Mistral_800_Train_Epoch4.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AtIoHZrLbPmq",
        "outputId": "5a24dc58-903c-4500-b9fc-21859df0637f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue May 28 12:18:30 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\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  NVIDIA A100-SXM4-40GB          Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   30C    P0              41W / 400W |      2MiB / 40960MiB |      0%      Default |\n",
            "|                                         |                      |             Disabled |\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",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBt10q-Omfrv",
        "outputId": "ee3bf9ac-e557-41f3-9433-4dc0f4365bb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "_MqdRYDtFMVM"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "\n",
        "\n",
        "kaggle_json = './drive/MyDrive/kaggle.json'\n",
        "with open(kaggle_json, 'r') as f:\n",
        "    json_data = json.load(f)\n",
        "    os.environ['KAGGLE_USERNAME'] = json_data['username']\n",
        "    os.environ['KAGGLE_KEY'] = json_data['key']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PoTMsMmMFCzR",
        "outputId": "4ada507f-7273-4ced-b658-3c8af33318c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.14)\n",
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            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.7)\n",
            "Dataset URL: https://www.kaggle.com/datasets/umgeeyo/korean-essay\n",
            "License(s): unknown\n",
            "Downloading korean-essay.zip to /content\n",
            " 95% 1.00M/1.06M [00:00<00:00, 1.10MB/s]\n",
            "100% 1.06M/1.06M [00:00<00:00, 1.15MB/s]\n",
            "Dataset URL: https://www.kaggle.com/datasets/umgeeyo/translated-d4\n",
            "License(s): unknown\n",
            "Downloading translated-d4.zip to /content\n",
            " 98% 73.0M/74.3M [00:06<00:00, 17.2MB/s]\n",
            "100% 74.3M/74.3M [00:06<00:00, 12.4MB/s]\n"
          ]
        }
      ],
      "source": [
        "!pip install kaggle --upgrade\n",
        "\n",
        "\n",
        "!kaggle datasets download -d umgeeyo/korean-essay\n",
        "!kaggle datasets download -d umgeeyo/translated-d4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Ht8AfVhGD6H6"
      },
      "outputs": [],
      "source": [
        "# 압축 해제 후 input 에 train.csv test.csv  넣기\n",
        "import zipfile\n",
        "\n",
        "zipfile.ZipFile(f'/content/korean-essay.zip').extractall()\n",
        "zipfile.ZipFile(f'/content/translated-d4.zip').extractall()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDB9ax76QlrK",
        "outputId": "4e8454ba-1e0d-43c1-b8cd-f5c4bb5f50dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.19.1 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n",
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            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
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            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate) (12.5.40)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install -q bitsandbytes\n",
        "!pip install -q transformers\n",
        "!pip install -q accelerate\n",
        "!pip install -q peft\n",
        "!pip install -i https://pypi.org/simple/ bitsandbytes\n",
        "!pip install datasets\n",
        "!pip install accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "NH34_UsVR5Dg"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import time\n",
        "import yaml\n",
        "import torch\n",
        "import random\n",
        "import pandas as pd\n",
        "import torch.nn as nn\n",
        "from torch import optim\n",
        "from copy import deepcopy\n",
        "import bitsandbytes as bnb\n",
        "from tqdm.auto import tqdm\n",
        "from datasets import Dataset\n",
        "from accelerate import Accelerator\n",
        "from torch.utils.data import DataLoader\n",
        "from dataclasses import dataclass, field\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from transformers import AutoConfig, AutoModel\n",
        "from typing import List, Optional, Tuple, Union\n",
        "from peft import (LoraConfig, TaskType, get_peft_model,\n",
        "                  prepare_model_for_kbit_training)\n",
        "\n",
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "from transformers import (AutoModelForSequenceClassification,\n",
        "                          BitsAndBytesConfig, get_cosine_schedule_with_warmup)\n",
        "\n",
        "from transformers.modeling_outputs import SequenceClassifierOutputWithPast\n",
        "from transformers.models.mistral.modeling_mistral import (\n",
        "    MistralModel, MistralPreTrainedModel)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "1ya9yEvER61g"
      },
      "outputs": [],
      "source": [
        "def get_tokenizer(cfg):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\n",
        "        cfg['model']['backbone_path'],\n",
        "        use_fast=cfg['model']['tokenizer']['use_fast'],\n",
        "        padding_side=cfg['model']['tokenizer']['padding_side'],\n",
        "        truncation_side=cfg['model']['tokenizer']['truncation_side'],\n",
        "    )\n",
        "\n",
        "    # if the eos token is an empty string, we assign it to a token\n",
        "    if tokenizer.eos_token == \"\":\n",
        "        tokenizer.add_special_tokens({\"eos_token\": \"</s>\"})\n",
        "        tokenizer.eos_token = \"</s>\"\n",
        "\n",
        "    if tokenizer.pad_token is None:\n",
        "        if tokenizer.unk_token is not None:\n",
        "            tokenizer.pad_token = tokenizer.unk_token\n",
        "        else:\n",
        "            tokenizer.pad_token = tokenizer.eos_token\n",
        "    return tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "RMuP5mv_SA5M"
      },
      "outputs": [],
      "source": [
        "class AiDataset:\n",
        "    def __init__(self, cfg):\n",
        "        self.cfg = cfg\n",
        "        self.tokenizer = get_tokenizer(cfg)\n",
        "\n",
        "    def tokenize_function(self, examples):\n",
        "        tz = self.tokenizer(\n",
        "            examples[\"text\"],\n",
        "            padding=False,\n",
        "            truncation=True,\n",
        "            max_length=self.cfg['model']['max_length'],\n",
        "            add_special_tokens=True,\n",
        "        )\n",
        "\n",
        "        return tz\n",
        "\n",
        "    def compute_input_length(self, examples):\n",
        "        return {\"input_length\": [len(x) for x in examples[\"input_ids\"]]}\n",
        "\n",
        "#     def preprocess_function(self, df):\n",
        "#         df['text'] = df['text'].apply(lambda x: x.strip() + \"\\n###\\nIs the essay generated by AI?\")\n",
        "#         return df\n",
        "\n",
        "    def get_dataset(self, df):\n",
        "        \"\"\"\n",
        "        :param df: input dataframe\n",
        "        :type df: pd.DataFrame\n",
        "        :return: the created dataset\n",
        "        :rtype: Dataset\n",
        "        \"\"\"\n",
        "        df = deepcopy(df)\n",
        "        # df = self.preprocess_function(df)\n",
        "        task_dataset = Dataset.from_pandas(df)\n",
        "\n",
        "        task_dataset = task_dataset.map(self.tokenize_function, batched=True)\n",
        "        task_dataset = task_dataset.map(self.compute_input_length, batched=True)\n",
        "\n",
        "        return task_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfgsIf6sSDPT",
        "outputId": "93ec6a7e-a88a-4137-ce11-655bde65df72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing /conf_r_detect_mix_v16.yaml\n"
          ]
        }
      ],
      "source": [
        "%%writefile /conf_r_detect_mix_v16.yaml\n",
        "\n",
        "seed: 424\n",
        "use_wandb: false\n",
        "\n",
        "model:\n",
        "    backbone_path: maywell/Mistral-ko-7B-v0.1\n",
        "    max_length: 1296\n",
        "    num_labels: 1\n",
        "\n",
        "    tokenizer:\n",
        "        padding_side: left\n",
        "        truncation_side: left\n",
        "        use_fast: true\n",
        "\n",
        "    lora:\n",
        "        target_modules:\n",
        "          - q_proj\n",
        "          - k_proj\n",
        "        r: 8\n",
        "        lora_alpha: 16\n",
        "        lora_dropout: 0.1\n",
        "        modules_to_save:\n",
        "            - classification_head\n",
        "\n",
        "train_params:\n",
        "    per_device_train_batch_size: 1 # run on 4x A100\n",
        "    per_device_eval_batch_size: 1\n",
        "    num_train_epochs: 4 # 16\n",
        "    gradient_accumulation_steps: 4\n",
        "\n",
        "    warmup_pct: 0.1\n",
        "    eval_frequency: 500\n",
        "    patience: 20\n",
        "    save_trigger: 0.0\n",
        "\n",
        "    use_mask_aug: false # false\n",
        "    mask_aug_prob: 0.0\n",
        "\n",
        "optimizer:\n",
        "    name: AdamW8bit\n",
        "    head_lr: 2e-6\n",
        "    lr: 2e-5\n",
        "    weight_decay: 1e-2\n",
        "    max_grad_norm: 0.5\n",
        "\n",
        "outputs:\n",
        "    model_dir: /content/drive/MyDrive/\n",
        "\n",
        "wandb:\n",
        "    project: detect-ai-a1\n",
        "    run_name: exp010-r-detect\n",
        "    tags:\n",
        "    - mistral"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vyfv2CptSHLA",
        "outputId": "98cefbe7-be54-436d-eef7-4e41485e7e12"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'seed': 424, 'use_wandb': False, 'model': {'backbone_path': 'maywell/Mistral-ko-7B-v0.1', 'max_length': 1296, 'num_labels': 1, 'tokenizer': {'padding_side': 'left', 'truncation_side': 'left', 'use_fast': True}, 'lora': {'target_modules': ['q_proj', 'k_proj'], 'r': 8, 'lora_alpha': 16, 'lora_dropout': 0.1, 'modules_to_save': ['classification_head']}}, 'train_params': {'per_device_train_batch_size': 1, 'per_device_eval_batch_size': 1, 'num_train_epochs': 4, 'gradient_accumulation_steps': 4, 'warmup_pct': 0.1, 'eval_frequency': 500, 'patience': 20, 'save_trigger': 0.0, 'use_mask_aug': False, 'mask_aug_prob': 0.0}, 'optimizer': {'name': 'AdamW8bit', 'head_lr': '2e-6', 'lr': '2e-5', 'weight_decay': '1e-2', 'max_grad_norm': 0.5}, 'outputs': {'model_dir': '/content/drive/MyDrive/'}, 'wandb': {'project': 'detect-ai-a1', 'run_name': 'exp010-r-detect', 'tags': ['mistral']}}\n"
          ]
        }
      ],
      "source": [
        "# YAML 파일 경로\n",
        "yaml_file_path = \"/conf_r_detect_mix_v16.yaml\"\n",
        "\n",
        "def read_yaml(yaml_file_path):\n",
        "    with open(yaml_file_path, \"r\") as file:\n",
        "        config = yaml.safe_load(file)\n",
        "    return config\n",
        "\n",
        "# YAML 파일 읽기\n",
        "config = read_yaml(yaml_file_path)\n",
        "print(config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xiyCofhsSIdS",
        "outputId": "49a43089-5885-426f-e18f-cc90f00688ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Set Length: 800\n",
            "Validation Set Length: 100\n",
            "Test Set Length: 100\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "all_df = pd.read_csv(\"/content/train_drcat_04-translated.csv\")[['translated_text','label']]\n",
        "# NaN 값이 있는 행 삭제\n",
        "all_df = all_df.dropna(subset=['translated_text'])\n",
        "all_df['id'] = range(1, len(all_df) + 1)\n",
        "\n",
        "# test mode를 위해 all_df 길이 1000으로 단축\n",
        "all_df = all_df[:1000]\n",
        "\n",
        "all_df.rename(columns ={'translated_text':'text'},inplace=True)\n",
        "# 데이터셋을 train(80%)과 temp(20%)로 분할\n",
        "train_df, temp_df = train_test_split(all_df, test_size=0.2, stratify=all_df['label'], random_state=42)\n",
        "\n",
        "# temp 데이터셋을 다시 valid(10%)와 test(10%)로 분할\n",
        "valid_df, test_df = train_test_split(temp_df, test_size=0.5, stratify=temp_df['label'], random_state=42)\n",
        "\n",
        "print(f\"Training Set Length: {len(train_df)}\")\n",
        "print(f\"Validation Set Length: {len(valid_df)}\")\n",
        "print(f\"Test Set Length: {len(test_df)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "2p6wPkvvSJuG",
        "outputId": "56326d87-e7a0-4aba-9636-2cab84cca690"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of train data: (800, 3)\n"
          ]
        },
        {
          "output_type": "display_data",
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              "                                                text  label   id\n",
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              "2  학생들은 집에서 수업에 참석할 수 있어 혜택을 누릴 수 있습니다. 온라인 수업을 들...      0  882\n",
              "3  약사는 환자가 적시에 올바른 약을 받을 수 있도록 노력하는 의료 전문가입니다. 그들...      1  318\n",
              "4  우리 모두는 인생에 대해 긍정적인 시각을 갖는 것이 얼마나 중요한지 알고 있습니다....      1  183"
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              "\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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      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-a5ba733b-ca2f-469c-a19f-be10cfffa183 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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\\uac00\\uc7a5 \\uc778\\uae30 \\uc788\\ub294 \\uc704\\uc2a4\\ud0a4 \\ube0c\\ub79c\\ub4dc \\uc911 \\ud558\\ub098\\uc778 \\uba54\\uc774\\ucee4\\uc2a4 \\ub9c8\\ud06c(Maker\\u2019s Mark)\\ub97c \\ub9cc\\ub4dc\\ub294 \\uacc4\\uae30\\uac00 \\ub418\\uc5c8\\uc2b5\\ub2c8\\ub2e4.\\n\\n\\ub54c\\ub54c\\ub85c \\uc0b6\\uc774 \\uc544\\ubb34\\ub9ac \\ud798\\ub4e4\\uc5b4 \\ubcf4\\uc77c\\uc9c0\\ub77c\\ub3c4 \\uae0d\\uc815\\uc801\\uc778 \\ud0dc\\ub3c4\\ub97c \\uac16\\uace0 \\uc790\\uc2e0\\uc744 \\ubbff\\uc73c\\uba74 \\ud070 \\ubcc0\\ud654\\ub97c \\uac00\\uc838\\uc62c \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\uc774\\ub294 \\uc7a5\\uc560\\ubb3c\\uc744 \\ubb34\\ub108\\ub728\\ub9ac\\uace0, \\ubaa9\\ud45c \\ub2ec\\uc131\\uc744 \\uc704\\ud55c \\ub3d9\\uae30\\ub97c \\uc720\\uc9c0\\ud558\\uba70, \\uc790\\uc2e0\\uacfc \\uc5ec\\uc815\\uc744 \\uc2e0\\ub8b0\\ud558\\ub294 \\ub370 \\ub3c4\\uc6c0\\uc774 \\ub429\\ub2c8\\ub2e4. \\uc62c\\ubc14\\ub978 \\ud0dc\\ub3c4\\ub97c \\uac16\\ub294 \\uac83\\uc740 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\\uc218\\uc5c5\\uc774 \\ud6e8\\uc52c \\uc27d\\uae30 \\ub54c\\ubb38\\uc5d0 \\uc628\\ub77c\\uc778 \\uc218\\uc5c5\\uc744 \\ub4e3\\ub294 \\uac83\\uc744 \\uc120\\ud638\\ud569\\ub2c8\\ub2e4. \\ud559\\uad50\\uc5d0\\uc11c \\uc218\\uc5c5\\uc744 \\ud558\\uba74 \\ub9ce\\uc740 \\ud559\\uc0dd\\ub4e4\\uc774 \\uc218\\uc5c5\\uc2dc\\uac04\\uc5d0 \\uc18c\\ub9ac\\ub97c \\uc9c0\\ub974\\uae30 \\ub54c\\ubb38\\uc5d0 \\uc9d1\\uc911\\uc774 \\uc798 \\ub418\\uc9c0 \\uc54a\\uc2b5\\ub2c8\\ub2e4. \\uadf8\\ub798\\uc11c \\uc628\\ub77c\\uc778 \\uc218\\uc5c5\\uc744 \\ub4e3\\ub294 \\uac83\\uc774 \\ub354 \\uc88b\\uc2b5\\ub2c8\\ub2e4. \\ud559\\uc0dd\\ub4e4\\uc740 \\ud559\\uc2b5 \\uae30\\uc220\\uacfc \\ub354 \\ub9ce\\uc740 \\uc9c0\\uc2dd\\uc744 \\uac1c\\ubc1c\\ud569\\ub2c8\\ub2e4.\\n\\n\\ud558\\uc9c0\\ub9cc \\ud559\\uad50\\uc5d0\\uc11c \\uc218\\uc5c5\\uc744 \\ub4e3\\ub294 \\uac83\\uc740 \\uadf8\\ub2e4\\uc9c0 \\ub098\\uc058\\uc9c0 \\uc54a\\uc740\\ub370, \\ud559\\uc0dd\\ub4e4\\uc774 \\uc218\\uc5c5\\uc5d0 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\\uc5b4\\ub5a4 \\uc0ac\\ub78c\\ub4e4\\uc740 \\uc628\\ub77c\\uc778 \\uc218\\uc5c5\\uc744 \\ub4e3\\ub294 \\uac83\\uc774 \\uc9c0\\ub8e8\\ud558\\ub2e4\\uace0 \\uc0dd\\uac01\\ud558\\uc9c0\\ub9cc, \\uacf5\\ubd80\\ud558\\ub294 \\ub0b4\\uc6a9\\uc5d0 \\ub300\\ud574 \\uc54c\\uace0\\uc790 \\ub178\\ub825\\ud558\\uba74 \\uc9c0\\ub8e8\\ud558\\uc9c0 \\uc54a\\uace0 \\uc88b\\uc544\\ud560 \\uac83\\uc785\\ub2c8\\ub2e4. \\uc2dc\\uac04\\ub0ad\\ube44 \\ub300\\uc2e0 \\uc9d1\\uc5d0\\uc11c \\uc628\\ub77c\\uc778 \\uc218\\uc5c5\\uc744 \\ub4e4\\uc5b4\\ubcf4\\uc138\\uc694. \\uc989, \\ud559\\uc0dd\\uc774 \\ud61c\\ud0dd\\uc744 \\ubc1b\\uc744 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4.\\n\\n\\ud655\\uc2e4\\ud788, \\uc9d1\\uc5d0\\uc11c \\uc218\\uc5c5\\uc744 \\ub4e3\\ub294 \\ubc29\\ubc95\\uc740 \\uc2dc\\uac04\\uc744 \\uc808\\uc57d\\ud560 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\ud559\\uc0dd\\ub4e4\\uc740 \\uc5b4\\ub5bb\\uac8c \\ud574\\uc57c \\ud558\\ub294\\uc9c0 \\uc54c\\uace0 \\uc788\\uc2b5\\ub2c8\\ub2e4. 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\\ud558\\uc9c0\\ub9cc \\uc628\\ub77c\\uc778 \\uc218\\uc5c5\\uc744 \\ud558\\uba74 \\uacf5\\ubd80\\ub97c \\uc2dc\\uc791\\ud558\\uace0 \\ubb38\\uc81c\\uac00 \\ud574\\uacb0\\ub420 \\uac70\\uc608\\uc694. \\uadf8\\uac8c \\uc2e4\\ub825\\uc744 \\ud5a5\\uc0c1\\uc2dc\\ud0a4\\ub294 \\ub354 \\uc88b\\uc740 \\ubc29\\ubc95\\uc774\\uc5d0\\uc694. \\ud559\\uc0dd\\ub4e4\\uc740 \\uc5f4\\uc2ec\\ud788 \\uc77c\\ud558\\uace0 \\uc5f4\\uc2ec\\ud788 \\uacf5\\ubd80\\ud569\\ub2c8\\ub2e4. \\uadf8\\ub4e4\\uc740 \\uc88b\\uc740 \\uc9c1\\uc5c5\\uc744 \\uc5bb\\uc744 \\uac83\\uc785\\ub2c8\\ub2e4.\\n\\n\\uc774\\uac83\\uc740 \\uac00\\ub4dd \\ucc28 \\uc788\\uc74c\\uc744 \\uc81c\\uacf5\\ud569\\ub2c8\\ub2e4. \\ub611\\ub611\\ud55c. \\ucc9c\\uc7ac. \\uc5f4\\uc2ec\\ud788 \\uc77c\\ud558\\uace0 \\uc790\\uc2e0\\uc744 \\ubbff\\uc73c\\uc138\\uc694. \\uadf8\\ub4e4\\uc740 \\uc790\\uae30 \\uc790\\uc2e0\\uc744 \\ubbff\\uae30 \\uc2dc\\uc791\\ud588\\uc2b5\\ub2c8\\ub2e4. \\uadf8\\uac83\\uc740 \\ud559\\uc0dd\\ub4e4\\uc744 \\uc790\\ub791\\uc2a4\\ub7fd\\uac8c \\ub9cc\\ub4ed\\ub2c8\\ub2e4. \\uadf8\\ub4e4\\uc740 \\ub354 \\ub9ce\\uc740 \\uc77c\\uc744 \\ud558\\ub824\\uace0 \\uc0dd\\uac01\\ud558\\uae30 \\uc2dc\\uc791\\ud569\\ub2c8\\ub2e4. \\uc628\\ub77c\\uc778 \\uc218\\uc5c5\\uc5d0\\uc11c \\uacf5\\ubd80\\ub97c \\ub9ce\\uc774 \\ud558\\uba74 \\ub9ce\\uc740 \\uac83\\uc744 \\ud560 \\uc218 \\uc788\\uc744 \\uac83\\uc785\\ub2c8\\ub2e4. \\uadf8\\uac83\\uc774 \\uc9c0\\uc2dd\\uc744 \\ud5a5\\uc0c1\\uc2dc\\ud0a4\\ub294 \\ubc29\\ubc95\\uc774\\ub2e4. \\ub610\\ud55c \\uc5b4\\ub5a4 \\ud14c\\uc2a4\\ud2b8\\ub3c4 \\ud560 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\uc5b4\\ub5a4 \\uc2dc\\ud5d8\\ub3c4 \\ub450\\ub824\\uc6cc\\ud558\\uc9c0 \\uc54a\\uc744 \\uac83\\uc785\\ub2c8\\ub2e4. \\uacf5\\ubd80\\ud558\\ub294 \\ubc29\\uc2dd\\uc744 \\ubc14\\uafb8\\ub824\\uba74. \\uc628\\ub77c\\uc778 \\uc218\\uc5c5\\uc744 \\ud1b5\\ud574 \\ub354 \\ub9ce\\uc740 \\uacf5\\ubd80\\ub97c \\ud560 \\uc218 \\uc788\\uc744 \\ubfd0\\ub9cc \\uc544\\ub2c8\\ub77c \\uacbd\\ud5d8\\uc744 \\uc313\\ub294\\ub370\\ub3c4 \\ub3c4\\uc6c0\\uc774 \\ub429\\ub2c8\\ub2e4.\\n\\n\\ubd84\\uba85\\ud788 \\uc628\\ub77c\\uc778 \\uc218\\uc5c5\\uc740 \\uadc0\\ud558\\uc758 \\ubb38\\uc81c\\ub97c \\ud574\\uacb0\\ud560 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\uc62c\\ud574 \\uc878\\uc5c5\\ud558\\uace0 \\uc2f6\\uc740\\ub370 \\ud559\\uc810\\uc744 \\ub9c8\\uce58\\uc9c0 \\ubabb\\ud588\\ub2e4\\uba74, \\uac00\\uc7a5 \\uc88b\\uc740 \\ubc29\\ubc95\\uc740 \\uc62c\\ud574 \\uc878\\uc5c5\\ud558\\uae30 \\uc704\\ud574 \\ub4e4\\uc5b4\\uc57c \\ud558\\ub294 \\uc628\\ub77c\\uc778 \\uc218\\uc5c5\\uc744 \\ub4e3\\ub294 \\uac83\\uc785\\ub2c8\\ub2e4. \\uadf8\\ub7ec\\uba74 \\uc878\\uc5c5\\ud558\\uac8c \\ub429\\ub2c8\\ub2e4. \\uc628\\ub77c\\uc778 \\uc218\\uc5c5\\uc73c\\ub85c \\uc2dc\\uac04\\uc744 \\uc808\\uc57d\\ud560 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\uc644\\ubcbd\\ud558\\uac8c \\ud68d\\ub4dd\\ud558\\uc138\\uc694. \\uae30\\uc220\\uc744 \\uac1c\\ubc1c\\ud558\\uace0 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],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 291,\n        \"min\": 183,\n        \"max\": 882,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          187,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of validation data: (100, 3)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                                text  label   id\n",
              "0  \"저녁 별\"로도 알려진 금성은 작업하기 가장 어려운 행성 중 하나입니다. 화산이 많...      0  149\n",
              "1  주 5일, 하루 7시간, 일부 학생들은 방과 후 할 수 있는 활동이 있으며, 밤마다...      0  513\n",
              "2  운전 중 휴대폰\\n\\n운전 중 휴대전화는 오늘날 우리가 겪고 있는 가장 큰 문제 중...      0  976\n",
              "3  자동차를 운전하는 것은 사람들의 삶에 매우 정서적인 측면이 될 수 있으며 매일 수행...      0   90\n",
              "4  친애하는 상원의원님,\\n\\n선거인단 제도는 미국이 탄생할 때부터 존재해 왔습니다. ...      0  243"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-455c8fb1-0d33-412c-a220-6c34c1adf1c7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>\"저녁 별\"로도 알려진 금성은 작업하기 가장 어려운 행성 중 하나입니다. 화산이 많...</td>\n",
              "      <td>0</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>주 5일, 하루 7시간, 일부 학생들은 방과 후 할 수 있는 활동이 있으며, 밤마다...</td>\n",
              "      <td>0</td>\n",
              "      <td>513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>운전 중 휴대폰\\n\\n운전 중 휴대전화는 오늘날 우리가 겪고 있는 가장 큰 문제 중...</td>\n",
              "      <td>0</td>\n",
              "      <td>976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>자동차를 운전하는 것은 사람들의 삶에 매우 정서적인 측면이 될 수 있으며 매일 수행...</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>친애하는 상원의원님,\\n\\n선거인단 제도는 미국이 탄생할 때부터 존재해 왔습니다. ...</td>\n",
              "      <td>0</td>\n",
              "      <td>243</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #D2E3FC;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-455c8fb1-0d33-412c-a220-6c34c1adf1c7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    display: none;\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "  }\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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\\ud569\\ub2c8\\ub2e4.\",\n          \"\\uce5c\\uc560\\ud558\\ub294 \\uc0c1\\uc6d0\\uc758\\uc6d0\\ub2d8,\\n\\n\\uc120\\uac70\\uc778\\ub2e8 \\uc81c\\ub3c4\\ub294 \\ubbf8\\uad6d\\uc774 \\ud0c4\\uc0dd\\ud560 \\ub54c\\ubd80\\ud130 \\uc874\\uc7ac\\ud574 \\uc654\\uc2b5\\ub2c8\\ub2e4. \\uac74\\uad6d\\uc758 \\uc544\\ubc84\\uc9c0\\ub4e4\\uc740 \\uc774\\ub97c \\ud5cc\\ubc95\\uc5d0 \\uba85\\uc2dc\\ud588\\uc2b5\\ub2c8\\ub2e4. \\ub124, \\uc120\\uac70\\uc778\\ub2e8\\uc5d0 \\ub300\\ud55c \\ubb38\\uc81c\\uc640 \\uc6b0\\ub824\\ub3c4 \\uc788\\uc2b5\\ub2c8\\ub2e4\\ub9cc, \\uc624\\ub298\\ub0a0 \\uc138\\uc0c1\\uc758 \\ubaa8\\ub4e0 \\uc77c\\uc5d0\\ub294 \\ubb38\\uc81c\\uac00 \\uc788\\uc9c0 \\uc54a\\uc2b5\\ub2c8\\uae4c? pronalems\\ub294 \\uc57d\\uac04\\uc758 \\uc2dc\\uac04\\uacfc \\ub178\\ub825\\uc73c\\ub85c \\uace0\\uce60 \\uc218 \\uc788\\uc73c\\uba70, \\uc81c \\uc0dd\\uac01\\uc5d0\\ub294 \\uc120\\uac70\\uc778\\ub2e8 \\uc81c\\ub3c4\\uac00 \\ud574\\ub85c\\uc6b4 \\uac83\\ubcf4\\ub2e4 \\uc120\\uac70 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\\uc3df\\uace0 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\ubb38\\uc790 \\uba54\\uc2dc\\uc9c0\\ub97c \\ubcf4\\ub0b4\\uac70\\ub098 \\ud734\\ub300\\uc804\\ud654\\ub97c \\ubcf4\\uba74\\uc11c \\ucc9c\\ucc9c\\ud788 \\ucc28\\uc5d0 \\ub300\\ud55c \\ud1b5\\uc81c\\ub825\\uc744 \\uc783\\uc5b4 \\ub3c4\\ub85c\\uc5d0 \\uc8fc\\uc758\\ub97c \\uae30\\uc6b8\\uc774\\ub294 \\uac83\\ub9cc\\uc73c\\ub85c\\ub3c4 \\uc27d\\uac8c \\ud53c\\ud560 \\uc218 \\uc788\\uc5c8\\ub358 \\uc790\\ub3d9\\ucc28 \\uc0ac\\uace0\\ub97c \\uc77c\\uc73c\\ud0ac \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4.\\n\\n\\uc6b4\\uc804 \\uc911 \\uc804\\ud654\\ub97c \\ud558\\ub294 \\uac83\\ub3c4 \\uc704\\ud5d8\\ud569\\ub2c8\\ub2e4. \\uadc0\\ud558\\uac00 \\ubc14\\uc058\\uace0 \\uc911\\uc694\\ud55c \\uc804\\ud654\\uac00 \\ub4e4\\uc5b4\\uc624\\uac70\\ub098 \\ub098\\uac00\\ub294 \\uacbd\\uc6b0\\ub3c4 \\uc788\\uc9c0\\ub9cc, \\uadc0\\ud558\\uac00 \\uba48\\ucd94\\uac70\\ub098 \\ubaa9\\uc801\\uc9c0\\uc5d0 \\ub3c4\\ucc29\\ud560 \\ub54c\\uae4c\\uc9c0 \\uc804\\ud654\\uac00 \\uc9c4\\uc815\\uc73c\\ub85c \\uae30\\ub2e4\\ub9b4 \\uc218 \\uc788\\ub2e4\\ub294 \\uc810\\uc744 \\uc774\\ud574\\ud569\\ub2c8\\ub2e4. \\ud734\\ub300\\ud3f0\\uc744 \\uc9d1\\uc5b4\\ub4e4\\uac70\\ub098 \\uc190\\uc5d0 \\ub4e4\\uace0 \\uc788\\uc744 \\uc774\\uc720\\uac00 \\uc804\\ud600 \\uc5c6\\uc5b4\\uc57c \\ud569\\ub2c8\\ub2e4. \\uc804\\ud654\\uac00 \\uc815\\ub9d0 \\uc911\\uc694\\ud558\\ub2e4\\uba74 \\ud604\\uc7ac \\uc788\\ub294 \\uacf3\\uc5d0\\uc11c \\ucc28\\ub97c \\uc138\\uc6b0\\uace0 \\uc804\\ud654\\ub97c \\ubc1b\\ub3c4\\ub85d \\ud558\\uc138\\uc694. \\ucd5c\\uc18c\\ud55c \\uae38\\uc5d0 \\uc788\\ub294 \\ub2e4\\ub978 \\uc0ac\\ub78c\\ub4e4\\uc5d0 \\ub300\\ud574 \\uc0dd\\uac01\\ud574\\ubcf4\\uc138\\uc694.\\n\\n\\uc804\\ud654\\ub098 \\ubb38\\uc790\\ub294 \\ubaa9\\uc801\\uc9c0\\uc5d0 \\ub3c4\\ucc29\\ud560 \\ub54c\\uae4c\\uc9c0 \\uae30\\ub2e4\\ub9b4 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\uc6b4\\uc804 \\uc911\\uc5d0\\ub294 \\ud734\\ub300\\ud3f0\\uc744 \\uc0ac\\uc6a9\\ud574\\uc11c\\ub294 \\uc548 \\ub429\\ub2c8\\ub2e4. \\ub2f9\\uc2e0\\ubfd0\\ub9cc \\uc544\\ub2c8\\ub77c \\ubaa8\\ub4e0 \\uc0ac\\ub78c\\uc758 \\uc548\\uc804\\uc5d0 \\uc720\\uc758\\ud558\\uc2ed\\uc2dc\\uc624. \\ub0b4\\uac00 \\ubc29\\uae08 \\ub9d0\\ud55c \\ubaa8\\ub4e0 \\uac83\\uc744 \\uc0dd\\uac01\\ud558\\uace0 \\uace0\\ub824\\ud558\\uc2ed\\uc2dc\\uc624. \\ub2e4\\ub978 \\uc0ac\\ub78c\\ub4e4\\uc740 \\uc790\\uc2e0\\uc774 \\ubb34\\uc5c7\\uc744 \\ud558\\uace0 \\uc788\\ub294\\uc9c0 \\uc54c\\uace0 \\uc788\\uc9c0\\ub9cc \\ud53c\\ud560 \\uc218 \\uc788\\ub294 \\uc77c\\uc5d0 \\uc704\\ud5d8\\uc744 \\uac10\\uc218\\ud560 \\uc774\\uc720\\uac00 \\uc788\\ub2e4\\uba74 \\uc6b4\\uc804 \\uc911 \\uc804\\ud654\\ub97c \\ubc1b\\ub294 \\uac83\\ub3c4 \\ub098\\uc058\\uc9c0 \\uc54a\\ub2e4\\uace0 \\ub9d0\\ud560 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4.\\n\\n2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 363,\n        \"min\": 90,\n        \"max\": 976,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          513\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import random\n",
        "import pandas as pd\n",
        "\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "valid_df = valid_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "print(f\"shape of train data: {train_df.shape}\")\n",
        "display(train_df.head())\n",
        "print(f\"shape of validation data: {valid_df.shape}\")\n",
        "display(valid_df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371,
          "referenced_widgets": [
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          ]
        },
        "id": "uAFU5CPPUjSm",
        "outputId": "58aeeda9-609d-46bc-8b1d-b4c20ec20761"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b5c2182fe69495d9d4e60d94f7d90b1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.50M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f359efaf5caf4c2d9046684bbb751d97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/630 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82a194e10b2f45c3a856c09320a0134b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/800 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bde929a810fe48729155ad95c0142938"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/800 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e08f0c52f8bb4b268908332b358a660e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "52524690d4f64dc99648d364ca7fd9a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5abb93aa0bdb4b8e8d999b036eccc346"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset_creator = AiDataset(config)\n",
        "\n",
        "train_ds = dataset_creator.get_dataset(train_df)\n",
        "valid_ds = dataset_creator.get_dataset(valid_df)\n",
        "\n",
        "tokenizer = dataset_creator.tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "HxR-WEL_UnK-"
      },
      "outputs": [],
      "source": [
        "train_ds.set_format(\n",
        "    type=None,\n",
        "    columns=[\n",
        "        'id',\n",
        "        'input_ids',\n",
        "        'attention_mask',\n",
        "        'label'\n",
        "    ]\n",
        ")\n",
        "\n",
        "valid_ds = valid_ds.sort(\"input_length\")\n",
        "\n",
        "valid_ds.set_format(\n",
        "    type=None,\n",
        "    columns=[\n",
        "        'id',\n",
        "        'input_ids',\n",
        "        'attention_mask',\n",
        "        'label'\n",
        "    ]\n",
        ")\n",
        "valid_ids = valid_df[\"id\"]  # .tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "UtzIIXUEUqJu"
      },
      "outputs": [],
      "source": [
        "def print_line():\n",
        "    prefix, unit, suffix = \"#\", \"~~\", \"#\"\n",
        "    print(prefix + unit*50 + suffix)\n",
        "\n",
        "def apply_mask_augmentation(input_ids, tokenizer, mask_prob=0.1):\n",
        "    input_ids = deepcopy(input_ids)\n",
        "    input_ids = torch.tensor(input_ids, dtype=torch.int64)\n",
        "    indices_mask = torch.bernoulli(torch.full(input_ids.shape, mask_prob)).bool()\n",
        "\n",
        "    do_not_mask_tokens = list(set(tokenizer.all_special_ids))\n",
        "    pass_gate = [\n",
        "        [0 if token_id in do_not_mask_tokens else 1 for token_id in token_id_seq] for token_id_seq in input_ids\n",
        "    ]\n",
        "    pass_gate = torch.tensor(pass_gate, dtype=torch.bool)\n",
        "\n",
        "    indices_mask = torch.logical_and(indices_mask, pass_gate)\n",
        "    input_ids[indices_mask] = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n",
        "    return input_ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "TzK6cM_GUrcN"
      },
      "outputs": [],
      "source": [
        "# 모델이 이해할수 있는 형태로 가공하는것이라고 이해하면 좋을 것 같다.\n",
        "@dataclass\n",
        "class AiCollator(DataCollatorWithPadding):\n",
        "    \"\"\"\n",
        "    data collector for LLM Detect AI Generated Text task\n",
        "    \"\"\"\n",
        "\n",
        "    tokenizer = None\n",
        "    padding = True\n",
        "    max_length = None\n",
        "    pad_to_multiple_of = None\n",
        "    return_tensors = \"pt\"\n",
        "\n",
        "    def __call__(self, features):\n",
        "\n",
        "        buffer_dict = dict()\n",
        "        buffer_keys = [\"id\"]\n",
        "\n",
        "        for key in buffer_keys:\n",
        "            if key in features[0].keys():\n",
        "                value = [feature[key] for feature in features]\n",
        "                buffer_dict[key] = value\n",
        "\n",
        "        labels = None\n",
        "        if \"label\" in features[0].keys():\n",
        "            labels = [feature[\"label\"] for feature in features]\n",
        "\n",
        "        features = [\n",
        "            {\n",
        "                \"input_ids\": feature[\"input_ids\"],\n",
        "                \"attention_mask\": feature[\"attention_mask\"],\n",
        "            } for feature in features\n",
        "        ]\n",
        "\n",
        "        batch = self.tokenizer.pad(\n",
        "            features,\n",
        "            padding='longest',  # self.padding,\n",
        "            max_length=self.max_length,\n",
        "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
        "            return_tensors=None,\n",
        "        )\n",
        "\n",
        "        # for key, value in buffer_dict.items():\n",
        "        #     batch[key] = value\n",
        "\n",
        "        if labels is not None:\n",
        "            batch[\"labels\"] = labels\n",
        "\n",
        "        tensor_keys = [\n",
        "            \"input_ids\",\n",
        "            \"attention_mask\",\n",
        "        ]\n",
        "\n",
        "        for key in tensor_keys:\n",
        "            batch[key] = torch.tensor(batch[key], dtype=torch.int64)\n",
        "\n",
        "        if labels is not None:\n",
        "            batch[\"labels\"] = torch.tensor(batch[\"labels\"], dtype=torch.float32)\n",
        "\n",
        "        return batch\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "kL1LavCKUtap"
      },
      "outputs": [],
      "source": [
        "data_collator = AiCollator(\n",
        "    tokenizer=tokenizer,\n",
        "    pad_to_multiple_of=64\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "JxWW384aUumK"
      },
      "outputs": [],
      "source": [
        "@dataclass\n",
        "class AiCollatorTrain(DataCollatorWithPadding):\n",
        "    \"\"\"\n",
        "    data collector for LLM Detect AI Generated Text task\n",
        "    \"\"\"\n",
        "\n",
        "    tokenizer = None\n",
        "    padding = True\n",
        "    max_length = None\n",
        "    pad_to_multiple_of = None\n",
        "    return_tensors = \"pt\"\n",
        "    kwargs: field(default_factory=dict) = None\n",
        "\n",
        "    def __post_init__(self):\n",
        "        [setattr(self, k, v) for k, v in self.kwargs.items()]\n",
        "\n",
        "    def __call__(self, features):\n",
        "\n",
        "        buffer_dict = dict()\n",
        "        buffer_keys = [\"id\"]\n",
        "\n",
        "        for key in buffer_keys:\n",
        "            if key in features[0].keys():\n",
        "                value = [feature[key] for feature in features]\n",
        "                buffer_dict[key] = value\n",
        "\n",
        "        labels = None\n",
        "        if \"label\" in features[0].keys():\n",
        "            labels = [feature[\"label\"] for feature in features]\n",
        "\n",
        "        features = [\n",
        "            {\n",
        "                \"input_ids\": feature[\"input_ids\"],\n",
        "                \"attention_mask\": feature[\"attention_mask\"],\n",
        "            } for feature in features\n",
        "        ]\n",
        "\n",
        "        batch = self.tokenizer.pad(\n",
        "            features,\n",
        "            padding='longest',  # self.padding,\n",
        "            max_length=self.max_length,\n",
        "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
        "            return_tensors=None,\n",
        "        )\n",
        "\n",
        "        # for key, value in buffer_dict.items():\n",
        "        #     batch[key] = value\n",
        "\n",
        "        if self.cfg['train_params']['use_mask_aug']:\n",
        "            batch[\"input_ids\"] = apply_mask_augmentation(\n",
        "                batch[\"input_ids\"], self.tokenizer, self.cfg['train_params']['mask_aug_prob']\n",
        "            )\n",
        "\n",
        "        if labels is not None:\n",
        "            batch[\"labels\"] = labels\n",
        "\n",
        "        tensor_keys = [\n",
        "            \"input_ids\",\n",
        "            \"attention_mask\",\n",
        "        ]\n",
        "\n",
        "        for key in tensor_keys:\n",
        "            batch[key] = torch.tensor(batch[key], dtype=torch.int64)\n",
        "\n",
        "        if labels is not None:\n",
        "            batch[\"labels\"] = torch.tensor(batch[\"labels\"], dtype=torch.float32)\n",
        "\n",
        "        return batch\n",
        "# ---\n",
        "\n",
        "\n",
        "def show_batch(batch, tokenizer, n_examples=16, task='training', print_fn=print):\n",
        "    bs = batch['input_ids'].size(0)\n",
        "    print_fn(f\"batch size: {bs}\")\n",
        "\n",
        "    print_fn(f\"shape of input_ids: {batch['input_ids'].shape}\")\n",
        "\n",
        "    n_examples = min(n_examples, bs)\n",
        "    print_fn(f\"Showing {n_examples} from a {task} batch...\")\n",
        "\n",
        "    print_fn(\"\\n\\n\")\n",
        "    for idx in range(n_examples):\n",
        "        print_fn(f\"Example {idx+1}\")\n",
        "        print_fn(f\"Input:\\n\\n{tokenizer.decode(batch['input_ids'][idx], skip_special_tokens=False)}\")\n",
        "        # print(\"\\n\\n\")\n",
        "\n",
        "        if \"infer\" not in task.lower():\n",
        "            print_fn(\"--\"*20)\n",
        "            labels = batch['labels'][idx]\n",
        "            print_fn(f\"Label: {labels}\")\n",
        "        print_fn('~~'*40)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "w4Jfd9zzUwLw"
      },
      "outputs": [],
      "source": [
        "\n",
        "data_collator_train = AiCollatorTrain(\n",
        "    tokenizer=tokenizer,\n",
        "    pad_to_multiple_of=64,\n",
        "    kwargs=dict(cfg=config)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3GIo9OFUyEv",
        "outputId": "1baef97e-9c6d-4caa-ee51-f5fcf3aae786"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data preparation done...\n",
            "batch size: 1\n",
            "shape of input_ids: torch.Size([1, 640])\n",
            "Showing 1 from a training batch...\n",
            "\n",
            "\n",
            "\n",
            "Example 1\n",
            "Input:\n",
            "\n",
            "</s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s><s> 스스로 운전할 수 있는 자동차?\n",
            "\n",
            "무인자동차는 과연 좋은 아이디어일까? 무인 자동차를 제조하는 것은 성공조차 하지 못했습니다. 스스로 운전할 수 있는 자동차에는 여전히 경계심이 강한 운전자가 필요합니다. 자율주행차가 제조된 법률을 변경해야 한다면, 충돌 사고가 발생하면 회사와 소유자는 문제를 겪게 될 것이며, 주의가 필요한 소유자는 경계를 유지하지 못할 수도 있습니다. 자율주행차는 실제 고객에게 시험해 보기까지는 더 많은 시간이 필요하다.\n",
            "\n",
            "구글이 제작한 자율주행차가 충돌 없이 50만 마일을 달렸다. 그러나 주차, 교통 탐색 또는 도로 건설 시 운전자는 운전대를 잡도록 경고를 받습니다. \"무인자동차\"가 소유자에게 경고했지만 소유자가 주의를 기울이지 않고 제 시간에 운전하지 않으면 어떻게 될까요? 이로 인해 대형 사고가 발생할 수 있습니다. 도로에 집중하지 않는 운전자는 자신과 주변의 다른 운전자를 위험에 빠뜨릴 수 있습니다.\n",
            "\n",
            "운전자가 부주의하여 사고가 난다면 회사와 소유주 모두 심각한 문제에 빠질 것입니다. 이 문제를 해결하려면 인증법을 제정하거나 변경해야 합니다. 두 대의 차량이 충돌하는 경우 운전자 중 한 명이 지금처럼 비난을 받거나 두 회사 모두 비난을 받게 됩니다. 기술에는 작동하지 않는 경우가 있습니다. 누구나 전화나 TV에 소리를 지르는 적이 있습니다. 비록 그 기술이 제대로 작동하지 않는다면 그것은 생사 상황이 아닙니다. 게다가 이게 얼마나 비쌀까요? 전화, TV 또는 새 컴퓨터가 얼마나 비쌀 수 있는지는 잘 알려져 있습니다. 판매 가격으로 만드는 것은 너무 비싸서 제조에 소비된 돈을 회수해야 하며 판매원은 실제로 작업을 위해 돈이 필요할 것입니다. 실제로 그것을 감당할 수 있는 사람은 몇 명이나 될까요? 그것이 도움이 될 사람들은 돈을 벌기 위한 돈조차 마련하지 못할 가능성이 높습니다.\n",
            "\n",
            "사고율이 높아지면서 이제 운전자는 운전대를 잡고도 주의를 기울이는 일이 거의 없습니다. 그러나 자동차가 스스로 운전하는 동안 원하는 대로 할 자유가 있다는 것을 알거나 생각하면 집중력을 잃게 됩니다. 운전자는 도로에 주의를 기울이지 않습니다. 사람들은 자신의 기술에 대해 많은 신뢰를 갖고 있기 때문에 운전을 해야 할 때 무슨 일이 일어날지 생각조차 하지 않습니다. 낮잠을 자려고 했다가 깨어나 운전대를 잡게 된다면 그들은 어떻게 반응할 것인가? 사고를 피하기 위해 무엇을 해야 할지 생각할 시간조차 없을 수도 있습니다.\n",
            "\n",
            "무인 자동차의 요점은 무엇입니까? 도로 위의 음주운전자 수를 줄일 수도 있습니다. 또는 대중교통 없이는 운전을 할 수 없는 사람들에게 필요한 교통수단을 제공하십시오. 이 모든 것이 좋을 것입니다! 하지만 자동차가 그들을 대신해야 한다면 무슨 일이 일어날까요? 물론 운전을 할 수 없는 사람이 운전을 할 수 없도록 운전 능력을 갖추려면 여전히 훈련 등을 거쳐야 합니다. 또한 음주운전자가 경고를 받은 경우에도 음주운전을 계속하게 됩니다. 이 기술을 사용할 수 있는 유일한 사람은 이미 운전할 수 있는 사람입니다. 그렇다면 무인자동차의 의미는 무엇일까? 무인 자동차 제조를 시작하는 데에는 안전상의 위험과 무의미한 이유가 너무 많습니다.\n",
            "----------------------------------------\n",
            "Label: 0.0\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            "batch size: 1\n",
            "shape of input_ids: torch.Size([1, 192])\n",
            "Showing 1 from a training batch...\n",
            "\n",
            "\n",
            "\n",
            "Example 1\n",
            "Input:\n",
            "\n",
            "</s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s><s> 방과후 프로그램은 학생들이 학교에서 성공하고 성적을 올릴 수 있도록 돕는 효과적인 방법입니다. 학생들은 수업을 더 잘 이해할 수 있도록 추가적인 학업 도움을 받을 수 있으며, 방과 후 개인교습을 통해 누락된 과제를 완료할 수 있는 조용한 공간도 제공됩니다. 이러한 추가 학습 환경을 통해 학생들은 질문하고, 교사 및 교직원과의 관계를 강화하고, 학문적 성공에 필요한 기술을 구축할 수 있는 더 많은 시간을 얻을 수 있습니다. 지도해 주는 어른들과 함께 시간을 보내는 것은 학생의 사회적, 정서적 건강을 지원하여 학업 성취도 향상으로 이어질 수 있습니다.\n",
            "----------------------------------------\n",
            "Label: 1.0\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n"
          ]
        }
      ],
      "source": [
        "train_dl = DataLoader(\n",
        "    train_ds,\n",
        "    batch_size=config['train_params']['per_device_train_batch_size'],\n",
        "    shuffle=True,\n",
        "    collate_fn=data_collator_train,\n",
        ")\n",
        "\n",
        "valid_dl = DataLoader(\n",
        "    valid_ds,\n",
        "    batch_size=config['train_params']['per_device_eval_batch_size'],\n",
        "    shuffle=False,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "\n",
        "print(\"data preparation done...\")\n",
        "\n",
        "for b in train_dl:\n",
        "    break\n",
        "show_batch(b, tokenizer, task='training', print_fn=print)\n",
        "\n",
        "print_line()\n",
        "\n",
        "for b in valid_dl:\n",
        "    break\n",
        "show_batch(b, tokenizer, task='training', print_fn=print)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "TK7JyT8UUzFv"
      },
      "outputs": [],
      "source": [
        "class MistralForDetectAI(MistralPreTrainedModel):\n",
        "    def __init__(self, config):\n",
        "        super().__init__(config)\n",
        "        self.num_labels = config.num_labels\n",
        "        self.model = MistralModel(config)\n",
        "        # self.dropout = nn.Dropout(0.3)\n",
        "\n",
        "        self.classification_head = nn.Linear(config.hidden_size, self.num_labels, bias=False)\n",
        "\n",
        "        self.loss_fn = nn.BCEWithLogitsLoss()\n",
        "\n",
        "        # Initialize weights and apply final processing\n",
        "        self.post_init()\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        input_ids: torch.LongTensor = None,\n",
        "        attention_mask: Optional[torch.Tensor] = None,\n",
        "        position_ids: Optional[torch.LongTensor] = None,\n",
        "        past_key_values: Optional[List[torch.FloatTensor]] = None,\n",
        "        inputs_embeds: Optional[torch.FloatTensor] = None,\n",
        "        labels: Optional[torch.LongTensor] = None,\n",
        "        use_cache: Optional[bool] = None,\n",
        "        output_attentions: Optional[bool] = None,\n",
        "        output_hidden_states: Optional[bool] = None,\n",
        "        return_dict: Optional[bool] = None,\n",
        "    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:\n",
        "\n",
        "        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
        "\n",
        "        transformer_outputs = self.model(\n",
        "            input_ids,\n",
        "            attention_mask=attention_mask,\n",
        "            position_ids=position_ids,\n",
        "            past_key_values=past_key_values,\n",
        "            inputs_embeds=inputs_embeds,\n",
        "            use_cache=use_cache,\n",
        "            output_attentions=output_attentions,\n",
        "            output_hidden_states=output_hidden_states,\n",
        "            return_dict=return_dict,\n",
        "        )\n",
        "        hidden_states = transformer_outputs[0]  # (bs, seq_len, dim)\n",
        "        # hidden_states = self.dropout(hidden_states)\n",
        "        logits = self.classification_head(hidden_states[:, -1]).reshape(-1)  # (bs, num_labels)\n",
        "\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            labels = labels.to(logits.device).reshape(-1)\n",
        "            loss = self.loss_fn(logits, labels)\n",
        "\n",
        "        if not return_dict:\n",
        "            output = (logits,) + transformer_outputs[1:]\n",
        "            return ((loss,) + output) if loss is not None else output\n",
        "\n",
        "        return SequenceClassifierOutputWithPast(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            past_key_values=transformer_outputs.past_key_values,\n",
        "            hidden_states=transformer_outputs.hidden_states,\n",
        "            attentions=transformer_outputs.attentions,\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install numba\n",
        "\n",
        "# from numba import cuda\n",
        "\n",
        "# device = cuda.get_current_device(); device.reset()\n",
        "# device = cuda.get_current_device();"
      ],
      "metadata": {
        "id": "e_8GEhK9oR3A"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "\n",
        "# # Python의 가비지 컬렉터를 강제로 실행\n",
        "# gc.collect()\n",
        "\n",
        "# # PyTorch가 GPU 캐시에서 할당하지 않은 메모리를 비움\n",
        "# torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "BYEB1X5Zissh"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXS8x5G3U06B",
        "outputId": "a8c6b6b9-d976-41d8-8733-6bb9abd7f5ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/quantizers/auto.py:167: UserWarning: You passed `quantization_config` or equivalent parameters to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` from the model will be used.\n",
            "  warnings.warn(warning_msg)\n"
          ]
        }
      ],
      "source": [
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=torch.float16\n",
        ")\n",
        "\n",
        "base_model = MistralForDetectAI.from_pretrained(\n",
        "            '/content/drive/MyDrive/last',\n",
        "            num_labels=config['model']['num_labels'],  # 2\n",
        "            quantization_config=bnb_config,\n",
        "            low_cpu_mem_usage=True,\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # After determining if it's the best model or just a regular save...\n",
        "# save_model(base_model, tokenizer,'/content/drive/MyDrive/', is_best=False)"
      ],
      "metadata": {
        "id": "L3R-vO_puvmq"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "yg0ZfZ_UU2Kv"
      },
      "outputs": [],
      "source": [
        "base_model.config.pretraining_tp = 1\n",
        "for param in base_model.parameters():\n",
        "    if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):\n",
        "        param.data = param.data.to(torch.float32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "qAFRy1CXU3Wh"
      },
      "outputs": [],
      "source": [
        "# lora ---\n",
        "peft_config = LoraConfig(\n",
        "    r=config['model']['lora']['r'],\n",
        "    lora_alpha=config['model']['lora']['lora_alpha'],\n",
        "    lora_dropout=config['model']['lora']['lora_dropout'],\n",
        "    bias=\"none\",\n",
        "    task_type=TaskType.SEQ_CLS,\n",
        "    inference_mode=False,\n",
        "    target_modules=config[\"model\"][\"lora\"][\"target_modules\"],\n",
        "    modules_to_save=config[\"model\"][\"lora\"][\"modules_to_save\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GPS3qrNlU4gY",
        "outputId": "fa67fd46-77d3-4689-a398-dffce066d5cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda:0\n",
            "trainable params: 3,411,968 || all params: 7,171,751,936 || trainable%: 0.0476\n"
          ]
        }
      ],
      "source": [
        "model = get_peft_model(base_model, peft_config)\n",
        "print(model.device)\n",
        "model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSjbteFJU5YK",
        "outputId": "4bd2616d-8b9c-486a-ac1e-85b5cafd38b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'seed': 424,\n",
              " 'use_wandb': False,\n",
              " 'model': {'backbone_path': 'maywell/Mistral-ko-7B-v0.1',\n",
              "  'max_length': 1296,\n",
              "  'num_labels': 1,\n",
              "  'tokenizer': {'padding_side': 'left',\n",
              "   'truncation_side': 'left',\n",
              "   'use_fast': True},\n",
              "  'lora': {'target_modules': ['q_proj', 'k_proj'],\n",
              "   'r': 8,\n",
              "   'lora_alpha': 16,\n",
              "   'lora_dropout': 0.1,\n",
              "   'modules_to_save': ['classification_head', 'classifier', 'score']}},\n",
              " 'train_params': {'per_device_train_batch_size': 1,\n",
              "  'per_device_eval_batch_size': 1,\n",
              "  'num_train_epochs': 4,\n",
              "  'gradient_accumulation_steps': 4,\n",
              "  'warmup_pct': 0.1,\n",
              "  'eval_frequency': 500,\n",
              "  'patience': 20,\n",
              "  'save_trigger': 0.0,\n",
              "  'use_mask_aug': False,\n",
              "  'mask_aug_prob': 0.0},\n",
              " 'optimizer': {'name': 'AdamW8bit',\n",
              "  'head_lr': '2e-6',\n",
              "  'lr': '2e-5',\n",
              "  'weight_decay': '1e-2',\n",
              "  'max_grad_norm': 0.5},\n",
              " 'outputs': {'model_dir': '/content/drive/MyDrive/'},\n",
              " 'wandb': {'project': 'detect-ai-a1',\n",
              "  'run_name': 'exp010-r-detect',\n",
              "  'tags': ['mistral']}}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "z6byZ8FgVGC3"
      },
      "outputs": [],
      "source": [
        "def get_optimizer(cfg, model, print_fn=None):\n",
        "    _optimizers = {\n",
        "        \"Adam\": optim.Adam,\n",
        "        \"AdamW\": optim.AdamW,\n",
        "        \"AdamW8bit\": bnb.optim.Adam8bit,\n",
        "    }\n",
        "    assert cfg['optimizer']['name'] in _optimizers, f\"Optimizer {cfg['optimizer']['name']} not supported\"\n",
        "\n",
        "    no_decay = [\"bias\", \"LayerNorm.weight\"]\n",
        "    head_layer_name = \"classification_head\"\n",
        "\n",
        "    # start with all of the candidate parameters\n",
        "    param_dict = {name: param for name, param in model.named_parameters()}\n",
        "    # filter out those that do not require grad\n",
        "    param_dict = {name: param for name, param in param_dict.items() if param.requires_grad}\n",
        "\n",
        "    # head & body params\n",
        "    param_dict_head = {\n",
        "        name: param for name, param in param_dict.items() if head_layer_name in name\n",
        "    }\n",
        "    param_dict_body = {\n",
        "        name: param for name, param in param_dict.items() if head_layer_name not in name\n",
        "    }\n",
        "\n",
        "    # create groups ---\n",
        "    head_params_no_decay = [\n",
        "        param for name, param in param_dict_head.items() if any(nd in name for nd in no_decay)\n",
        "    ]\n",
        "    head_params_decay = [\n",
        "        param for name, param in param_dict_head.items() if not any(nd in name for nd in no_decay)\n",
        "    ]\n",
        "    body_params_no_decay = [\n",
        "        param for name, param in param_dict_body.items() if any(nd in name for nd in no_decay)\n",
        "    ]\n",
        "    body_params_decay = [\n",
        "        param for name, param in param_dict_body.items() if not any(nd in name for nd in no_decay)\n",
        "    ]\n",
        "\n",
        "    optim_groups = [\n",
        "        {'params': head_params_no_decay, 'lr': float(cfg['optimizer']['head_lr']), 'weight_decay': 0},\n",
        "        {'params': head_params_decay, 'lr': float(cfg['optimizer']['head_lr']), 'weight_decay': float(cfg['optimizer']['weight_decay'])},\n",
        "        {'params': body_params_no_decay, 'lr': float(cfg['optimizer']['lr']), 'weight_decay': 0},\n",
        "        {'params': body_params_decay, 'lr': float(cfg['optimizer']['lr']),\n",
        "            'weight_decay': float(cfg['optimizer']['weight_decay']) * 1e-1},  # less weight decay for body\n",
        "    ]\n",
        "\n",
        "    if print_fn is not None:\n",
        "        n_head_params_no_decay = sum(p.numel() for p in head_params_no_decay)\n",
        "        n_head_params_decay = sum(p.numel() for p in head_params_decay)\n",
        "        n_body_params_no_decay = sum(p.numel() for p in body_params_no_decay)\n",
        "        n_body_params_decay = sum(p.numel() for p in body_params_decay)\n",
        "\n",
        "        print_fn(f\"n_head_params_no_decay: {n_head_params_no_decay}\")\n",
        "        print_fn(f\"n_head_params_decay: {n_head_params_decay}\")\n",
        "        print_fn(f\"n_body_params_no_decay: {n_body_params_no_decay}\")\n",
        "        print_fn(f\"n_body_params_decay: {n_body_params_decay}\")\n",
        "\n",
        "    # Create AdamW optimizer and use the fused version if it is available\n",
        "    optimizer = _optimizers[cfg['optimizer']['name']](\n",
        "        optim_groups,\n",
        "        lr=float(cfg['optimizer']['lr']),\n",
        "        weight_decay=float(cfg['optimizer']['weight_decay']),\n",
        "    )\n",
        "    return optimizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1DvNdScVSOc",
        "outputId": "a9b7cb7a-f386-4a08-a021-82c44323fa7f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n_head_params_no_decay: 0\n",
            "n_head_params_decay: 4096\n",
            "n_body_params_no_decay: 0\n",
            "n_body_params_decay: 3407872\n"
          ]
        }
      ],
      "source": [
        "optimizer = get_optimizer(config, model, print_fn=print)\n",
        "#     optimizer = get_optimizer(cfg, model, print_fn=accelerator.print)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3tW_3i5VIrF",
        "outputId": "e9bbac93-3d3b-4365-aa55-b644d365faa4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# training updates per epoch: 800\n",
            "# training steps: 3200\n",
            "# warmup steps: 320\n"
          ]
        }
      ],
      "source": [
        "# ------- Scheduler -----------------------------------------------------------------#\n",
        "num_epochs = config['train_params']['num_train_epochs']\n",
        "warmup_pct = config['train_params']['warmup_pct']\n",
        "\n",
        "num_update_steps_per_epoch = len(train_dl)\n",
        "num_training_steps = num_epochs * num_update_steps_per_epoch\n",
        "num_warmup_steps = int(warmup_pct*num_training_steps)\n",
        "\n",
        "print(f\"# training updates per epoch: {num_update_steps_per_epoch}\")\n",
        "print(f\"# training steps: {num_training_steps}\")\n",
        "print(f\"# warmup steps: {num_warmup_steps}\")\n",
        "\n",
        "scheduler = get_cosine_schedule_with_warmup(\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=num_warmup_steps,\n",
        "    num_training_steps=num_training_steps\n",
        ")\n",
        "\n",
        "# ------- training setup --------------------------------------------------------------#\n",
        "best_lb = -1.\n",
        "save_trigger = config['train_params']['save_trigger']\n",
        "\n",
        "patience_tracker = 0\n",
        "current_iteration = 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Fl1s3fEcVK_L"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(predictions, truths):\n",
        "    \"\"\"\n",
        "    ROC AUC SCORE\n",
        "    \"\"\"\n",
        "\n",
        "    assert len(predictions) == len(truths)\n",
        "    score = roc_auc_score(truths, predictions)\n",
        "\n",
        "    to_return = {\n",
        "        \"lb\": round(score, 4),\n",
        "    }\n",
        "\n",
        "    return to_return\n",
        "\n",
        "def run_evaluation(model, valid_dl, valid_ids):\n",
        "    model.eval()\n",
        "\n",
        "    all_predictions = []\n",
        "    all_truths = []\n",
        "\n",
        "    progress_bar = tqdm(range(len(valid_dl)))\n",
        "\n",
        "    for step, batch in enumerate(valid_dl):\n",
        "        with torch.no_grad():\n",
        "            outputs = model(**batch)\n",
        "\n",
        "        logits = outputs.logits\n",
        "\n",
        "        predictions = torch.sigmoid(logits)\n",
        "\n",
        "        predictions, references = predictions, batch[\"labels\"].to(torch.long).reshape(-1)\n",
        "        predictions, references = predictions.cpu().numpy().tolist(), references.cpu().numpy().tolist()\n",
        "\n",
        "        all_predictions.extend(predictions)\n",
        "        all_truths.extend(references)\n",
        "\n",
        "        progress_bar.update(1)\n",
        "\n",
        "    progress_bar.close()\n",
        "\n",
        "    # compute metric\n",
        "    eval_dict = compute_metrics(all_predictions, all_truths)\n",
        "\n",
        "    result_df = pd.DataFrame()\n",
        "    result_df[\"id\"] = valid_ids\n",
        "    result_df[\"predictions\"] = all_predictions\n",
        "    result_df[\"truths\"] = all_truths\n",
        "\n",
        "    oof_df = deepcopy(result_df)\n",
        "    oof_df = oof_df.rename(columns={\"predictions\": \"generated\"})\n",
        "    oof_df = oof_df[[\"id\", \"generated\"]].copy()\n",
        "\n",
        "    to_return = {\n",
        "        \"scores\": eval_dict,\n",
        "        \"result_df\": result_df,\n",
        "        \"oof_df\": oof_df,\n",
        "    }\n",
        "\n",
        "    return to_return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "7sg7t9xDVVjq"
      },
      "outputs": [],
      "source": [
        "class AverageMeter(object):\n",
        "    \"\"\"Computes and stores the average and current value\n",
        "       Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "\n",
        "    def reset(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        self.val = val\n",
        "        self.sum += val * n\n",
        "        self.count += n\n",
        "        self.avg = self.sum / self.count\n",
        "\n",
        "def get_lr(optimizer):\n",
        "    return optimizer.param_groups[0]['lr']*1e6\n",
        "\n",
        "def as_minutes(s):\n",
        "    m = math.floor(s / 60)\n",
        "    s -= m * 60\n",
        "    return '%dm%ds' % (m, s)"
      ]
    },
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        },
        "id": "CybWWk8jVXbY",
        "outputId": "243a2dd1-4776-4c18-cfe8-b21761905021"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/800 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee8420344f53419b921fd130a94770ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "700f8cb23dd44e6db912ec9cf4f4efdf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 1 | Step 499 | Total Step 500 | Time: 9m19s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.963\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/800 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a59d818c86f740f49cd355b8ec2b0656"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "add8c096f4d14bc6b8d1df11c2178f6c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 2 | Step 199 | Total Step 1000 | Time: 18m53s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9947\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "501d7fb25b6c48e781f2f45960f8afa2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 2 | Step 699 | Total Step 1500 | Time: 28m18s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9951\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/800 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ce1637b53ac416681ac7ae7e17414f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0db62a222f5e433cabb6b6117366c81d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 3 | Step 399 | Total Step 2000 | Time: 37m30s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9978\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa3dd1c7df0a4a48a9cba6bff6cdb21c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0c64df3a9d143afbfc9206f50e79d51"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 4 | Step 99 | Total Step 2500 | Time: 47m4s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9969\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> patience reached 1/20\n",
            ">>> current best score: 0.9978\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f89ae9ba692a47629cc3478895437203"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 4 | Step 599 | Total Step 3000 | Time: 56m21s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9969\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> patience reached 2/20\n",
            ">>> current best score: 0.9978\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import gc\n",
        "\n",
        "# ------- training  --------------------------------------------------------------------#\n",
        "start_time = time.time()\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "\n",
        "    # Python의 가비지 컬렉터를 강제로 실행\n",
        "    gc.collect()\n",
        "\n",
        "    # PyTorch가 GPU 캐시에서 할당하지 않은 메모리를 비움\n",
        "    torch.cuda.empty_cache()\n",
        "    # close and reset progress bar\n",
        "    if epoch != 0:\n",
        "        progress_bar.close()\n",
        "\n",
        "    progress_bar = tqdm(range(num_update_steps_per_epoch))\n",
        "    loss_meter = AverageMeter()\n",
        "\n",
        "    # Training ------\n",
        "    model.train()\n",
        "\n",
        "    for step, batch in enumerate(train_dl):\n",
        "\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        progress_bar.update(1)\n",
        "        current_iteration += 1\n",
        "\n",
        "        # >--------------------------------------------------|\n",
        "        # >-- evaluation ------------------------------------|\n",
        "        # >--------------------------------------------------|\n",
        "\n",
        "        if current_iteration % config['train_params']['eval_frequency'] == 0:\n",
        "            # set model in eval mode\n",
        "            model.eval()\n",
        "            eval_response = run_evaluation(model, valid_dl, valid_ids)\n",
        "\n",
        "            scores_dict = eval_response[\"scores\"]\n",
        "            result_df = eval_response[\"result_df\"]\n",
        "            oof_df = eval_response[\"oof_df\"]\n",
        "\n",
        "            lb = scores_dict[\"lb\"]\n",
        "\n",
        "            print_line()\n",
        "            et = as_minutes(time.time()-start_time)\n",
        "\n",
        "            print(f\">>> Epoch {epoch+1} | Step {step} | Total Step {current_iteration} | Time: {et}\")\n",
        "            print_line()\n",
        "            print(f\">>> Current LB (AUC) = {round(lb, 4)}\")\n",
        "\n",
        "            print_line()\n",
        "\n",
        "            is_best = False\n",
        "            if lb >= best_lb:\n",
        "                best_lb = lb\n",
        "                is_best = True\n",
        "                patience_tracker = 0\n",
        "\n",
        "                # -----\n",
        "                best_dict = dict()\n",
        "                for k, v in scores_dict.items():\n",
        "                    best_dict[f\"{k}_at_best\"] = v\n",
        "            else:\n",
        "                patience_tracker += 1\n",
        "\n",
        "\n",
        "            if is_best:  # do in main process\n",
        "                oof_df.to_csv(os.path.join(config['outputs']['model_dir'], f\"oof_df_best.csv\"), index=False)\n",
        "                result_df.to_csv(os.path.join(config['outputs']['model_dir'], f\"result_df_best.csv\"), index=False)\n",
        "            else:\n",
        "                print(f\">>> patience reached {patience_tracker}/{config['train_params']['patience']}\")\n",
        "                print(f\">>> current best score: {round(best_lb, 4)}\")\n",
        "\n",
        "            oof_df.to_csv(os.path.join(config['outputs']['model_dir'], f\"oof_df_last.csv\"), index=False)\n",
        "            result_df.to_csv(os.path.join(config['outputs']['model_dir'], f\"result_df_last.csv\"), index=False)\n",
        "\n",
        "            # saving -----\n",
        "            unwrapped_model = model\n",
        "            unwrapped_model.save_pretrained(f\"{config['outputs']['model_dir']}/last\")\n",
        "\n",
        "            # debug --\n",
        "            # selected_adapters = list(unwrapped_model.peft_config.keys())\n",
        "            # print(f\"selected adapters: {selected_adapters}\")\n",
        "\n",
        "            # for adapter_name in selected_adapters:\n",
        "            #     peft_config = unwrapped_model.peft_config[adapter_name]\n",
        "            #     peft_config = asdict(peft_config)\n",
        "            #     print(f\"adapter: {adapter_name}\")\n",
        "            #     print(peft_config)\n",
        "            #     for k, v in peft_config.items():\n",
        "            #         print(f\"{k}: {v} ({type(v)})\")\n",
        "\n",
        "            # ------\n",
        "\n",
        "            tokenizer.save_pretrained(f\"{config['outputs']['model_dir']}/last\")\n",
        "\n",
        "            if best_lb > save_trigger:\n",
        "                # 모델 및 토크나이저를 저장하는 경로 설정\n",
        "                model_save_path = f\"{config['outputs']['model_dir']}/best\"\n",
        "                tokenizer_save_path = f\"{config['outputs']['model_dir']}/best\"\n",
        "\n",
        "                # 모델과 토크나이저 저장\n",
        "                model.save_pretrained(model_save_path)\n",
        "                tokenizer.save_pretrained(tokenizer_save_path)\n",
        "\n",
        "                # -- post eval\n",
        "                model.train()\n",
        "                torch.cuda.empty_cache()\n",
        "                print_line()\n",
        "\n",
        "                # early stopping ----\n",
        "                if patience_tracker >= config['train_params']['patience']:\n",
        "                    print(\"stopping early\")\n",
        "                    model.eval()\n",
        "                    break\n",
        "\n",
        "#     # --- end training\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "def save_model(model, tokenizer, model_dir, is_best=False):\n",
        "    \"\"\"\n",
        "    Save the model and tokenizer to the specified directory.\n",
        "\n",
        "    Args:\n",
        "    - model: The model to save.\n",
        "    - tokenizer: The tokenizer to save.\n",
        "    - model_dir: The base directory to save the model and tokenizer.\n",
        "    - is_best: If True, saves the model as the best model. Otherwise, saves as the last checkpoint.\n",
        "    \"\"\"\n",
        "    # Define save paths\n",
        "    model_save_path = os.path.join(model_dir, 'best' if is_best else 'last')\n",
        "    tokenizer_save_path = os.path.join(model_dir, 'best' if is_best else 'last')\n",
        "\n",
        "    # Create directories if they do not exist\n",
        "    os.makedirs(model_save_path, exist_ok=True)\n",
        "    os.makedirs(tokenizer_save_path, exist_ok=True)\n",
        "\n",
        "    # Save the model and tokenizer\n",
        "    model.save_pretrained(model_save_path)\n",
        "    tokenizer.save_pretrained(tokenizer_save_path)\n",
        "    print(f\"Model saved {'as best model' if is_best else 'as last checkpoint'} in {model_save_path}\")"
      ],
      "metadata": {
        "id": "Y_9j4AVnnN4I"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# After determining if it's the best model or just a regular save...\n",
        "save_model(model, tokenizer,'/content/drive/MyDrive/', is_best=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9rDyLELnPjh",
        "outputId": "acba4108-e8d3-45ed-c7e4-0d46af69d08e"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved as last checkpoint in /content/drive/MyDrive/last\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "bS-M9Bl1oTrx"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import unicodedata\n",
        "\n",
        "\n",
        "# pre-process -----\n",
        "char_to_remove = ['{', '£', '\\x97', '¹', 'å', '\\\\', '\\x85', '<', '\\x99', \\\n",
        "                  'é', ']', '+', 'Ö', '\\xa0', '>', '|', '\\x80', '~', '©', \\\n",
        "                  '/', '\\x93', '$', 'Ó', '²', '^', ';', '`', 'á', '*', '(', \\\n",
        "                  '¶', '®', '[', '\\x94', '\\x91', '#', '-', 'ó', ')', '}', '=']\n",
        "\n",
        "\n",
        "\n",
        "def preprocess_text(text, strategy='light'):\n",
        "    assert strategy in [\"none\", \"light\", \"heavy\"], \"pre-processing strategy must one of: none, light, heavy\"\n",
        "\n",
        "    if strategy == \"none\":\n",
        "        text = text\n",
        "\n",
        "    elif strategy == \"light\":\n",
        "        text = str(text)\n",
        "        text = text.strip()\n",
        "        text = text.strip(\"\\\"\")\n",
        "\n",
        "        for c in char_to_remove:\n",
        "            text = text.replace(c, \"\")\n",
        "\n",
        "        if text[-1]!=\".\":\n",
        "            text = text.split(\".\")\n",
        "            text = \".\".join(text[:-1])\n",
        "            text += \".\"\n",
        "    else:\n",
        "        text = unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('ascii')\n",
        "        text = text.lower()\n",
        "        text = re.sub(r'[^a-z0-9\\s.,;?!:()\\'\\\"%-]', '', text)\n",
        "        text = re.sub(r'\\s+', ' ', text).strip()\n",
        "\n",
        "    return text\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "infer_df = pd.read_csv('/content/korean_essay.csv')\n",
        "infer_df = infer_df[['생성 Text', 'model']]\n",
        "infer_df['label'] = infer_df['model'].apply(lambda x: 1 if 'gpt' in str(x).lower() else 0)\n",
        "infer_df = infer_df[['생성 Text','label']]\n",
        "infer_df.rename(columns={'생성 Text':'text'}, inplace = True)\n",
        "infer_df =infer_df.dropna(axis=0).reset_index(drop=True)\n",
        "infer_df['id'] = range(1, len(infer_df) + 1)\n",
        "\n",
        "infer_df['text'] = infer_df['text'].apply(lambda x: preprocess_text(x))\n",
        "infer_df.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "nB51seuyGjdM",
        "outputId": "7f96e105-b1fb-4962-a519-d20f4517b475"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-42-e3f72c3489c4>:1: DtypeWarning: Columns (0,1,2,3,5,6,7,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  infer_df = pd.read_csv('/content/korean_essay.csv')\n",
            "<ipython-input-42-e3f72c3489c4>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  infer_df.rename(columns={'생성 Text':'text'}, inplace = True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "495  제시문에서 '표현의 자유'는 인간의 이성을 기반으로 한 자유롭고 공개된 의견 교환을...      1  496\n",
              "48   가에 따르면 장애인이나 장노년층을 비롯한 취약 계층에서 정보 격차가 발생하고 있으\\...      0   49\n",
              "465  제시문 가를 선택하여 자료 1 분석:\\n\\n분석:\\n제시문 가는 언어가 논리적 시스...      1  466\n",
              "214  제시문 가와 다는 각각 여유와 예술교육의 중요성을 다루며 지속가능성의 관점에서 근본...      1  215\n",
              "349  푸생의 두 편의 아르카디아의 목자들에서 인물들은 죽음이라는 진실을 받아들이는 모습을...      1  350\n",
              "163  제시문 사는 사회 및 문화 현상의 연구에 대한 두 가지 접근 방식을 소개하고 있습니...      1  164\n",
              "356  다모가 범한 죄인 '술 담근 죄'는 사회적, 도덕적으로 매우 심각한 문제를 초래할 ...      1  357\n",
              "256  제시문 1에서 다루어진 정통과 이단에 대한 사회적 인식의 유동성은 사회적 가치관의 ...      1  257"
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              "  <div id=\"df-ffd14cb6-5084-406b-b3ed-a9423db0864f\" class=\"colab-df-container\">\n",
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              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>다모가 범한 죄인 '술 담근 죄'는 사회적, 도덕적으로 매우 심각한 문제를 초래할 ...</td>\n",
              "      <td>1</td>\n",
              "      <td>357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>제시문 1에서 다루어진 정통과 이단에 대한 사회적 인식의 유동성은 사회적 가치관의 ...</td>\n",
              "      <td>1</td>\n",
              "      <td>257</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ffd14cb6-5084-406b-b3ed-a9423db0864f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ffd14cb6-5084-406b-b3ed-a9423db0864f 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-ffd14cb6-5084-406b-b3ed-a9423db0864f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ee77e81e-7cd6-47e2-9146-9a129c5b3d25\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee77e81e-7cd6-47e2-9146-9a129c5b3d25')\"\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",
              "     width=\"24px\">\n",
              "    <g>\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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      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-ee77e81e-7cd6-47e2-9146-9a129c5b3d25 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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\\uc778\\uc2dd\\ud558\\uace0 \\uac00\\uce58 \\uc788\\uac8c \\ub9cc\\ub4e4 \\uc218 \\uc788\\ub294 \\ub2a5\\ub825\\uc744 \\uac1c\\ubc1c\\ud558\\ub294 \\ub370 \\ub3c4\\uc6c0\\uc744 \\uc900\\ub2e4. \\ub530\\ub77c\\uc11c, \\uc608\\uc220\\uad50\\uc721\\uc740 \\uc5ec\\uc720\\ub97c \\ud1b5\\ud574 \\uc5bb\\uc740 \\ub0b4\\uba74\\uc758 \\uae4a\\uc774\\uc640 \\ub9c8\\ucc2c\\uac00\\uc9c0\\ub85c, \\uac1c\\uc778\\uacfc \\uc0ac\\ud68c\\uac00 \\uc9c0\\uc18d\\uac00\\ub2a5\\ud55c \\ubc29\\uc2dd\\uc73c\\ub85c \\ubc1c\\uc804\\ud560 \\uc218 \\uc788\\ub294 \\uae30\\ubc18\\uc744 \\ub9c8\\ub828\\ud55c\\ub2e4.\\n\\n\\uc5ec\\uc720\\uc640 \\uc608\\uc220\\uad50\\uc721\\uc740 \\ubaa8\\ub450 \\uc778\\uac04\\uc774 \\uc790\\uc2e0\\uc758 \\ub0b4\\uba74\\uc138\\uacc4\\uc640 \\uc678\\ubd80 \\uc138\\uacc4 \\uc0ac\\uc774\\uc5d0\\uc11c \\uade0\\ud615\\uc744 \\uc774\\ub8e8\\uace0, \\uc790\\uc2e0\\uc758 \\uc0b6\\uc744 \\uc758\\ubbf8 \\uc788\\uace0 \\uc9c0\\uc18d\\uac00\\ub2a5\\ud55c \\ubc29\\uc2dd\\uc73c\\ub85c \\uc774\\ub04c\\uc5b4\\uac08 \\uc218 \\uc788\\ub3c4\\ub85d \\ud558\\ub294 \\ub370 \\uc911\\uc694\\ud55c \\uc5ed\\ud560\\uc744 \\ud55c\\ub2e4. \\uc5ec\\uc720\\uac00 \\uac1c\\uc778\\uc5d0\\uac8c \\uc790\\uae30 \\uc131\\ucc30\\uc758 \\uae30\\ud68c\\ub97c \\uc81c\\uacf5\\ud558\\uace0, \\uc608\\uc220\\uad50\\uc721\\uc774 \\uadf8 \\uc131\\ucc30\\uc744 \\ub354 \\uae4a\\uace0 \\ub113\\uc740 \\uc774\\ud574\\uc640 \\uc544\\ub984\\ub2e4\\uc6c0\\uc758 \\uc778\\uc2dd\\uc73c\\ub85c \\ud655\\uc7a5\\uc2dc\\ud0b4\\uc73c\\ub85c\\uc368, \\uc774 \\ub450 \\uc694\\uc18c\\ub294 \\uac1c\\uc778\\uc758 \\uc131\\uc7a5\\uacfc \\uc0ac\\ud68c\\uc758 \\ubc1c\\uc804\\uc5d0 \\ud544\\uc218\\uc801\\uc778 \\uc694\\uc18c\\ub85c \\uc791\\uc6a9\\ud55c\\ub2e4. \\uc774\\ub294 \\uc9c0\\uc18d\\uac00\\ub2a5\\ud55c \\ubc1c\\uc804\\uc758 \\ud575\\uc2ec\\uc774 \\ub2e8\\uc21c\\ud788 \\uacbd\\uc81c\\uc801 \\uc131\\uc7a5\\uc774\\ub098 \\uae30\\uc220\\uc801 \\uc9c4\\ubcf4\\uc5d0\\ub9cc \\uc788\\ub294 \\uac83\\uc774 \\uc544\\ub2c8\\ub77c, \\uc778\\uac04\\uc758 \\ub0b4\\uba74\\uc138\\uacc4\\uc640 \\uadf8\\uac00 \\uc18d\\ud55c \\uc0ac\\ud68c\\uc640\\uc758 \\uc870\\ud654\\ub85c\\uc6b4 \\uad00\\uacc4 \\uad6c\\ucd95\\uc5d0\\ub3c4 \\ud06c\\uac8c \\uc758\\uc874\\ud55c\\ub2e4\\ub294 \\uc0ac\\uc2e4\\uc744 \\ub4dc\\ub7ec\\ub0b8\\ub2e4. \\uacb0\\uad6d, \\uc5ec\\uc720\\uc640 \\uc608\\uc220\\uad50\\uc721\\uc740 \\uc9c0\\uc18d\\uac00\\ub2a5\\ud55c \\ubbf8\\ub798\\ub97c \\ud5a5\\ud55c \\uc5ec\\uc815\\uc5d0\\uc11c \\uac1c\\uc778\\uacfc \\uc0ac\\ud68c\\uac00 \\ud568\\uaed8 \\ub098\\uc544\\uac08 \\uc218 \\uc788\\ub294 \\ubc29\\ud5a5\\uc744 \\uc81c\\uc2dc\\ud55c\\ub2e4.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 153,\n        \"min\": 49,\n        \"max\": 496,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          357,\n          177\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "infer_ds = dataset_creator.get_dataset(infer_df)\n",
        "infer_ds = infer_ds.sort(\"input_length\")\n",
        "tokenizer = dataset_creator.tokenizer\n",
        "infer_ds.set_format(\n",
        "        type=None,\n",
        "        columns=[\n",
        "            'id',\n",
        "            'input_ids',\n",
        "            'attention_mask',\n",
        "        ]\n",
        "    )\n",
        "\n",
        "infer_ids = infer_ds[\"id\"]  # .tolist()\n",
        "\n",
        "#--\n",
        "data_collator = AiCollator(\n",
        "    tokenizer=tokenizer,\n",
        "    pad_to_multiple_of=64\n",
        ")\n",
        "\n",
        "infer_dl = DataLoader(\n",
        "    infer_ds,\n",
        "    batch_size=1,\n",
        "    shuffle=False,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "\n",
        "#----------\n",
        "for b in infer_dl:\n",
        "    break\n",
        "show_batch(b, tokenizer, task='infer', print_fn=print)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303,
          "referenced_widgets": [
            "7bb2f91c70d243b4a225b7ca535448d8",
            "b1580d528b4d48b3b50cbcb5f21dc8fd",
            "8c0b97bc19b846828d809f18f1cd660d",
            "ce0308617e4a428b82e9533e31f48180",
            "2f3e1e6499844b6fbede6eee5a6eb1cf",
            "1a36c5ca4868412c8921b3f7c2a1772f",
            "2780236ef1534ebd8593ddfc0634dae5",
            "9a59be5b9774467887f76e6bb0ea793d",
            "c7026a096bce43b483095a954f963605",
            "3637739726944c5492b10612b42b8821",
            "ad223edb0890442aaaa85d2b30677426",
            "b9185f35a0b74edaa6cf32b39abdc29a",
            "7ccb64ceadae4fdf847fcfffbcab5509",
            "df0d56e80e2a4a42a2d289f913cf9bf6",
            "9f27207a8108458bb9847b0109fc2411",
            "fedbdf9bbd7940faa4bbd84172287ccd",
            "43dfa2abb4334d9fb0a86d2c8f4022ce",
            "f45745bc405245f39fef1d52263cac3d",
            "64661180f2a143b19336d321d8f2f277",
            "461d3d270c1a42be9caa91e5f3076664",
            "fb460c1e2f4a432eb0fe3011a970b224",
            "2a2731b6e2df4d1a957cb30ae4fefa4d"
          ]
        },
        "id": "gbe7FSB7lAKc",
        "outputId": "5d3895ae-af61-4cb5-ba09-24fa17b0760d"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/501 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7bb2f91c70d243b4a225b7ca535448d8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/501 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9185f35a0b74edaa6cf32b39abdc29a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "batch size: 1\n",
            "shape of input_ids: torch.Size([1, 128])\n",
            "Showing 1 from a infer batch...\n",
            "\n",
            "\n",
            "\n",
            "Example 1\n",
            "Input:\n",
            "\n",
            "</s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s><s> 제시문 A는 의사소통이 개인의 삶의 질, 건강, 웰빙에 미치는 중요성을 강조하며, 긍정적인 인간관계가 특히 건강에 미치는 긍정적인 영향을 다룹니다. 제시문 B는 직장 내 의사소통 부재가 갈등을 유발하고 조직에 해를 끼칠 수 있다는 점을 강조합니다. 이 두 제시문은 의사소통의 중요성이 개인의 건강과 조직의 효율성에 큰 영향을 미친다는 공통된 논지를 가지고 있습니다.\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def run_inference(model, infer_dl, example_ids):\n",
        "    model.eval()\n",
        "    all_predictions = []\n",
        "\n",
        "    progress_bar = tqdm(range(len(infer_dl)))\n",
        "\n",
        "    for step, batch in enumerate(infer_dl):\n",
        "        with torch.no_grad():\n",
        "            outputs = model(**batch)\n",
        "\n",
        "        logits = outputs.logits.reshape(-1)\n",
        "        predictions = torch.sigmoid(logits)\n",
        "        predictions = predictions.cpu().numpy().tolist()\n",
        "\n",
        "        all_predictions.extend(predictions)\n",
        "\n",
        "        progress_bar.update(1)\n",
        "    progress_bar.close()\n",
        "\n",
        "    result_df = pd.DataFrame()\n",
        "    result_df[\"id\"] = example_ids\n",
        "    result_df[\"generated\"] = all_predictions\n",
        "\n",
        "    return result_df"
      ],
      "metadata": {
        "id": "wjD-Xku5lcmj"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 훈련 1시간, inference : 500개 3분 30초"
      ],
      "metadata": {
        "id": "6HxDTfGRYE-C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sub_df = run_inference(model, infer_dl, infer_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2dcf023012f34fc7bc1fc60cb792a475",
            "6c17d1a34cd94fa9aab0353165a2284b",
            "cc59030331d340a18a33e006503a64cf",
            "c37109d79cfb406983e1a6a303ec24de",
            "96f051dac43441aaa29f3af50d891937",
            "89c70eb0f63c4843ac3fe34d0161eb74",
            "d296cfe0b588453db9f0e606d842ab70",
            "4bea07d6df174334ba2463cadf95b41c",
            "670e2b839441486f9719360caa1ae04d",
            "f0ac291fed364a8e8f1b68eba35fc495",
            "565eae0e8ce5430bba4e3ef8444db7c7"
          ]
        },
        "id": "rQj5wRVwos9O",
        "outputId": "2aeb21e4-dd13-4777-817e-32467c5b40b5"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/501 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2dcf023012f34fc7bc1fc60cb792a475"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 'id' 열을 기준으로 두 DataFrame 병합\n",
        "merged_df = pd.merge(infer_df, sub_df, on='id')\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8OPnw0C0poud",
        "outputId": "0f291fbf-2021-4516-b9b8-5e475d18155a"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  label  id  generated\n",
              "0  제시문 가는 역사적으로 새로운 종교를 창설하려는 시도가 실패로 끝났음에도 불구하고,...      1   1   0.998379\n",
              "1  제시문 가2에서는 돈 키호테와 산초의 대화를 통해 현실과 환상 사이의 갈등이 나타나...      1   2   0.997278\n",
              "2  주제어로 선택된 공통 키워드는 \"아름다움\"입니다.\\n\\n제시문 가에서는 매화의 아름...      1   3   0.998391\n",
              "3  네 제시문들의 순서를 정하기 위해서는 각 제시문이 개인과 사회 간 영향을 어떻게 다...      1   4   0.965115\n",
              "4  네 제시문들에는 현대 사회에서의 갈등 상황에 대한 다양한 해법과 접근 방식이 내포되...      1   5   0.999348"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79422fae-6dbf-4ad5-b400-eadca7bc7f1e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "      <th>id</th>\n",
              "      <th>generated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>제시문 가는 역사적으로 새로운 종교를 창설하려는 시도가 실패로 끝났음에도 불구하고,...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.998379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>제시문 가2에서는 돈 키호테와 산초의 대화를 통해 현실과 환상 사이의 갈등이 나타나...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.997278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>주제어로 선택된 공통 키워드는 \"아름다움\"입니다.\\n\\n제시문 가에서는 매화의 아름...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.998391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>네 제시문들의 순서를 정하기 위해서는 각 제시문이 개인과 사회 간 영향을 어떻게 다...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.965115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>네 제시문들에는 현대 사회에서의 갈등 상황에 대한 다양한 해법과 접근 방식이 내포되...</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.999348</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79422fae-6dbf-4ad5-b400-eadca7bc7f1e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-79422fae-6dbf-4ad5-b400-eadca7bc7f1e 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-79422fae-6dbf-4ad5-b400-eadca7bc7f1e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8ac0e9b5-d2d6-4f96-8349-7e95a8b5dc42\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8ac0e9b5-d2d6-4f96-8349-7e95a8b5dc42')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      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-8ac0e9b5-d2d6-4f96-8349-7e95a8b5dc42 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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\\ub3c5\\uc11c\\ubc95\\uc740 \\uc81c\\uc2dc\\ubb38 \\uac00\\uc5d0\\uc11c \\uc9c0\\uc801\\ud55c \\uc798\\ubabb\\ub41c \\ub3c5\\uc11c \\ud0dc\\ub3c4\\uc640\\ub294 \\ub2ec\\ub9ac, \\ub3c5\\uc11c\\uc758 \\uc9c4\\uc815\\ud55c \\ubaa9\\uc801\\uacfc \\uac00\\uce58\\ub97c \\uc2e4\\ud604\\ud558\\ub294 \\ub370 \\uae30\\uc5ec\\ud558\\ub294 \\ubc14\\ub78c\\uc9c1\\ud55c \\uc811\\uadfc \\ubc29\\uc2dd\\uc73c\\ub85c \\ud574\\uc11d\\ub420 \\uc218 \\uc788\\ub2e4.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 144,\n        \"min\": 1,\n        \"max\": 501,\n        \"num_unique_values\": 501,\n        \"samples\": [\n          363,\n          74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"generated\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2926087158341628,\n        \"min\": 0.001191436662338674,\n        \"max\": 0.9999234676361084,\n        \"num_unique_values\": 496,\n        \"samples\": [\n          0.9766214489936829,\n          0.5103887319564819\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 실제 라벨\n",
        "y_true = merged_df['label'].values\n",
        "y_preds = merged_df['generated'].values\n",
        "\n",
        "# ROC 곡선 그리기\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_preds)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# ROC 곡선 시각화\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "\n",
        "# ROC-AUC 면적 출력\n",
        "print(\"ROC-AUC 면적:\", roc_auc)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "VourwOJXp1ou",
        "outputId": "87aba6cd-7415-4839-d052-e69c4f663dd0"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC-AUC 면적: 0.9599153439153438\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "# F1 스코어 계산\n",
        "y_pred = [1 if score > 0.5 else 0 for score in y_preds]  # fixed_results는 모델 예측 결과로 가정\n",
        "f1 = f1_score(y_true,  y_pred)\n",
        "\n",
        "# F1 스코어 출력\n",
        "print(\"F1 Score:\", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWEJHABnGn5j",
        "outputId": "ee91b156-5b03-478a-a9ff-100a34e5d9c4"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1 Score: 0.9102244389027431\n"
          ]
        }
      ]
    }
  ],
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