Mistral_Lora_7B.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AtIoHZrLbPmq",
        "outputId": "53dd07d4-04ac-4930-d423-50f22d78959e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Thu Mar 28 06:18:01 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   35C    P0              49W / 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"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBt10q-Omfrv",
        "outputId": "a738af3f-0cba-4de5-82c2-43adcaf5aba6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PoTMsMmMFCzR",
        "outputId": "c6e7595e-1266-488e-addb-d8ee141d8e82"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.8)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
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            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.2)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "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) (3.6)\n",
            "korean-essay.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "translated-d4.zip: Skipping, found more recently modified local copy (use --force to force download)\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": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDB9ax76QlrK",
        "outputId": "c6befb76-d7d4-4259-abef-8279d9520f50"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple/\n",
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          ]
        }
      ],
      "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": null,
      "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": null,
      "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": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfgsIf6sSDPT",
        "outputId": "df4f2b34-f622-4fa4-de48-127fef9b98f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Overwriting /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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vyfv2CptSHLA",
        "outputId": "db84c111-5ed6-491f-b215-03e275e91827"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xiyCofhsSIdS",
        "outputId": "1c5e8cb3-8d5e-4de4-8414-976feaf9d1ef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "2p6wPkvvSJuG",
        "outputId": "92b85f22-9697-4a17-9d68-609a4f03808f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "shape of train data: (800, 3)\n"
          ]
        },
        {
          "data": {
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\\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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              "      <td>실제로는 전혀 그렇지 않은 이상한 형태의 암석을 보면 왜 모두가 화성에 생명체가 있...</td>\n",
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              "\n",
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            ],
            "text/plain": [
              "                                                text  label   id\n",
              "0  친애하는 교장 선생님,\\n\\n저는 모든 학생들이 지역사회 봉사를 수행하도록 의무화해...      0  283\n",
              "1  실제로는 전혀 그렇지 않은 이상한 형태의 암석을 보면 왜 모두가 화성에 생명체가 있...      0  187\n",
              "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"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "shape of validation data: (100, 3)\n"
          ]
        },
        {
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            "application/vnd.google.colaboratory.intrinsic+json": {
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\\uc219\\uc81c\\ub294 \\ud55c \\ud559\\uc0dd\\uc5d0\\uac8c\\ub294 \\ub9ce\\uc740 \\uc2dc\\uac04\\uc774 \\uac78\\ub9ac\\uc9c0 \\uc54a\\uc9c0\\ub9cc \\ub2e4\\ub978 \\ud559\\uc0dd\\uc740 \\uacfc\\uc81c\\ub97c \\uc218\\ud589\\ud558\\ub294 \\ub370 \\uc5b4\\ub824\\uc6c0\\uc744 \\uacaa\\uc744 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\uadf8\\ub807\\uae30 \\ub54c\\ubb38\\uc5d0 \\ud559\\uc0dd\\uc774 \\uacfc\\uc678 \\ud65c\\ub3d9\\uae4c\\uc9c0 \\ubcd1\\ud589\\ud55c\\ub2e4\\uba74 \\ud070 \\uae30\\ub300\\ub97c \\ud558\\uae30\\ub294 \\uc5b4\\ub824\\uc6b8 \\uac83\\uc785\\ub2c8\\ub2e4.\\n\\n\\ub9ce\\uc740 \\uc5f0\\uad6c\\uc5d0 \\ub530\\ub974\\uba74 \\uc9c0\\ub09c \\uc138\\ub300\\uc758 \\ud559\\uc0dd\\ub4e4\\uc740 \\uacfc\\ud559\\uc790\\ub4e4\\uc774 \\uba87 \\ub144 \\ub3d9\\uc548 \\ubcf8 \\uac83 \\uc911 \\uac00\\uc7a5 \\ub9ce\\uc740 \\uc591\\uc758 \\uc2a4\\ud2b8\\ub808\\uc2a4\\ub97c \\uac00\\uc9c0\\uace0 \\uc788\\ub294 \\uac83\\uc73c\\ub85c \\ub098\\ud0c0\\ub0ac\\uc2b5\\ub2c8\\ub2e4. \\uad34\\ub86d\\ud798, \\uac00\\uc815 \\uc0dd\\ud65c, \\ud559\\uad50 \\uacf5\\ubd80, \\uc815\\uc2e0 \\uac74\\uac15 \\ub610\\ub294 \\ub192\\uc740 \\uae30\\ub300 \\ub4f1\\uc73c\\ub85c \\uc778\\ud574 \\uc2a4\\ud2b8\\ub808\\uc2a4\\uac00 \\ubc1c\\uc0dd\\ud558\\ub354\\ub77c\\ub3c4 \\uc2a4\\ud2b8\\ub808\\uc2a4\\ub294 \\ubaa8\\ub4e0 \\uc5f0\\ub839\\ub300\\uc758 \\ud559\\uc0dd\\ub4e4\\uc5d0\\uac8c \\uac00\\uc7a5 \\ud070 \\ubd88\\uc548\\uc744 \\uc720\\ubc1c\\ud569\\ub2c8\\ub2e4. \\uc911\\ud559\\uc0dd\\uc740 \\uace0\\ub4f1\\ud559\\uad50 \\uc9c4\\ud559\\uc744 \\uac71\\uc815\\ud558\\uace0, \\uace0\\ub4f1\\ud559\\uc0dd\\uc740 \\ub300\\ud559 \\uc9c4\\ud559\\uc744 \\uac71\\uc815\\ud569\\ub2c8\\ub2e4. \\ubaa8\\ub4e0 \\ud559\\uc0dd\\ub4e4\\uc740 \\uac71\\uc815\\uc744 \\ud569\\ub2c8\\ub2e4. \\ube44\\ub85d \\uadf8 \\uac71\\uc815\\uc774 \\ube44\\ud569\\ub9ac\\uc801\\uc77c\\uc9c0\\ub77c\\ub3c4 \\uadf8\\ub4e4\\uc740 \\uc5ec\\uc804\\ud788 \\u200b\\u200b\\uc2a4\\ud2b8\\ub808\\uc2a4\\ub97c \\ubc1b\\uc2b5\\ub2c8\\ub2e4. \\uadf8\\ub807\\uae30 \\ub54c\\ubb38\\uc5d0 \\uacfc\\uc678 \\ud65c\\ub3d9\\uc744 \\ucd94\\uac00\\ud558\\ub294 \\uac83\\uc740 \\ud559\\uc0dd\\ub4e4\\uc5d0\\uac8c \\ub108\\ubb34 \\ub9ce\\uc740 \\uac83\\uc744 \\uc694\\uad6c\\ud560 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4.\\n\\n\\uc804\\ubc18\\uc801\\uc73c\\ub85c \\uacfc\\uc678 \\ud65c\\ub3d9\\uc744 \\uc758\\ubb34\\ud654\\ud558\\ub294 \\uac83\\uc740 \\ud6cc\\ub96d\\ud574 \\ubcf4\\uc774\\uace0 \\uc7a5\\uc810\\ub3c4 \\ub9ce\\uc9c0\\ub9cc, \\uc790\\uc138\\ud788 \\uc0b4\\ud3b4\\ubcf4\\uba74 \\uacb0\\uad6d \\ub9dd\\ud560 \\uc77c\\uc774\\ub77c\\ub294 \\uac83\\uc744 \\uc0ac\\ub78c\\ub4e4\\uc774 \\uae68\\ub2eb\\uac8c \\ub429\\ub2c8\\ub2e4. \\ucd5c\\uc120\\uc744 \\ub2e4\\ud560 \\uc2dc\\uac04\\uacfc \\uc5d0\\ub108\\uc9c0\\uac00 \\uc788\\ub294 \\ud559\\uc0dd\\ub4e4\\uc5d0\\uac8c\\ub294 \\uacfc\\uc678 \\ud65c\\ub3d9\\uc774 \\uc120\\ud0dd \\uc0ac\\ud56d\\uc73c\\ub85c \\uc720\\uc9c0\\ub418\\uc5b4\\uc57c \\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 \\uacfc\\uc815\\uc5d0 \\ub354 \\uc720\\uc775\\ud569\\ub2c8\\ub2e4.\\n\\n\\uc120\\uac70\\uc778\\ub2e8 \\uc81c\\ub3c4\\ub294 \\ud0c0\\ud611\\uc73c\\ub85c \\ucc3d\\uc124\\ub418\\uc5c8\\uc73c\\uba70 \\ub9ce\\uc740 \\uc0ac\\ub78c\\ub4e4\\uc758 \\ub3d9\\uc758\\ub97c \\ubc1b\\uc558\\uc2b5\\ub2c8\\ub2e4. \\uc0ac\\ub78c\\ub4e4\\uc774 \\uc120\\uac70\\uc778\\ub2e8 \\uc81c\\ub3c4\\uc5d0 \\ub300\\ud574 \\uc2eb\\uc5b4\\ud558\\ub294 \\uc810 \\uc911 \\ud558\\ub098\\ub294 \\uadf8\\uac83\\uc774 \\\"\\uc2b9\\uc790 \\ub3c5\\uc2dd\\\" \\uc2dc\\uc2a4\\ud15c\\uacfc \\uac19\\ub2e4\\ub294 \\uac83\\uc785\\ub2c8\\ub2e4. \\uc65c\\ub0d0\\ud558\\uba74 \\ud6c4\\ubcf4\\uc790\\ub4e4\\uc740 \\uc790\\uc2e0\\uc774 \\uc2b9\\ub9ac\\ud558\\uc9c0 \\ubabb\\ud560 \\uac83\\uc774\\ub77c\\ub294 \\uac83\\uc744 \\uc54c\\uace0 \\uc788\\ub294 \\uc8fc\\uc5d0\\uc11c \\uc2dc\\uac04\\uc744 \\ubcf4\\ub0b4\\uc9c0 \\uc54a\\uae30 \\ub54c\\ubb38\\uc785\\ub2c8\\ub2e4. \\ud558\\uc9c0\\ub9cc \\uc2b9\\uc790\\ub3c5\\uc2dd \\uccb4\\uc81c\\ub77c\\uba74 \\uc774\\uacac \\uc5c6\\uc774 \\ud655\\uc2e4\\ud55c \\uacb0\\uacfc\\ub97c \\uc5bb\\uc744 \\uc218 \\uc788\\uc744 \\uac83\\uc785\\ub2c8\\ub2e4. \\uc774\\ub294 \\ud6c4\\ubcf4\\uc790\\uc5d0\\uac8c \\ud574\\ub2f9 \\uc8fc\\uc5d0\\uc11c \\uc555\\ub3c4\\uc801\\uc778 \\uc120\\uac70 \\ud22c\\ud45c \\uc2b9\\ub9ac\\ub97c \\uc81c\\uacf5\\ud569\\ub2c8\\ub2e4. \\ub610, \\uae30\\uc0ac\\\"\\n\\n\\uc120\\uac70\\uc778\\ub2e8 \\uc81c\\ub3c4\\ub97c \\uc639\\ud638\\ud558\\ub2e4: \\uba78\\uc2dc\\ubc1b\\ub294 \\ub300\\ud1b5\\ub839 \\uc120\\ucd9c \\ubc29\\uc2dd\\uc744 \\uace0\\uc218\\ud574\\uc57c \\ud558\\ub294 \\ub2e4\\uc12f \\uac00\\uc9c0 \\uc774\\uc720\\n\\n\\\"\\uacfc\\ubc18\\uc218 \\ub4dd\\ud45c\\ub97c \\uc5bb\\uc740 \\ud6c4\\ubcf4\\uac00 \\uc5c6\\uc744 \\ub54c \\uacb0\\uc120\\ud22c\\ud45c\\uc5d0 \\ub300\\ud55c \\uc555\\ub825\\uc740 \\uc5c6\\uc2b5\\ub2c8\\ub2e4. \\ub300\\ud1b5\\ub839 \\uc120\\uac70 \\uacfc\\uc815\\uc744 \\ud06c\\uac8c \\ubcf5\\uc7a1\\ud558\\uac8c \\ub9cc\\ub4dc\\ub294 \\uc555\\ub825\\uc740 \\ud56d\\uc0c1 \\ud655\\uc2e4\\ud55c \\uc2b9\\uc790\\ub97c \\ubc30\\ucd9c\\ud558\\ub294 \\uc120\\uac70\\uc778\\ub2e8 \\uc81c\\ub3c4\\uc5d0 \\uc758\\ud574 \\uac10\\uc18c\\ub429\\ub2c8\\ub2e4.\\\" \\\"\\n\\n\\uc0ac\\ub78c\\ub4e4\\uc774 \\uac00\\uc9c0\\uace0 \\uc788\\ub294 \\uc120\\uac70\\uc778\\ub2e8 \\uc81c\\ub3c4\\uc5d0 \\ub300\\ud55c \\ub610 \\ub2e4\\ub978 \\uc6b0\\ub824\\ub294 \\uc720\\uad8c\\uc790\\ub4e4\\uc774 \\ub300\\ud1b5\\ub839\\uc5d0\\uac8c \\ud22c\\ud45c\\ud558\\ub294 \\uac83\\uc774 \\uc544\\ub2c8\\ub77c \\ub300\\ud1b5\\ub839\\uc744 \\uc120\\ucd9c\\ud558\\ub294 \\uc120\\uac70\\uc778\\ub2e8\\uc5d0\\uac8c \\ud22c\\ud45c\\ud55c\\ub2e4\\ub294 \\uac83\\uc785\\ub2c8\\ub2e4. \\uadf8\\ub7ec\\ub098 \\uac01 \\uc815\\ub2f9\\uc740 \\ud574\\ub2f9 \\uc815\\ub2f9\\uc758 \\ud6c4\\ubcf4\\uc790\\uc5d0\\uac8c \\ud22c\\ud45c\\ud560 \\uc120\\uac70\\uc778\\ub2e8\\uc744 \\uc120\\ud0dd\\ud569\\ub2c8\\ub2e4. \\uc6b0\\ub9ac \\uad6d\\ubbfc\\uc740 \\uc120\\uac70\\uc778\\ub2e8\\uc744 \\uc120\\ud0dd\\ud560 \\uc218 \\uc788\\uc73c\\ubbc0\\ub85c \\uc790\\uc2e0\\uc774 \\uc120\\ud0dd\\ud55c \\ub300\\ud1b5\\ub839\\uc5d0\\uac8c \\ud22c\\ud45c\\ud560 \\uc0ac\\ub78c\\uc744 \\uc120\\ud0dd\\ud560 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\uc774\\ub294 \\uacf5\\uc815\\ud569\\ub2c8\\ub2e4. \\uadf8\\ub9ac\\uace0, \\uadc0\\ud558\\uac00 \\ud22c\\ud45c\\ud558\\uc9c0 \\uc54a\\uc740 \\ud6c4\\ubcf4\\uc790\\uac00 \\uc120\\uac70\\uc778\\ub2e8 \\ud22c\\ud45c\\uc5d0\\uc11c \\uc2b9\\ub9ac\\ud55c\\ub2e4\\uace0 \\ud574\\uc11c \\uadf8\\ub4e4\\uc774 \\uc804\\uad6d \\uc778\\uae30 \\ud22c\\ud45c\\uc5d0\\uc11c \\uc2b9\\ub9ac\\ud55c\\ub2e4\\ub294 \\uc758\\ubbf8\\ub294 \\uc544\\ub2d9\\ub2c8\\ub2e4. \\uc608\\ub97c \\ub4e4\\uc5b4, 2000\\ub144\\uc5d0 \\uace0\\uc5b4\\ub294 \\ubd80\\uc2dc\\ubcf4\\ub2e4 \\ub354 \\ub9ce\\uc740 \\ub300\\uc911 \\ud22c\\ud45c\\ub97c \\ubc1b\\uc558\\uc9c0\\ub9cc \\uc120\\uac70\\uc778\\ub2e8 \\ud22c\\ud45c\\ub294 \\ub354 \\uc801\\uc5c8\\uace0 \\uc6b0\\ub9ac \\ubaa8\\ub450\\ub294 \\ubd80\\uc2dc\\uac00 \\uc5ec\\uc804\\ud788 \\uc2b9\\ub9ac\\ud588\\ub2e4\\ub294 \\uac83\\uc744 \\uc54c\\uace0 \\uc788\\uc2b5\\ub2c8\\ub2e4.\\n\\n\\uc120\\uac70\\uc778\\ub2e8 \\uc81c\\ub3c4\\ub294 \\ub610\\ud55c \\ud22c\\ud45c \\uacfc\\uc815\\uc5d0\\uc11c \\ud070 \\uc5ed\\ud560\\uc744 \\ud560 \\\"\\uc2a4\\uc719(swing)\\\" \\uc8fc\\ub97c \\uc81c\\uacf5\\ud569\\ub2c8\\ub2e4. \\ub300\\ubd80\\ubd84\\uc758 \\ud6c4\\ubcf4\\uc790\\ub4e4\\uc740 \\uc2b9\\uc790\\ub3c5\\uc2dd \\uccb4\\uc81c\\uc774\\uae30 \\ub54c\\ubb38\\uc5d0 \\ub358\\uc9c0\\ub294 \\uc8fc\\uc5d0 \\uc120\\uac70\\uc6b4\\ub3d9\\uc744 \\uc9d1\\uc911\\ud569\\ub2c8\\ub2e4. \\uacbd\\ud569\\uc8fc\\uc5d0\\uc11c\\ub294 \\ucc28\\uae30 \\ub300\\ud1b5\\ub839\\uc774 \\ub204\\uad6c\\uc778\\uc9c0 \\uacb0\\uc815\\ud569\\ub2c8\\ub2e4. \\uadf8\\ub4e4\\uc740 \\uc774\\ubbf8 \\ud22c\\ud45c\\ub97c \\ubc1b\\uc744 \\uc8fc\\ub97c \\uc54c\\uace0 \\uc788\\uc9c0\\ub9cc \\uacbd\\ud569 \\uc8fc\\uc5d0\\uc11c\\ub294 \\uacb0\\ucf54 \\uc77c\\uad00\\uc131\\uc774 \\uc5c6\\uc73c\\uba70 \\ud56d\\uc0c1 \\uc120\\uac70\\uc5d0\\uc11c \\ub204\\uad70\\uac00\\ub97c \\uc774\\uae41\\ub2c8\\ub2e4. \\ub610\\ud55c, \\uc778\\uad6c\\uac00 \\ub9ce\\uc740 \\uc8fc\\ub294 \\ub354 \\ub9ce\\uc740 \\uc120\\uac70\\uc778\\ub2e8\\uc744 \\ubcf4\\uc720\\ud558\\uace0 \\uc788\\uc73c\\uba70, \\uc774\\ub85c \\uc778\\ud574 \\uc120\\uac70\\uc778\\ub2e8\\uc740 \\ud5cc\\ubc95\\uc5d0\\uc11c \\uaddc\\uc815\\ud55c \\uc0c1\\uc6d0\\uc758 \\uc798\\ubabb\\ub41c \\ubc30\\ubd84\\uc73c\\ub85c \\uc778\\ud574 \\uc815\\uce58\\uc801 \\uade0\\ud615\\uc5d0 \\uc5b4\\ub290 \\uc815\\ub3c4 \\ube44\\uc911\\uc744 \\ud68c\\ubcf5\\ud558\\uac8c \\ub429\\ub2c8\\ub2e4.\\n\\n\\uc804\\uccb4\\uc801\\uc73c\\ub85c \\uc120\\uac70\\uc778\\ub2e8\\uc740 \\uc774\\ub7ec\\ud55c \\uc0c1\\ud669\\uc5d0\\uc11c \\ubd80\\uc815\\uc801\\uc778 \\uc810\\ubcf4\\ub2e4 \\ub354 \\ub9ce\\uc740 \\uae0d\\uc815\\uc801\\uc778 \\uc810\\uc744 \\uac00\\uc9c0\\uace0 \\uc788\\uc2b5\\ub2c8\\ub2e4. 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              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-959e1ca5-7b20-4cab-b2b1-3a86d967b834\" 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",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-959e1ca5-7b20-4cab-b2b1-3a86d967b834')\"\n",
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              "\n",
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              "    .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",
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              "      display: none;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-959e1ca5-7b20-4cab-b2b1-3a86d967b834 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-959e1ca5-7b20-4cab-b2b1-3a86d967b834');\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",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --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",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      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-651c5342-a91f-42af-871e-e7c09dfb7299 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275,
          "referenced_widgets": [
            "45a5f1c9328344f3a93cfdec7da29d00",
            "8021d7990cb24a729d52b7f870e83e8e",
            "45385a9d8b7048b997b18d457cfedae3",
            "1150a21eaafb498c85ab6945a0a5e3c6",
            "08c51c44f76547be89c72c089eccf763",
            "18d801d470544bb4841e18376c9c02c4",
            "9f8590c5bf1d4402a3d34aedaf215c32",
            "90d14dc6cb5346c6a542a7a3ab1967f5",
            "4840ef274fbc4907ac10e5498f8d12d9",
            "67e8480346ba4bb5ae985c7ccb229305",
            "c0fcdfa686264a17bf1dc4203d59d863",
            "e28f743705af47318b224b208f75f15d",
            "32dd436d4df243c0a1581478df300a18",
            "a8c2270309fb4c46a6271876298f56a4",
            "d621348400bb4ffb8c1e8702c41718e8",
            "2d48f0e68a6e41f98ea7f6aef48346c8",
            "59e0c9d5e13c4af8ad6a60321ac02067",
            "9244748b973e444aa9f5e39d21218338",
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            "1d613cf767fb44acb6a83b2b37df8d76",
            "b4cc494e7e9746f0ba410949027022a5",
            "cfa24bea44604f96bb5102e35f3305b1",
            "9e9a802e290e4798b8e9f8bc806e8fe2",
            "4fe0c726d4b34846bd09776f2cb7f217",
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            "e294137c82b84b3c86f00b090a814b70",
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            "71bf3b87b00a414994cf3b3ee79de45f",
            "283d70f0eb9f4a76ada5a52865c95aa9",
            "fe1f6527afab431e9ebad3709025d44f",
            "706f12f6bbdf40fd9eec513b95dcdaa6"
          ]
        },
        "id": "uAFU5CPPUjSm",
        "outputId": "fc7012d4-6131-4892-e10c-d4db7fba2de4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "45a5f1c9328344f3a93cfdec7da29d00",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/800 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e28f743705af47318b224b208f75f15d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/800 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d613cf767fb44acb6a83b2b37df8d76",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e294137c82b84b3c86f00b090a814b70",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": null,
      "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": null,
      "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": null,
      "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": null,
      "metadata": {
        "id": "kL1LavCKUtap"
      },
      "outputs": [],
      "source": [
        "data_collator = AiCollator(\n",
        "    tokenizer=tokenizer,\n",
        "    pad_to_multiple_of=64\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "id": "w4Jfd9zzUwLw"
      },
      "outputs": [],
      "source": [
        "data_collator_train = AiCollatorTrain(\n",
        "    tokenizer=tokenizer,\n",
        "    pad_to_multiple_of=64,\n",
        "    kwargs=dict(cfg=config)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3GIo9OFUyEv",
        "outputId": "4144c3f3-753c-4785-d240-e343aab89990"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "data preparation done...\n",
            "batch size: 1\n",
            "shape of input_ids: torch.Size([1, 384])\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> 친애하는 교장 선생님,\n",
            "\n",
            "학생들이 학교 스포츠와 활동에 참여하기 위해 평균 B 등급이 필요한 것은 매우 불공평하다고 생각합니다! 우선 운동을 정말 잘하는 아이들이 평균 B등급이 아니면 학교의 명예를 위해 필요한데, 운동을 정말 못하는 A, B등급 학생들만 스포츠 활동에 참여한다면 어떨까요? 이것은 확실히 우리 학교의 평판을 망칠 것입니다. 또한 평균 B등급 이하의 일부 학생들은 때때로 이 새로운 정책에 대해 겁을 먹습니다. 우리는 이 새롭고 불공정한 정책을 정말로 취소해야 합니다! 그리고 활동은 어떻습니까!? 학생들이 바이올린을 정말 잘 연주하고 평균 B등급 미만이면 어떻게 되나요? 그들도 그것에 대해 겁을 먹을 것입니다! 그들은 새로운 취미를 찾는 것 외에는 아무것도 할 수 없다는 사실에 겁을 먹을 것입니다! 장담하는데, 학교 콘서트 당일에는 절반의 사람들이 떠날 것입니다. 왜 물어보나요? 왜냐면 어쩌면 어떤 사람들은 바이올린 연주법을 기억하지 못할 수도 있으니까요! 이제 내 생각에는 당신이 어떻게 물어보는 것 같아요. 글쎄요, 당신의 정책이 그런 식이라면 사람들은 성적과 숙제에 정신을 집중해야 할 것입니다!!!!!! 그러니 그 새로운 정책이 여러분 자신과 학교를 당혹스럽게 한다고 상상해보세요! 그래서 왜 안돼?\n",
            "\n",
            "어서 학교를 난처하게 만드세요! 그것은 우리의 문제가 아니라 당신의 문제입니다! 학생들은 모두 동의하지 않지만 교수진은 모두 동의합니다! 이제 학생 대 s. 학부. 모든 학생들은 이 정책에 동의하지 않습니다.\n",
            "\n",
            "심지어 일부 부모도 동의하지 않습니다. 우리 학생들은 성적에 대해 걱정하지 않고 스포츠와 악기를 연주하고 싶어합니다. 시간 내 주셔서 감사합니다.\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": null,
      "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",
      "execution_count": null,
      "metadata": {
        "id": "e_8GEhK9oR3A"
      },
      "outputs": [],
      "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();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BYEB1X5Zissh"
      },
      "outputs": [],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXS8x5G3U06B",
        "outputId": "ad22419a-5325-4b04-fe7c-966785b8be30"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/quantizers/auto.py:155: 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",
      "execution_count": null,
      "metadata": {
        "id": "L3R-vO_puvmq"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GPS3qrNlU4gY",
        "outputId": "65ba0f04-ed38-4844-8d31-2c5d4464e28f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cuda:0\n",
            "trainable params: 3,411,968 || all params: 7,171,751,936 || trainable%: 0.04757509783450491\n"
          ]
        }
      ],
      "source": [
        "model = get_peft_model(base_model, peft_config)\n",
        "print(model.device)\n",
        "model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSjbteFJU5YK",
        "outputId": "b5e3f643-ed3c-4a3c-fc8a-aac71f540552"
      },
      "outputs": [
        {
          "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']}},\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': '/kaggle/working/'},\n",
              " 'wandb': {'project': 'detect-ai-a1',\n",
              "  'run_name': 'exp010-r-detect',\n",
              "  'tags': ['mistral']}}"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1DvNdScVSOc",
        "outputId": "34b03bb2-d3ba-4627-cf2c-fd1b002be47b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3tW_3i5VIrF",
        "outputId": "efcc3168-f606-4255-92e8-e1ecd209be6c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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": null,
      "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": null,
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "CybWWk8jVXbY",
        "outputId": "64559c9f-c834-4bd8-fd2a-3adfc9f11704"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50e31f0f486146c7bf3d29f6653725c1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/800 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0ad428f9fa04f27b72a8260a66395e5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 1 | Step 499 | Total Step 1500 | Time: 9m15s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.996\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "311a85c2f72446b3a0decd9594746e11",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/800 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50d4a15c150b4493bb2e0a07746f730d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 2 | Step 199 | Total Step 2000 | Time: 18m42s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9969\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cefeec677293407182dcc230fce5f96a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 2 | Step 699 | Total Step 2500 | Time: 28m6s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9969\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ed49c1e83c7404485f1bb922ea95028",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/800 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc5e6e2063cc4226b8a88717f1ce349c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 3 | Step 399 | Total Step 3000 | Time: 37m40s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9964\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> patience reached 1/20\n",
            ">>> current best score: 0.9969\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "56a2811f79974f7e9468ba37c405201f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/800 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6a63f39061ae4917829ba7989091f207",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 4 | Step 99 | Total Step 3500 | Time: 46m49s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9964\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> patience reached 2/20\n",
            ">>> current best score: 0.9969\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "03b0b69e39d645b3a68f42749a552438",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 4 | Step 599 | Total Step 4000 | Time: 56m14s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9964\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> patience reached 3/20\n",
            ">>> current best score: 0.9969\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "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",
      "execution_count": null,
      "metadata": {
        "id": "Y_9j4AVnnN4I"
      },
      "outputs": [],
      "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}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9rDyLELnPjh",
        "outputId": "dc898182-36ea-4c08-e498-505fb98e3cfa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model saved as last checkpoint in /content/drive/MyDrive/last\n"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "0OUXwMXkhUhz",
        "outputId": "f6f64e1d-27ef-4753-8d79-d5d1f9ecc877"
      },
      "outputs": [
        {
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              "\n",
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              "      <th></th>\n",
              "      <th>text</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>가에서는 폭력의 정의를 다양한 관점에서 살펴보고 있습니다. 기존의 폭력 정의에 의문...</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>가 제시문은 생명 복제 기술의 문제점에 대하여 논하고 있다. 인간의 고유성, 복제 ...</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>257</th>\n",
              "      <td>제시문 1의 가와 나는 현대 사회에서 상품화의 문제를 실감나게 보여줍니다. 가에서는...</td>\n",
              "      <td>1</td>\n",
              "      <td>258</td>\n",
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              "      <th>267</th>\n",
              "      <td>제시문 가의 허친과 제시문 다의 간디는 모두 현대 사회의 문제점에 대해 깊은 우려를...</td>\n",
              "      <td>1</td>\n",
              "      <td>268</td>\n",
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              "      <th>186</th>\n",
              "      <td>가와 나의 공통점은 다양성과 포용성에 대한 이야기를 담고 있다는 점입니다. 가에서는...</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>노자의 관점에서는 자율성을 중요시하여 거대국가보다는 작은 국가를 지지합니다. 이에 ...</td>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>제시문 나에서 스페인은 실물화폐의 가치를 절대적으로 맹신함으로서 참사를 경험한 사례...</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
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              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>제시문 가는 동물과 인간의 감정은 동일성을 가지고 있으며, 인간의 생각과 감정이 우...</td>\n",
              "      <td>0</td>\n",
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              "      <th>219</th>\n",
              "      <td>제시문 나는 정 진사와 한 여행자가 울음에 대한 관점을 논의하는 대화를 다루고 있습...</td>\n",
              "      <td>1</td>\n",
              "      <td>220</td>\n",
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              "      <th>45</th>\n",
              "      <td>제시문 가는 디지털 기기를 쉽게 접할 수 있게 됨으로서 문해력이 퇴화되고 있음을 시...</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\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",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      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-117fdefd-6cd1-4482-b646-b36f8b4a5198 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "text/plain": [
              "                                                  text  label   id\n",
              "43   가에서는 폭력의 정의를 다양한 관점에서 살펴보고 있습니다. 기존의 폭력 정의에 의문...      1   44\n",
              "11   가 제시문은 생명 복제 기술의 문제점에 대하여 논하고 있다. 인간의 고유성, 복제 ...      0   12\n",
              "257  제시문 1의 가와 나는 현대 사회에서 상품화의 문제를 실감나게 보여줍니다. 가에서는...      1  258\n",
              "267  제시문 가의 허친과 제시문 다의 간디는 모두 현대 사회의 문제점에 대해 깊은 우려를...      1  268\n",
              "186  가와 나의 공통점은 다양성과 포용성에 대한 이야기를 담고 있다는 점입니다. 가에서는...      1  187\n",
              "92   노자의 관점에서는 자율성을 중요시하여 거대국가보다는 작은 국가를 지지합니다. 이에 ...      1   93\n",
              "27   제시문 나에서 스페인은 실물화폐의 가치를 절대적으로 맹신함으로서 참사를 경험한 사례...      0   28\n",
              "36   제시문 가는 동물과 인간의 감정은 동일성을 가지고 있으며, 인간의 생각과 감정이 우...      0   37\n",
              "219  제시문 나는 정 진사와 한 여행자가 울음에 대한 관점을 논의하는 대화를 다루고 있습...      1  220\n",
              "45   제시문 가는 디지털 기기를 쉽게 접할 수 있게 됨으로서 문해력이 퇴화되고 있음을 시...      0   46"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "infer_df = pd.read_excel('/content/korean_essay.xlsx')\n",
        "infer_df = infer_df[['생성 Text', 'model']]\n",
        "infer_df['label'] = infer_df['model'].apply(lambda x: 1 if 'gpt' in 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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321,
          "referenced_widgets": [
            "ef902e2945ca46c4b0a025950af72866",
            "a1d660f51caa4e76a3e43a4a6c1f0797",
            "84f62e2fc96c4691bc65295ab5557d83",
            "5c898e265c1d416a99459ff9d918eb8a",
            "d9d240c8d0684762ab289314a56fe833",
            "44cba53ca3e346689d141f2dd07c2ffa",
            "7f5b269384e741f9b5007974ba10f47e",
            "c47552c633bf4386aed0263b64fc6054",
            "0a2e502ebe1d4bfca21928a5174f153f",
            "7cbc04ccc7a04a0eb7b3bfcad338adf6",
            "ccfcf134ba404a3080e2d21355ab5a90",
            "cd00543052cf4e78a637a706ae74eb43",
            "172029a9609f4def989ee23eea2a7091",
            "0146ca4480694c738ec8269c508bd193",
            "23b776c4da244b838e304ff599706141",
            "742d393c7ab5464f8a2e70fdce113bcc",
            "724e56359b7c44f9b06845327c857ab2",
            "d7148dfd951c462b9ede88279636cc57",
            "f81c3b2d579e4f41937fdf8ccd3f5ee8",
            "95df697df1cc4ccc8c41273eeb9763b9",
            "38e809fc770246b1829fda21c15326da",
            "3857dececb9e405d818045e61c557055"
          ]
        },
        "id": "gbe7FSB7lAKc",
        "outputId": "46ce3c42-dcd0-4502-c4b3-7ee72030c369"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ef902e2945ca46c4b0a025950af72866",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/296 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cd00543052cf4e78a637a706ae74eb43",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/296 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "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> 가의 주제를 나타내는 단어는 ‘편견’이다. 첫 번째 예에서 엘리자베스는 첫인상만으로 다아시를 오만한 남자로 간주한다. 그러나 편견을 버린 후 그녀는 그가 너그럽고 사려 깊은 인물임을\n",
            "알게 된다. 두 번째 예는 장애인에 대한 편견이 잘못된 것임을 보여준다. 태민이는 청각 장애2등급임에도 불구하고 남을 먼저 배려하겠다는 공약을 내세워 전교 회장에 당선된다.\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wjD-Xku5lcmj"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "8464dc3d6db7480391442d2b6296e8c2",
            "872edc37c13f44edb63a1a7ee42469f8",
            "a41b6424783a4df98541ac24ad84b350",
            "b173e697929c4fe9872eb4787ded8c7c",
            "b1a967ed66684621ad4a0e9a97c18f6c",
            "0218974620d24662a296b57b2371adad",
            "ef912158eb7648adb02395c9b2235a77",
            "9f44b53c5cb2412f8355f2ee3f28c221",
            "c87dd0aa5eaf410e80179cc183cef1c1",
            "b5f246c68b854c0cbb36dce420188aa6",
            "ebe3601b91af434fb43c084a928b4c13"
          ]
        },
        "id": "rQj5wRVwos9O",
        "outputId": "0dd3f0aa-f950-4ddf-e7a5-599b51b21093"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8464dc3d6db7480391442d2b6296e8c2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/296 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sub_df = run_inference(model, infer_dl, infer_ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8OPnw0C0poud",
        "outputId": "f6eeaccc-36f6-4d39-ee8b-e9ac3348da2f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "type": "dataframe",
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            "text/html": [
              "\n",
              "  <div id=\"df-f5621da5-a282-46d1-9f52-2a7f24abb61d\" 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",
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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>0.995598</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>0.998202</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>0.997298</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>0.967120</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>0.999529</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-f5621da5-a282-46d1-9f52-2a7f24abb61d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </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",
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              "      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-f5621da5-a282-46d1-9f52-2a7f24abb61d 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-f5621da5-a282-46d1-9f52-2a7f24abb61d');\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",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    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",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    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-f678606e-3100-43a4-bbe7-b1975e35fee8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                text  label  id  generated\n",
              "0  제시문 가는 역사적으로 새로운 종교를 창설하려는 시도가 실패로 끝났음에도 불구하고,...      1   1   0.995598\n",
              "1  제시문 가2에서는 돈 키호테와 산초의 대화를 통해 현실과 환상 사이의 갈등이 나타나...      1   2   0.998202\n",
              "2  주제어로 선택된 공통 키워드는 \"아름다움\"입니다.\\n\\n제시문 가에서는 매화의 아름...      1   3   0.997298\n",
              "3  네 제시문들의 순서를 정하기 위해서는 각 제시문이 개인과 사회 간 영향을 어떻게 다...      1   4   0.967120\n",
              "4  네 제시문들에는 현대 사회에서의 갈등 상황에 대한 다양한 해법과 접근 방식이 내포되...      1   5   0.999529"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 'id' 열을 기준으로 두 DataFrame 병합\n",
        "merged_df = pd.merge(infer_df, sub_df, on='id')\n",
        "merged_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "VourwOJXp1ou",
        "outputId": "c0f29324-7722-420a-a816-c31fb1af621f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC-AUC 면적: 0.9864399597436304\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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()"
      ]
    }
  ],
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