n",
        "# use model to predict new summary on test set \n",
        "test_results = trainer.predict(\n",
        "      test_dataset = test_dataset,\n",
        "      metric_key_prefix = \"test\",\n",
        "      max_length = max_target_length,\n",
        "      num_beams = 6)\n",
        "print(test_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gYnK8nXhA-E8",
        "outputId": "81334f28-6a36-4b1d-ddb9-f02e21613644"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-0e76fbb02310889f/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a/cache-4647a2979950ca8f.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-0c8ec60eda2473a2/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a/cache-df1601ea417277ab.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-ed26533af00391d2/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a/cache-6958e54a6963cead.arrow\n",
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "The following columns in the training set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: key_point_id, keypoint, argument, topic, stance, Unnamed: 0, arg_topic, arg_id. If key_point_id, keypoint, argument, topic, stance, Unnamed: 0, arg_topic, arg_id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running training *****\n",
            "  Num examples = 24454\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 4587\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='4587' max='4587' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [4587/4587 35:31, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rouge1</th>\n",
              "      <th>Rouge2</th>\n",
              "      <th>Rougel</th>\n",
              "      <th>Rougelsum</th>\n",
              "      <th>Gen Len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>7.091400</td>\n",
              "      <td>4.887713</td>\n",
              "      <td>7.049800</td>\n",
              "      <td>0.652300</td>\n",
              "      <td>6.865900</td>\n",
              "      <td>6.863600</td>\n",
              "      <td>13.276900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>4.152900</td>\n",
              "      <td>4.109730</td>\n",
              "      <td>7.731900</td>\n",
              "      <td>0.701400</td>\n",
              "      <td>7.662400</td>\n",
              "      <td>7.651800</td>\n",
              "      <td>9.354800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.729000</td>\n",
              "      <td>4.108914</td>\n",
              "      <td>6.629500</td>\n",
              "      <td>0.955600</td>\n",
              "      <td>6.445300</td>\n",
              "      <td>6.443700</td>\n",
              "      <td>11.334800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/checkpoint-500\n",
            "Configuration saved in /content/checkpoint-500/config.json\n",
            "Model weights saved in /content/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-500/special_tokens_map.json\n",
            "Copy vocab file to /content/checkpoint-500/spiece.model\n",
            "Saving model checkpoint to /content/checkpoint-1000\n",
            "Configuration saved in /content/checkpoint-1000/config.json\n",
            "Model weights saved in /content/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-1000/special_tokens_map.json\n",
            "Copy vocab file to /content/checkpoint-1000/spiece.model\n",
            "Saving model checkpoint to /content/checkpoint-1500\n",
            "Configuration saved in /content/checkpoint-1500/config.json\n",
            "Model weights saved in /content/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-1500/special_tokens_map.json\n",
            "Copy vocab file to /content/checkpoint-1500/spiece.model\n",
            "The following columns in the evaluation set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: key_point_id, keypoint, argument, topic, stance, Unnamed: 0, arg_topic, arg_id. If key_point_id, keypoint, argument, topic, stance, Unnamed: 0, arg_topic, arg_id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 4211\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/checkpoint-2000\n",
            "Configuration saved in /content/checkpoint-2000/config.json\n",
            "Model weights saved in /content/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-2000/special_tokens_map.json\n",
            "Copy vocab file to /content/checkpoint-2000/spiece.model\n",
            "Saving model checkpoint to /content/checkpoint-2500\n",
            "Configuration saved in /content/checkpoint-2500/config.json\n",
            "Model weights saved in /content/checkpoint-2500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-2500/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-2500/special_tokens_map.json\n",
            "Copy vocab file to /content/checkpoint-2500/spiece.model\n",
            "Saving model checkpoint to /content/checkpoint-3000\n",
            "Configuration saved in /content/checkpoint-3000/config.json\n",
            "Model weights saved in /content/checkpoint-3000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-3000/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-3000/special_tokens_map.json\n",
            "Copy vocab file to /content/checkpoint-3000/spiece.model\n",
            "The following columns in the evaluation set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: key_point_id, keypoint, argument, topic, stance, Unnamed: 0, arg_topic, arg_id. If key_point_id, keypoint, argument, topic, stance, Unnamed: 0, arg_topic, arg_id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 4211\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/checkpoint-3500\n",
            "Configuration saved in /content/checkpoint-3500/config.json\n",
            "Model weights saved in /content/checkpoint-3500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-3500/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-3500/special_tokens_map.json\n",
            "Copy vocab file to /content/checkpoint-3500/spiece.model\n",
            "Saving model checkpoint to /content/checkpoint-4000\n",
            "Configuration saved in /content/checkpoint-4000/config.json\n",
            "Model weights saved in /content/checkpoint-4000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-4000/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-4000/special_tokens_map.json\n",
            "Copy vocab file to /content/checkpoint-4000/spiece.model\n",
            "Saving model checkpoint to /content/checkpoint-4500\n",
            "Configuration saved in /content/checkpoint-4500/config.json\n",
            "Model weights saved in /content/checkpoint-4500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-4500/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-4500/special_tokens_map.json\n",
            "Copy vocab file to /content/checkpoint-4500/spiece.model\n",
            "The following columns in the evaluation set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: key_point_id, keypoint, argument, topic, stance, Unnamed: 0, arg_topic, arg_id. If key_point_id, keypoint, argument, topic, stance, Unnamed: 0, arg_topic, arg_id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 4211\n",
            "  Batch size = 16\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Saving model checkpoint to /content/\n",
            "Configuration saved in /content/config.json\n",
            "Model weights saved in /content/pytorch_model.bin\n",
            "tokenizer config file saved in /content/tokenizer_config.json\n",
            "Special tokens file saved in /content/special_tokens_map.json\n",
            "Copy vocab file to /content/spiece.model\n",
            "The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: key_point_id, keypoint, argument, topic, stance, Unnamed: 0, arg_topic, arg_id. If key_point_id, keypoint, argument, topic, stance, Unnamed: 0, arg_topic, arg_id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 3923\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PredictionOutput(predictions=array([[    0, 25750, 19878, ...,     0,     0,     0],\n",
            "       [    0, 25750, 19878, ...,     0,     0,     0],\n",
            "       [    0, 25750, 19878, ...,     0,     0,     0],\n",
            "       ...,\n",
            "       [    0, 34566, 31176, ...,     0,     0,     0],\n",
            "       [    0, 34566, 31176, ...,     0,     0,     0],\n",
            "       [    0, 34566, 31176, ...,     0,     0,     0]]), label_ids=array([[   259, 226116,    265, ...,   -100,   -100,   -100],\n",
            "       [ 49175,  59997,    259, ...,   -100,   -100,   -100],\n",
            "       [   486,  22552,    305, ...,   -100,   -100,   -100],\n",
            "       ...,\n",
            "       [   486,   2279,   1070, ...,   -100,   -100,   -100],\n",
            "       [   486,   2279,   1070, ...,   -100,   -100,   -100],\n",
            "       [   486,   2279,   1070, ...,   -100,   -100,   -100]]), metrics={'test_loss': 3.662539005279541, 'test_rouge1': 12.8791, 'test_rouge2': 3.6119, 'test_rougeL': 12.596, 'test_rougeLsum': 12.592, 'test_gen_len': 25.1323, 'test_runtime': 470.9206, 'test_samples_per_second': 8.33, 'test_steps_per_second': 0.522})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if trainer.is_world_process_zero():\n",
        "  if training_args.predict_with_generate:\n",
        "    \n",
        "    # decode model's predictions\n",
        "    test_preds = tokenizer.batch_decode(\n",
        "        test_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True\n",
        "        )\n",
        "    # split model's predictions\n",
        "    test_preds = [pred.strip() for pred in test_preds]\n",
        "    \n",
        "    # save model's predictions in a txt file\n",
        "    preds_file = os.path.join('/content/', \"test_set_generations.txt\")\n",
        "    with open(preds_file, \"w\") as file_out:\n",
        "          file_out.write(\"\\n\".join(test_preds))"
      ],
      "metadata": {
        "id": "2DCZqp91Qv7Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Keypoint Ranking**\n",
        "- Read all the generated keypoints \n",
        "- Assign to each keypoint a rouge-score with its corresponding <argument+topic>\n",
        "- Rank keypoint based on their score\n",
        "- Select top 5 keypoint for each topic and stance"
      ],
      "metadata": {
        "id": "ndA7gLK0_Yn4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# open file in which predictions are stored\n",
        "f = open('/content/test_set_generations.txt','r')\n",
        "preds = f.read().splitlines()\n",
        "\n",
        "# open test set as dataframe and add a column with all the given predictions\n",
        "df_test = pd.read_csv(dataset_path + 'KPAgen_ts.csv')\n",
        "df_test[\"generated_keypoint\"] = preds\n",
        "\n",
        "# store in a file this dataframe with all the generated keypoint and save it\n",
        "final_kps = df_test\n",
        "final_kps.to_csv(\"/content/final.csv\")"
      ],
      "metadata": {
        "id": "_oeEE-e2ycMG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from rouge_score import rouge_scorer\n",
        "\n",
        "# define a scorer object to assignn rouge 1 score\n",
        "scorer = rouge_scorer.RougeScorer(['rouge1'], use_stemmer=True)\n",
        "\n",
        "def compute_rouge1_metrics(kp,arg_topic):\n",
        "\n",
        "  # compute rouge-1 score between keypoint and its correspondinng arguments concatened with the topic\n",
        "  scores = []\n",
        "  for i in range(len(kp)):\n",
        "    score = scorer.score(kp[i], arg_topic[i])\n",
        "    score = float(score[\"rouge1\"][1])\n",
        "    scores.append(score)\n",
        "\n",
        "  return scores\n",
        "\n",
        "def get_best_score(kps, topic, stance):\n",
        "\n",
        "  final_kps = []\n",
        "  final_score = []\n",
        "  final_topic = []\n",
        "  final_stance = []\n",
        "  \n",
        "  # get list of unique keypoint\n",
        "  gen_kps_unique = kps[\"generated_keypoint\"].unique()\n",
        "  \n",
        "  # for each unique keypoint\n",
        "  for gen_kp in gen_kps_unique:\n",
        "    \n",
        "    # consider all keypoint with same text\n",
        "    key_point = kps[(kps[\"generated_keypoint\"]==gen_kp)]\n",
        "    # assign to this keypoint the mean of their score\n",
        "    best_score = key_point[\"rouge_score\"].max()\n",
        "    # return keypoint info: text, score, topic, stance\n",
        "    final_kps.append(gen_kp)\n",
        "    final_score.append(best_score)\n",
        "    final_topic.append(topic)\n",
        "    final_stance.append(stance)\n",
        "  \n",
        "  # create a keypoints dataframe\n",
        "  keypoint_dataframe = pd.DataFrame({\"generated_keypoint\":final_kps, \"score\":final_score,\"topic\":final_topic, \"stance\":final_stance})\n",
        "  # order keypoints based on their score\n",
        "  keypoint_dataframe = keypoint_dataframe.sort_values(by='score', ascending=False)\n",
        "  # save only the top 5 key point \n",
        "  keypoint_dataframe = keypoint_dataframe.iloc[0:5, :]\n",
        "\n",
        "  return keypoint_dataframe\n",
        "\n",
        "def keypoint_ranking(kps, test_set):\n",
        "  \n",
        "  # initialize an empty dataframe for final keypoints\n",
        "  final_kps = pd.DataFrame({\n",
        "      \"generated_keypoint\": [np.nan],\n",
        "      \"score\": [np.nan],\n",
        "      \"topic\": [np.nan],\n",
        "      \"stance\": [np.nan]\n",
        "      },\n",
        "      index=[0]\n",
        "      )\n",
        "\n",
        "  # get list of all topic inside TS set\n",
        "  all_topic = test_set[\"topic\"].unique()\n",
        "\n",
        "  # for each topic\n",
        "  for topic in all_topic:\n",
        "    \n",
        "    # create a temporaneous test set with only row concerning that topic\n",
        "    temp_test_set = test_set[(test_set['topic']==topic)]\n",
        "    # create a temporaneous test set with only row concerning that topic and having positive stance \n",
        "    pos_test_set = temp_test_set[(temp_test_set[\"stance\"]==1)]\n",
        "    # create a temporaneous test set with only row concerning that topic and having negative stance \n",
        "    neg_test_set = temp_test_set[(temp_test_set[\"stance\"]==-1)]\n",
        "   \n",
        "\n",
        "    # given a temporaneus test set\n",
        "    # ---> compute rouge score between keypoint (from temp. kp set) and corresponding argument+topic (from temp. test set)\n",
        "    # repeat the procedure both for positive and negative set  \n",
        "    pos_test_set[\"rouge_score\"] = compute_rouge1_metrics(pos_test_set[\"generated_keypoint\"].tolist(), pos_test_set[\"argument\"].tolist())\n",
        "    neg_test_set[\"rouge_score\"] = compute_rouge1_metrics(neg_test_set[\"generated_keypoint\"].tolist(), neg_test_set[\"argument\"].tolist())\n",
        "    \n",
        "\n",
        "    # both for postive stance and negative stance set of keypoint keep only top 5 keypoints\n",
        "    pos_kps_5 = get_best_score(pos_test_set, topic, 1)\n",
        "    neg_kps_5 = get_best_score(neg_test_set, topic, -1)\n",
        "    \n",
        "    # concatanate pos and neg keypoint to obtain top 10 keypoint under this topic\n",
        "    final_kps = pd.concat([final_kps, pos_kps_5])\n",
        "    final_kps = pd.concat([final_kps, neg_kps_5])\n",
        "\n",
        "  # return final keypoint set   \n",
        "  final_kps = final_kps.iloc[1: , :]\n",
        "  final_kps = final_kps[[\"generated_keypoint\", \"topic\", \"stance\"]]\n",
        "  final_kps[\"stance\"] = final_kps[\"stance\"].astype(int)\n",
        "  final_kps = final_kps.reset_index(drop=True)\n",
        "\n",
        "  display(final_kps)\n",
        "                                \n",
        "  return final_kps\n",
        "\n",
        "\n",
        "final_key_points = keypoint_ranking( final_kps, df_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "id": "pTjA5-Zvx3D9",
        "outputId": "e08f097e-0828-4f58-f634-981dd723ac20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                   generated_keypoint  \\\n",
              "0    The childhood vaccine is essential for the child   \n",
              "1   Private child vaccination is essential to prev...   \n",
              "2                 Legalizing vaccination is essential   \n",
              "3                Legalizing vaccine prevents diseases   \n",
              "4                 Legalizing vaccination is important   \n",
              "5         Legalizing child vaccinations are expensive   \n",
              "6   Women should be able to choose to choose their...   \n",
              "7                     Legalizing vaccines are harmful   \n",
              "8   Women should be able to be able to adopt their...   \n",
              "9                        Legalizing child vaccination   \n",
              "10                   Legalizing bullying is important   \n",
              "11                        Subsidies cannot be allowed   \n",
              "12                            Legalizing social media   \n",
              "13                         Legalizing social networks   \n",
              "14  Legalizing social networks are essential to in...   \n",
              "15    The social media promotes freedom of expression   \n",
              "16               Legalizing social media is important   \n",
              "17              Private social networks are expensive   \n",
              "18               Legalizing the freedom of expression   \n",
              "19              Private companies cannot be regulated   \n",
              "20                     The United States is a country   \n",
              "21                 The United States is a big country   \n",
              "22     The United States of America is a good country   \n",
              "23                 The United States is a good choice   \n",
              "24                The United States is a good country   \n",
              "25                     The health sector is expensive   \n",
              "26                           The US is a good country   \n",
              "27                  Legalizing education is expensive   \n",
              "28                            Subsidies are expensive   \n",
              "29                The United States is a good country   \n",
              "\n",
              "                                                topic  stance  \n",
              "0      Routine child vaccinations should be mandatory       1  \n",
              "1      Routine child vaccinations should be mandatory       1  \n",
              "2      Routine child vaccinations should be mandatory       1  \n",
              "3      Routine child vaccinations should be mandatory       1  \n",
              "4      Routine child vaccinations should be mandatory       1  \n",
              "5      Routine child vaccinations should be mandatory      -1  \n",
              "6      Routine child vaccinations should be mandatory      -1  \n",
              "7      Routine child vaccinations should be mandatory      -1  \n",
              "8      Routine child vaccinations should be mandatory      -1  \n",
              "9      Routine child vaccinations should be mandatory      -1  \n",
              "10  Social media platforms should be regulated by ...       1  \n",
              "11  Social media platforms should be regulated by ...       1  \n",
              "12  Social media platforms should be regulated by ...       1  \n",
              "13  Social media platforms should be regulated by ...       1  \n",
              "14  Social media platforms should be regulated by ...       1  \n",
              "15  Social media platforms should be regulated by ...      -1  \n",
              "16  Social media platforms should be regulated by ...      -1  \n",
              "17  Social media platforms should be regulated by ...      -1  \n",
              "18  Social media platforms should be regulated by ...      -1  \n",
              "19  Social media platforms should be regulated by ...      -1  \n",
              "20               The USA is a good country to live in       1  \n",
              "21               The USA is a good country to live in       1  \n",
              "22               The USA is a good country to live in       1  \n",
              "23               The USA is a good country to live in       1  \n",
              "24               The USA is a good country to live in       1  \n",
              "25               The USA is a good country to live in      -1  \n",
              "26               The USA is a good country to live in      -1  \n",
              "27               The USA is a good country to live in      -1  \n",
              "28               The USA is a good country to live in      -1  \n",
              "29               The USA is a good country to live in      -1  "
            ],
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              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generated_keypoint</th>\n",
              "      <th>topic</th>\n",
              "      <th>stance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The childhood vaccine is essential for the child</td>\n",
              "      <td>Routine child vaccinations should be mandatory</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Private child vaccination is essential to prev...</td>\n",
              "      <td>Routine child vaccinations should be mandatory</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Legalizing vaccination is essential</td>\n",
              "      <td>Routine child vaccinations should be mandatory</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Legalizing vaccine prevents diseases</td>\n",
              "      <td>Routine child vaccinations should be mandatory</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Legalizing vaccination is important</td>\n",
              "      <td>Routine child vaccinations should be mandatory</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Legalizing child vaccinations are expensive</td>\n",
              "      <td>Routine child vaccinations should be mandatory</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Women should be able to choose to choose their...</td>\n",
              "      <td>Routine child vaccinations should be mandatory</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Legalizing vaccines are harmful</td>\n",
              "      <td>Routine child vaccinations should be mandatory</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Women should be able to be able to adopt their...</td>\n",
              "      <td>Routine child vaccinations should be mandatory</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Legalizing child vaccination</td>\n",
              "      <td>Routine child vaccinations should be mandatory</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Legalizing bullying is important</td>\n",
              "      <td>Social media platforms should be regulated by ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Subsidies cannot be allowed</td>\n",
              "      <td>Social media platforms should be regulated by ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Legalizing social media</td>\n",
              "      <td>Social media platforms should be regulated by ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Legalizing social networks</td>\n",
              "      <td>Social media platforms should be regulated by ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Legalizing social networks are essential to in...</td>\n",
              "      <td>Social media platforms should be regulated by ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>The social media promotes freedom of expression</td>\n",
              "      <td>Social media platforms should be regulated by ...</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Legalizing social media is important</td>\n",
              "      <td>Social media platforms should be regulated by ...</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Private social networks are expensive</td>\n",
              "      <td>Social media platforms should be regulated by ...</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Legalizing the freedom of expression</td>\n",
              "      <td>Social media platforms should be regulated by ...</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Private companies cannot be regulated</td>\n",
              "      <td>Social media platforms should be regulated by ...</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>The United States is a country</td>\n",
              "      <td>The USA is a good country to live in</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>The United States is a big country</td>\n",
              "      <td>The USA is a good country to live in</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>The United States of America is a good country</td>\n",
              "      <td>The USA is a good country to live in</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>The United States is a good choice</td>\n",
              "      <td>The USA is a good country to live in</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>The United States is a good country</td>\n",
              "      <td>The USA is a good country to live in</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>The health sector is expensive</td>\n",
              "      <td>The USA is a good country to live in</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>The US is a good country</td>\n",
              "      <td>The USA is a good country to live in</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Legalizing education is expensive</td>\n",
              "      <td>The USA is a good country to live in</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Subsidies are expensive</td>\n",
              "      <td>The USA is a good country to live in</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>The United States is a good country</td>\n",
              "      <td>The USA is a good country to live in</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                     [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",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Final Keypoints\n",
        "\n",
        "> Final keypoints are saved in file `generated_keypoints.csv`"
      ],
      "metadata": {
        "id": "wPtM8HD1IojX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_key_points.to_csv('/content/generated_keypoints.csv')\n",
        "print(\"Final keypoint has been generated and saved in file: generated_keypoints.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bV6p8zyLzKmy",
        "outputId": "bb194ea7-a68c-414e-8e2c-b29cc8854109"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final keypoint has been generated and saved in file: generated_keypoints.csv\n"
          ]
        }
      ]
    }
  ]
}