Mistral_1600_Train_Epoch4.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AtIoHZrLbPmq",
        "outputId": "5a24dc58-903c-4500-b9fc-21859df0637f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue May 28 12:18:30 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM4-40GB          Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   30C    P0              41W / 400W |      2MiB / 40960MiB |      0%      Default |\n",
            "|                                         |                      |             Disabled |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBt10q-Omfrv",
        "outputId": "ee3bf9ac-e557-41f3-9433-4dc0f4365bb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "_MqdRYDtFMVM"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "\n",
        "\n",
        "kaggle_json = './drive/MyDrive/kaggle.json'\n",
        "with open(kaggle_json, 'r') as f:\n",
        "    json_data = json.load(f)\n",
        "    os.environ['KAGGLE_USERNAME'] = json_data['username']\n",
        "    os.environ['KAGGLE_KEY'] = json_data['key']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PoTMsMmMFCzR",
        "outputId": "4ada507f-7273-4ced-b658-3c8af33318c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.14)\n",
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            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.7)\n",
            "Dataset URL: https://www.kaggle.com/datasets/umgeeyo/korean-essay\n",
            "License(s): unknown\n",
            "Downloading korean-essay.zip to /content\n",
            " 95% 1.00M/1.06M [00:00<00:00, 1.10MB/s]\n",
            "100% 1.06M/1.06M [00:00<00:00, 1.15MB/s]\n",
            "Dataset URL: https://www.kaggle.com/datasets/umgeeyo/translated-d4\n",
            "License(s): unknown\n",
            "Downloading translated-d4.zip to /content\n",
            " 98% 73.0M/74.3M [00:06<00:00, 17.2MB/s]\n",
            "100% 74.3M/74.3M [00:06<00:00, 12.4MB/s]\n"
          ]
        }
      ],
      "source": [
        "!pip install kaggle --upgrade\n",
        "\n",
        "\n",
        "!kaggle datasets download -d umgeeyo/korean-essay\n",
        "!kaggle datasets download -d umgeeyo/translated-d4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Ht8AfVhGD6H6"
      },
      "outputs": [],
      "source": [
        "# 압축 해제 후 input 에 train.csv test.csv  넣기\n",
        "import zipfile\n",
        "\n",
        "zipfile.ZipFile(f'/content/korean-essay.zip').extractall()\n",
        "zipfile.ZipFile(f'/content/translated-d4.zip').extractall()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDB9ax76QlrK",
        "outputId": "4e8454ba-1e0d-43c1-b8cd-f5c4bb5f50dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.19.1 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n",
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            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
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            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate) (12.5.40)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install -q bitsandbytes\n",
        "!pip install -q transformers\n",
        "!pip install -q accelerate\n",
        "!pip install -q peft\n",
        "!pip install -i https://pypi.org/simple/ bitsandbytes\n",
        "!pip install datasets\n",
        "!pip install accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "NH34_UsVR5Dg"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import time\n",
        "import yaml\n",
        "import torch\n",
        "import random\n",
        "import pandas as pd\n",
        "import torch.nn as nn\n",
        "from torch import optim\n",
        "from copy import deepcopy\n",
        "import bitsandbytes as bnb\n",
        "from tqdm.auto import tqdm\n",
        "from datasets import Dataset\n",
        "from accelerate import Accelerator\n",
        "from torch.utils.data import DataLoader\n",
        "from dataclasses import dataclass, field\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from transformers import AutoConfig, AutoModel\n",
        "from typing import List, Optional, Tuple, Union\n",
        "from peft import (LoraConfig, TaskType, get_peft_model,\n",
        "                  prepare_model_for_kbit_training)\n",
        "\n",
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "from transformers import (AutoModelForSequenceClassification,\n",
        "                          BitsAndBytesConfig, get_cosine_schedule_with_warmup)\n",
        "\n",
        "from transformers.modeling_outputs import SequenceClassifierOutputWithPast\n",
        "from transformers.models.mistral.modeling_mistral import (\n",
        "    MistralModel, MistralPreTrainedModel)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "1ya9yEvER61g"
      },
      "outputs": [],
      "source": [
        "def get_tokenizer(cfg):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\n",
        "        cfg['model']['backbone_path'],\n",
        "        use_fast=cfg['model']['tokenizer']['use_fast'],\n",
        "        padding_side=cfg['model']['tokenizer']['padding_side'],\n",
        "        truncation_side=cfg['model']['tokenizer']['truncation_side'],\n",
        "    )\n",
        "\n",
        "    # if the eos token is an empty string, we assign it to a token\n",
        "    if tokenizer.eos_token == \"\":\n",
        "        tokenizer.add_special_tokens({\"eos_token\": \"</s>\"})\n",
        "        tokenizer.eos_token = \"</s>\"\n",
        "\n",
        "    if tokenizer.pad_token is None:\n",
        "        if tokenizer.unk_token is not None:\n",
        "            tokenizer.pad_token = tokenizer.unk_token\n",
        "        else:\n",
        "            tokenizer.pad_token = tokenizer.eos_token\n",
        "    return tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "RMuP5mv_SA5M"
      },
      "outputs": [],
      "source": [
        "class AiDataset:\n",
        "    def __init__(self, cfg):\n",
        "        self.cfg = cfg\n",
        "        self.tokenizer = get_tokenizer(cfg)\n",
        "\n",
        "    def tokenize_function(self, examples):\n",
        "        tz = self.tokenizer(\n",
        "            examples[\"text\"],\n",
        "            padding=False,\n",
        "            truncation=True,\n",
        "            max_length=self.cfg['model']['max_length'],\n",
        "            add_special_tokens=True,\n",
        "        )\n",
        "\n",
        "        return tz\n",
        "\n",
        "    def compute_input_length(self, examples):\n",
        "        return {\"input_length\": [len(x) for x in examples[\"input_ids\"]]}\n",
        "\n",
        "#     def preprocess_function(self, df):\n",
        "#         df['text'] = df['text'].apply(lambda x: x.strip() + \"\\n###\\nIs the essay generated by AI?\")\n",
        "#         return df\n",
        "\n",
        "    def get_dataset(self, df):\n",
        "        \"\"\"\n",
        "        :param df: input dataframe\n",
        "        :type df: pd.DataFrame\n",
        "        :return: the created dataset\n",
        "        :rtype: Dataset\n",
        "        \"\"\"\n",
        "        df = deepcopy(df)\n",
        "        # df = self.preprocess_function(df)\n",
        "        task_dataset = Dataset.from_pandas(df)\n",
        "\n",
        "        task_dataset = task_dataset.map(self.tokenize_function, batched=True)\n",
        "        task_dataset = task_dataset.map(self.compute_input_length, batched=True)\n",
        "\n",
        "        return task_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfgsIf6sSDPT",
        "outputId": "93ec6a7e-a88a-4137-ce11-655bde65df72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing /conf_r_detect_mix_v16.yaml\n"
          ]
        }
      ],
      "source": [
        "%%writefile /conf_r_detect_mix_v16.yaml\n",
        "\n",
        "seed: 424\n",
        "use_wandb: false\n",
        "\n",
        "model:\n",
        "    backbone_path: maywell/Mistral-ko-7B-v0.1\n",
        "    max_length: 1296\n",
        "    num_labels: 1\n",
        "\n",
        "    tokenizer:\n",
        "        padding_side: left\n",
        "        truncation_side: left\n",
        "        use_fast: true\n",
        "\n",
        "    lora:\n",
        "        target_modules:\n",
        "          - q_proj\n",
        "          - k_proj\n",
        "        r: 8\n",
        "        lora_alpha: 16\n",
        "        lora_dropout: 0.1\n",
        "        modules_to_save:\n",
        "            - classification_head\n",
        "\n",
        "train_params:\n",
        "    per_device_train_batch_size: 1 # run on 4x A100\n",
        "    per_device_eval_batch_size: 1\n",
        "    num_train_epochs: 4 # 16\n",
        "    gradient_accumulation_steps: 4\n",
        "\n",
        "    warmup_pct: 0.1\n",
        "    eval_frequency: 500\n",
        "    patience: 20\n",
        "    save_trigger: 0.0\n",
        "\n",
        "    use_mask_aug: false # false\n",
        "    mask_aug_prob: 0.0\n",
        "\n",
        "optimizer:\n",
        "    name: AdamW8bit\n",
        "    head_lr: 2e-6\n",
        "    lr: 2e-5\n",
        "    weight_decay: 1e-2\n",
        "    max_grad_norm: 0.5\n",
        "\n",
        "outputs:\n",
        "    model_dir: /content/drive/MyDrive/\n",
        "\n",
        "wandb:\n",
        "    project: detect-ai-a1\n",
        "    run_name: exp010-r-detect\n",
        "    tags:\n",
        "    - mistral"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vyfv2CptSHLA",
        "outputId": "98cefbe7-be54-436d-eef7-4e41485e7e12"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'seed': 424, 'use_wandb': False, 'model': {'backbone_path': 'maywell/Mistral-ko-7B-v0.1', 'max_length': 1296, 'num_labels': 1, 'tokenizer': {'padding_side': 'left', 'truncation_side': 'left', 'use_fast': True}, 'lora': {'target_modules': ['q_proj', 'k_proj'], 'r': 8, 'lora_alpha': 16, 'lora_dropout': 0.1, 'modules_to_save': ['classification_head']}}, 'train_params': {'per_device_train_batch_size': 1, 'per_device_eval_batch_size': 1, 'num_train_epochs': 4, 'gradient_accumulation_steps': 4, 'warmup_pct': 0.1, 'eval_frequency': 500, 'patience': 20, 'save_trigger': 0.0, 'use_mask_aug': False, 'mask_aug_prob': 0.0}, 'optimizer': {'name': 'AdamW8bit', 'head_lr': '2e-6', 'lr': '2e-5', 'weight_decay': '1e-2', 'max_grad_norm': 0.5}, 'outputs': {'model_dir': '/content/drive/MyDrive/'}, 'wandb': {'project': 'detect-ai-a1', 'run_name': 'exp010-r-detect', 'tags': ['mistral']}}\n"
          ]
        }
      ],
      "source": [
        "# YAML 파일 경로\n",
        "yaml_file_path = \"/conf_r_detect_mix_v16.yaml\"\n",
        "\n",
        "def read_yaml(yaml_file_path):\n",
        "    with open(yaml_file_path, \"r\") as file:\n",
        "        config = yaml.safe_load(file)\n",
        "    return config\n",
        "\n",
        "# YAML 파일 읽기\n",
        "config = read_yaml(yaml_file_path)\n",
        "print(config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xiyCofhsSIdS",
        "outputId": "30f0876d-a3a2-448a-994a-32e879228fec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Set Length: 1600\n",
            "Validation Set Length: 200\n",
            "Test Set Length: 200\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[:2000]\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": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "2p6wPkvvSJuG",
        "outputId": "98b9f05f-59a1-4971-e751-15271e8ad787"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of train data: (1600, 3)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "                                                text  label    id\n",
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              "2  저자는 그것이 얼마나 위험한지, 그리고 왜 우리가 금성을 계속 연구해야 하는지에 대...      0   698\n",
              "3  집에서 학교에 가는 것은 생각할 수 있는 가장 좋은 아이디어 중 하나인 것 같습니다...      0   934\n",
              "4  여름 프로젝트는 학생들이 방학 중에 학습을 계속할 수 있는 좋은 방법입니다. 나는 ...      1   786"
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0c8887e6-4f34-43ca-9fbc-58150cff6604 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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            "shape of validation data: (200, 3)\n"
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              "                                                text  label    id\n",
              "0  항해하는 카우보이가 된다는 것은 단순히 동물을 돌보고 동물의 마구간을 청소하는 것 ...      0   850\n",
              "1  저는 교장 선생님께서 학생들이 학교에 전화기를 가지고 와서 점심시간이나 자유시간에 ...      0  1772\n",
              "2  안녕 내 이름은 알렉스 야. 저는 제가 관심을 갖고 있는 사업을 관찰하고 더 많이 ...      1   374\n",
              "3  어떤 학생에게는 스포츠와 학교가 너무 과중하기 때문에 평균 B학점을 받은 학생은 스...      0  1898\n",
              "4  높은 목표와 낮은 목표를 설정하는 것은 수년간 논쟁의 주제였습니다. 어떤 사람들은 ...      1   396"
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\\uc788\\uc2b5\\ub2c8\\ub2e4. \\ub610\\ud55c, \\uc790\\uc2e0\\uc758 \\uc548\\uc804 \\uc9c0\\ub300\\uc5d0\\uc11c \\ubc97\\uc5b4\\ub098\\ub294 \\uac83\\uc740 \\uc548\\uc804 \\uc9c0\\ub300\\uc5d0 \\uba38\\ubb3c\\ub800\\ub2e4\\uba74 \\uac00\\uc9c8 \\uc218 \\uc5c6\\uc5c8\\ub358 \\uc0c8\\ub85c\\uc6b4 \\uae30\\ud68c\\uc640 \\uacbd\\ud5d8\\uc73c\\ub85c \\uc774\\uc5b4\\uc9c8 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4.\\n\\n\\uadf8\\ub7ec\\ub098 \\ub192\\uc740 \\ubaa9\\ud45c\\ub97c \\uc124\\uc815\\ud558\\ub294 \\ub370\\uc5d0\\ub294 \\uba87 \\uac00\\uc9c0 \\uc7a0\\uc7ac\\uc801\\uc778 \\ub2e8\\uc810\\uc774 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\ud55c \\uac00\\uc9c0 \\uc7a0\\uc7ac\\uc801\\uc778 \\ub2e8\\uc810\\uc740 \\ub192\\uc740 \\ubaa9\\ud45c\\ub97c \\uc124\\uc815\\ud558\\uba74 \\ubaa9\\ud45c\\ub97c \\ub2ec\\uc131\\ud558\\uc9c0 \\ubabb\\ud560 \\uacbd\\uc6b0 \\uc2e4\\ud328\\uac10\\uacfc \\uc2e4\\ub9dd\\uac10\\uc744 \\ub290\\ub084 \\uc218 \\uc788\\ub2e4\\ub294 \\uac83\\uc785\\ub2c8\\ub2e4. \\uc774\\ub294 \\uc758\\uc695\\uc744 \\ub5a8\\uc5b4\\ub728\\ub9ac\\uace0 \\ubaa9\\ud45c\\ub97c \\uc644\\uc804\\ud788 \\ud3ec\\uae30\\ud558\\uac8c \\ub9cc\\ub4e4 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\ub610\\ud55c, \\ub192\\uc740 \\ubaa9\\ud45c\\ub97c \\uc124\\uc815\\ud558\\ub294 \\uac83\\uc740 \\uc2a4\\ud2b8\\ub808\\uc2a4\\ub97c \\uc904 \\uc218 \\uc788\\uc73c\\uba70, \\ud544\\uc694\\ud55c \\uc218\\uc900\\uc758 \\uaddc\\uc728\\uacfc \\uc9d1\\uc911\\ub825\\uc744 \\uc720\\uc9c0\\ud560 \\uc218 \\uc5c6\\uc73c\\uba74 \\ud0c8\\uc9c4\\uc73c\\ub85c \\uc774\\uc5b4\\uc9c8 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4.\\n\\n\\uacb0\\ub860\\uc801\\uc73c\\ub85c, \\ub192\\uc740 \\ubaa9\\ud45c\\ub97c \\uc124\\uc815\\ud558\\ub294 \\uac83\\uc740 \\uc5ec\\ub7ec \\uba74\\uc5d0\\uc11c \\uc720\\uc775\\ud560 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\uc774\\ub294 \\ub354 \\ud070 \\uc131\\ucde8\\uac10\\uc744 \\uac00\\uc838\\ub2e4\\uc8fc\\uace0, \\uc790\\uc2e0\\uc758 \\uc548\\uc804\\uc9c0\\ub300\\uc5d0\\uc11c \\ubc97\\uc5b4\\ub098\\ub3c4\\ub85d \\ub3c4\\uc640\\uc8fc\\uba70, \\uac1c\\uc778\\uc758 \\uc131\\uc7a5\\uacfc \\ubc1c\\uc804\\uc73c\\ub85c \\uc774\\uc5b4\\uc9c8 \\uc218 \\uc788\\uc2b5\\ub2c8\\ub2e4. \\ub192\\uc740 \\ubaa9\\ud45c\\ub97c \\uc124\\uc815\\ud558\\ub294 \\ub370\\ub294 \\uba87 \\uac00\\uc9c0 \\uc7a0\\uc7ac\\uc801\\uc778 \\ub2e8\\uc810\\uc774 \\uc788\\uc9c0\\ub9cc, \\uc704\\ud5d8\\ubcf4\\ub2e4 \\uc774\\uc810\\uc774 \\ub354 \\ud070 \\uacbd\\uc6b0\\uac00 \\ub9ce\\uc2b5\\ub2c8\\ub2e4. \\uadf8\\ub7ec\\ubbc0\\ub85c \\uc2a4\\uc2a4\\ub85c \\ub192\\uc740 \\ubaa9\\ud45c\\ub97c \\uc124\\uc815\\ud558\\uace0 \\uc774\\ub97c \\ub2ec\\uc131\\ud558\\uae30 \\uc704\\ud574 \\uc5f4\\uc2ec\\ud788 \\ub178\\ub825\\ud558\\ub294 \\uac83\\uc774 \\uc88b\\uc2b5\\ub2c8\\ub2e4.\",\n          \"\\uc548\\ub155 \\ub0b4 \\uc774\\ub984\\uc740 \\uc54c\\ub809\\uc2a4 \\uc57c. \\uc800\\ub294 \\uc81c\\uac00 \\uad00\\uc2ec\\uc744 \\uac16\\uace0 \\uc788\\ub294 \\uc0ac\\uc5c5\\uc744 \\uad00\\ucc30\\ud558\\uace0 \\ub354 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\\uc81c\\uacf5\\ud558\\uaca0\\uc2b5\\ub2c8\\ub2e4.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 735,\n        \"min\": 374,\n        \"max\": 1898,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1772,\n          396\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import random\n",
        "import pandas as pd\n",
        "\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "valid_df = valid_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "print(f\"shape of train data: {train_df.shape}\")\n",
        "display(train_df.head())\n",
        "print(f\"shape of validation data: {valid_df.shape}\")\n",
        "display(valid_df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "6fa6478c1ae14ee2aa1d525f343fd30f",
            "a79fba042fd9426bb65f22c0afe9ad68",
            "4cde58656b29422ca1598bb6cffc96a3",
            "670e53da0b5545fc8c11a71bdfe523c6",
            "76664103c436401bad5981e2e89f9b47",
            "dfb7684e70b64fd49bc247b25c485317",
            "d3734b33686f4f89b97ac76cb1e4d1cc",
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            "1593748fca3948b4a51503c15b872b60",
            "130cf007577744eb9fae8c2570593ac5",
            "3c70c17db2234a8c8b0793ac0c71191b",
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            "b1da4a5b5d484c728685476a0b522466",
            "8ab04c0fc6f74a4b96558176814f8a71",
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            "6a7c53de2d8344b8a64348138ef87a80",
            "afbacfcb77ec4e2b93d7da3e803137fd",
            "aac435e9e2504533857831208c78570d",
            "e795b05afacb41f78ccab9896bf4f73d",
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            "73b266b5392f4bbb89cab88f2389d515",
            "158297a30f104613b4f8846b39e9355e",
            "db79c44173a044c39c8e497799af660e"
          ]
        },
        "id": "uAFU5CPPUjSm",
        "outputId": "3b4edc90-1daa-4603-ae03-24f7e9bf9ca5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6fa6478c1ae14ee2aa1d525f343fd30f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c70c17db2234a8c8b0793ac0c71191b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/200 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a7c53de2d8344b8a64348138ef87a80"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/200 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2d1ba0bf4b742d4aff10b0783d98588"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset_creator = AiDataset(config)\n",
        "\n",
        "train_ds = dataset_creator.get_dataset(train_df)\n",
        "valid_ds = dataset_creator.get_dataset(valid_df)\n",
        "\n",
        "tokenizer = dataset_creator.tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "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": 59,
      "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": 60,
      "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": 61,
      "metadata": {
        "id": "kL1LavCKUtap"
      },
      "outputs": [],
      "source": [
        "data_collator = AiCollator(\n",
        "    tokenizer=tokenizer,\n",
        "    pad_to_multiple_of=64\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "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": 63,
      "metadata": {
        "id": "w4Jfd9zzUwLw"
      },
      "outputs": [],
      "source": [
        "\n",
        "data_collator_train = AiCollatorTrain(\n",
        "    tokenizer=tokenizer,\n",
        "    pad_to_multiple_of=64,\n",
        "    kwargs=dict(cfg=config)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3GIo9OFUyEv",
        "outputId": "e0b1a97b-b1e6-4244-8a9d-1bde28067f7b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data preparation done...\n",
            "batch size: 1\n",
            "shape of input_ids: torch.Size([1, 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> 저는 평소 심리학 분야에 관심이 많았습니다. 고등학교 시절 나는 이 주제에 관한 다양한 세미나와 강의에 참석할 기회가 있었고 그것은 나에게 가장 큰 영감의 원천 중 하나였습니다. 나는 인간 행동의 복잡성과 매혹적인 사실에 매료되어 있으며 사람들을 돕는 데 열정을 갖고 있습니다. 이 분야는 사람들이 왜 그렇게 생각하고 행동하는지 이해할 수 있는 기회를 제공할 뿐만 아니라 사람들의 삶에 긍정적인 변화를 만드는 데 도움이 되는 잠재력도 제공합니다. 이 진로를 추구하기 위해 저는 마음을 성숙시키고 이 분야에 적용할 수 있는 지식과 기술을 개발하기 위해 열심히 노력해 왔습니다. 또한, 나는 다재다능한 성격을 개발하고 이 여정을 시작할 준비를 하는 데 도움이 될 수 있는 다양한 열정과 취미를 탐구하기 위해 주도권을 잡았습니다. 배움, 마음의 성숙, 열정의 개발을 결합하여 심리학 분야에서 경력을 쌓을 수 있다고 확신합니다.\n",
            "----------------------------------------\n",
            "Label: 1.0\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> 전문가와 당국의 조언을 구하는 것은 의학적 조언부터 재정적 결정에 이르기까지 삶의 많은 중요한 문제에 있어서 현명한 결정이 될 수 있습니다. 해당 분야의 전문가로부터 조언을 구하는 것이 유익할 수 있습니다. 그러한 조언은 종종 전문가의 경험과 연구에서 나오기 때문입니다. 그러나 누구도 오류가 없는 사람은 없으며, 전문가나 권위자도 실수를 할 수 있다는 점을 기억하는 것이 중요합니다. 상황에 대한 자신의 이해를 구하고 실수할 수 있는 용기를 갖는 것은 정보에 입각한 결정을 내리는 데 도움이 될 수 있습니다. 전문가나 권위자가 개인의 삶을 완전히 이해하지 못한다면 그들이 제공하는 조언은 정확성이 부족하고 부정확할 수 있습니다. 따라서 전문가의 조언을 구하는 결정은 잠재적인 이익에 관계없이 신중하게 이루어져야 합니다.\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": 65,
      "metadata": {
        "id": "TK7JyT8UUzFv"
      },
      "outputs": [],
      "source": [
        "class MistralForDetectAI(MistralPreTrainedModel):\n",
        "    def __init__(self, config):\n",
        "        super().__init__(config)\n",
        "        self.num_labels = config.num_labels\n",
        "        self.model = MistralModel(config)\n",
        "        # self.dropout = nn.Dropout(0.3)\n",
        "\n",
        "        self.classification_head = nn.Linear(config.hidden_size, self.num_labels, bias=False)\n",
        "\n",
        "        self.loss_fn = nn.BCEWithLogitsLoss()\n",
        "\n",
        "        # Initialize weights and apply final processing\n",
        "        self.post_init()\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        input_ids: torch.LongTensor = None,\n",
        "        attention_mask: Optional[torch.Tensor] = None,\n",
        "        position_ids: Optional[torch.LongTensor] = None,\n",
        "        past_key_values: Optional[List[torch.FloatTensor]] = None,\n",
        "        inputs_embeds: Optional[torch.FloatTensor] = None,\n",
        "        labels: Optional[torch.LongTensor] = None,\n",
        "        use_cache: Optional[bool] = None,\n",
        "        output_attentions: Optional[bool] = None,\n",
        "        output_hidden_states: Optional[bool] = None,\n",
        "        return_dict: Optional[bool] = None,\n",
        "    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:\n",
        "\n",
        "        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
        "\n",
        "        transformer_outputs = self.model(\n",
        "            input_ids,\n",
        "            attention_mask=attention_mask,\n",
        "            position_ids=position_ids,\n",
        "            past_key_values=past_key_values,\n",
        "            inputs_embeds=inputs_embeds,\n",
        "            use_cache=use_cache,\n",
        "            output_attentions=output_attentions,\n",
        "            output_hidden_states=output_hidden_states,\n",
        "            return_dict=return_dict,\n",
        "        )\n",
        "        hidden_states = transformer_outputs[0]  # (bs, seq_len, dim)\n",
        "        # hidden_states = self.dropout(hidden_states)\n",
        "        logits = self.classification_head(hidden_states[:, -1]).reshape(-1)  # (bs, num_labels)\n",
        "\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            labels = labels.to(logits.device).reshape(-1)\n",
        "            loss = self.loss_fn(logits, labels)\n",
        "\n",
        "        if not return_dict:\n",
        "            output = (logits,) + transformer_outputs[1:]\n",
        "            return ((loss,) + output) if loss is not None else output\n",
        "\n",
        "        return SequenceClassifierOutputWithPast(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            past_key_values=transformer_outputs.past_key_values,\n",
        "            hidden_states=transformer_outputs.hidden_states,\n",
        "            attentions=transformer_outputs.attentions,\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install numba\n",
        "\n",
        "# from numba import cuda\n",
        "\n",
        "# device = cuda.get_current_device(); device.reset()\n",
        "# device = cuda.get_current_device();"
      ],
      "metadata": {
        "id": "e_8GEhK9oR3A"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "\n",
        "# # Python의 가비지 컬렉터를 강제로 실행\n",
        "# gc.collect()\n",
        "\n",
        "# # PyTorch가 GPU 캐시에서 할당하지 않은 메모리를 비움\n",
        "# torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "BYEB1X5Zissh"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXS8x5G3U06B",
        "outputId": "5080eaf8-bb84-4f63-da82-9e9bf3b1a3d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/quantizers/auto.py:167: UserWarning: You passed `quantization_config` or equivalent parameters to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` from the model will be used.\n",
            "  warnings.warn(warning_msg)\n"
          ]
        }
      ],
      "source": [
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=torch.float16\n",
        ")\n",
        "\n",
        "base_model = MistralForDetectAI.from_pretrained(\n",
        "            '/content/drive/MyDrive/last',\n",
        "            num_labels=config['model']['num_labels'],  # 2\n",
        "            quantization_config=bnb_config,\n",
        "            low_cpu_mem_usage=True,\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # After determining if it's the best model or just a regular save...\n",
        "# save_model(base_model, tokenizer,'/content/drive/MyDrive/', is_best=False)"
      ],
      "metadata": {
        "id": "L3R-vO_puvmq"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "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": 71,
      "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": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GPS3qrNlU4gY",
        "outputId": "e655b9b1-fe93-44d4-975a-edde379de57f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda:0\n",
            "trainable params: 3,411,968 || all params: 7,171,751,936 || trainable%: 0.0476\n"
          ]
        }
      ],
      "source": [
        "model = get_peft_model(base_model, peft_config)\n",
        "print(model.device)\n",
        "model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSjbteFJU5YK",
        "outputId": "a17b43ca-47e7-42fb-83ed-914332fc67cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'seed': 424,\n",
              " 'use_wandb': False,\n",
              " 'model': {'backbone_path': 'maywell/Mistral-ko-7B-v0.1',\n",
              "  'max_length': 1296,\n",
              "  'num_labels': 1,\n",
              "  'tokenizer': {'padding_side': 'left',\n",
              "   'truncation_side': 'left',\n",
              "   'use_fast': True},\n",
              "  'lora': {'target_modules': ['q_proj', 'k_proj'],\n",
              "   'r': 8,\n",
              "   'lora_alpha': 16,\n",
              "   'lora_dropout': 0.1,\n",
              "   'modules_to_save': ['classification_head',\n",
              "    'classifier',\n",
              "    'score',\n",
              "    'classifier',\n",
              "    'score']}},\n",
              " 'train_params': {'per_device_train_batch_size': 1,\n",
              "  'per_device_eval_batch_size': 1,\n",
              "  'num_train_epochs': 4,\n",
              "  'gradient_accumulation_steps': 4,\n",
              "  'warmup_pct': 0.1,\n",
              "  'eval_frequency': 500,\n",
              "  'patience': 20,\n",
              "  'save_trigger': 0.0,\n",
              "  'use_mask_aug': False,\n",
              "  'mask_aug_prob': 0.0},\n",
              " 'optimizer': {'name': 'AdamW8bit',\n",
              "  'head_lr': '2e-6',\n",
              "  'lr': '2e-5',\n",
              "  'weight_decay': '1e-2',\n",
              "  'max_grad_norm': 0.5},\n",
              " 'outputs': {'model_dir': '/content/drive/MyDrive/'},\n",
              " 'wandb': {'project': 'detect-ai-a1',\n",
              "  'run_name': 'exp010-r-detect',\n",
              "  'tags': ['mistral']}}"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "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": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1DvNdScVSOc",
        "outputId": "d7a5e190-76db-45a5-da74-dcfe25f6b5e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n_head_params_no_decay: 0\n",
            "n_head_params_decay: 4096\n",
            "n_body_params_no_decay: 0\n",
            "n_body_params_decay: 3407872\n"
          ]
        }
      ],
      "source": [
        "optimizer = get_optimizer(config, model, print_fn=print)\n",
        "#     optimizer = get_optimizer(cfg, model, print_fn=accelerator.print)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3tW_3i5VIrF",
        "outputId": "bb3d996d-b520-46ef-f147-177bb3154997"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# training updates per epoch: 1600\n",
            "# training steps: 6400\n",
            "# warmup steps: 640\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": 77,
      "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": 78,
      "metadata": {
        "id": "7sg7t9xDVVjq"
      },
      "outputs": [],
      "source": [
        "class AverageMeter(object):\n",
        "    \"\"\"Computes and stores the average and current value\n",
        "       Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "\n",
        "    def reset(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        self.val = val\n",
        "        self.sum += val * n\n",
        "        self.count += n\n",
        "        self.avg = self.sum / self.count\n",
        "\n",
        "def get_lr(optimizer):\n",
        "    return optimizer.param_groups[0]['lr']*1e6\n",
        "\n",
        "def as_minutes(s):\n",
        "    m = math.floor(s / 60)\n",
        "    s -= m * 60\n",
        "    return '%dm%ds' % (m, s)"
      ]
    },
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        },
        "id": "CybWWk8jVXbY",
        "outputId": "82a399ea-2ed3-4136-ccec-82d9a5456f25"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1600 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be2d05e9174b4408ab97cc3a41772fa0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57d326afdf424f8eb9882adf6cc61472"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 1 | Step 499 | Total Step 500 | Time: 10m19s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.8675\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dffb2d3390a9462782f6a04a08edebaa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 1 | Step 999 | Total Step 1000 | Time: 20m25s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9879\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b49bfb96fb48479092affbbdf14d2684"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 1 | Step 1499 | Total Step 1500 | Time: 30m48s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9932\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1600 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "06cf887dca5e4535926512adbde593e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d17ece912e340aaa1241ee5d9af6dd7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 2 | Step 399 | Total Step 2000 | Time: 41m1s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9954\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0cfada0cb874770b8d38ff9d36f3488"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 2 | Step 899 | Total Step 2500 | Time: 51m15s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9958\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1f0ef9872dd402b87f0fae384f85039"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 2 | Step 1399 | Total Step 3000 | Time: 61m38s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9975\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1600 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e6dcbd0df6545f2a434af6d340333b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "101327d3da564fb698677a7b065c875c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 3 | Step 299 | Total Step 3500 | Time: 71m54s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9974\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> patience reached 1/20\n",
            ">>> current best score: 0.9975\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71fcf0cb95554ca0b22f580f99a48491"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 3 | Step 799 | Total Step 4000 | Time: 82m1s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9986\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "47fc6b70014b4cd2aeac4ef3f6079bbc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 3 | Step 1299 | Total Step 4500 | Time: 92m20s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.999\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1600 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1406770dc4014d129fc6b1a8d3462efb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e350f9c77d1145bf8f4af424716d7db6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 4 | Step 199 | Total Step 5000 | Time: 102m41s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.9989\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> patience reached 1/20\n",
            ">>> current best score: 0.999\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a2680efb34d4667ba40887a777f0e82"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 4 | Step 699 | Total Step 5500 | Time: 113m4s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.999\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "44247baf687547269f3252b413e32697"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Epoch 4 | Step 1199 | Total Step 6000 | Time: 123m10s\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
            ">>> Current LB (AUC) = 0.999\n",
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:195: UserWarning: Could not find a config file in /content/drive/MyDrive/last - will assume that the vocabulary was not modified.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import gc\n",
        "\n",
        "# ------- training  --------------------------------------------------------------------#\n",
        "start_time = time.time()\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "\n",
        "    # Python의 가비지 컬렉터를 강제로 실행\n",
        "    gc.collect()\n",
        "\n",
        "    # PyTorch가 GPU 캐시에서 할당하지 않은 메모리를 비움\n",
        "    torch.cuda.empty_cache()\n",
        "    # close and reset progress bar\n",
        "    if epoch != 0:\n",
        "        progress_bar.close()\n",
        "\n",
        "    progress_bar = tqdm(range(num_update_steps_per_epoch))\n",
        "    loss_meter = AverageMeter()\n",
        "\n",
        "    # Training ------\n",
        "    model.train()\n",
        "\n",
        "    for step, batch in enumerate(train_dl):\n",
        "\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        progress_bar.update(1)\n",
        "        current_iteration += 1\n",
        "\n",
        "        # >--------------------------------------------------|\n",
        "        # >-- evaluation ------------------------------------|\n",
        "        # >--------------------------------------------------|\n",
        "\n",
        "        if current_iteration % config['train_params']['eval_frequency'] == 0:\n",
        "            # set model in eval mode\n",
        "            model.eval()\n",
        "            eval_response = run_evaluation(model, valid_dl, valid_ids)\n",
        "\n",
        "            scores_dict = eval_response[\"scores\"]\n",
        "            result_df = eval_response[\"result_df\"]\n",
        "            oof_df = eval_response[\"oof_df\"]\n",
        "\n",
        "            lb = scores_dict[\"lb\"]\n",
        "\n",
        "            print_line()\n",
        "            et = as_minutes(time.time()-start_time)\n",
        "\n",
        "            print(f\">>> Epoch {epoch+1} | Step {step} | Total Step {current_iteration} | Time: {et}\")\n",
        "            print_line()\n",
        "            print(f\">>> Current LB (AUC) = {round(lb, 4)}\")\n",
        "\n",
        "            print_line()\n",
        "\n",
        "            is_best = False\n",
        "            if lb >= best_lb:\n",
        "                best_lb = lb\n",
        "                is_best = True\n",
        "                patience_tracker = 0\n",
        "\n",
        "                # -----\n",
        "                best_dict = dict()\n",
        "                for k, v in scores_dict.items():\n",
        "                    best_dict[f\"{k}_at_best\"] = v\n",
        "            else:\n",
        "                patience_tracker += 1\n",
        "\n",
        "\n",
        "            if is_best:  # do in main process\n",
        "                oof_df.to_csv(os.path.join(config['outputs']['model_dir'], f\"oof_df_best.csv\"), index=False)\n",
        "                result_df.to_csv(os.path.join(config['outputs']['model_dir'], f\"result_df_best.csv\"), index=False)\n",
        "            else:\n",
        "                print(f\">>> patience reached {patience_tracker}/{config['train_params']['patience']}\")\n",
        "                print(f\">>> current best score: {round(best_lb, 4)}\")\n",
        "\n",
        "            oof_df.to_csv(os.path.join(config['outputs']['model_dir'], f\"oof_df_last.csv\"), index=False)\n",
        "            result_df.to_csv(os.path.join(config['outputs']['model_dir'], f\"result_df_last.csv\"), index=False)\n",
        "\n",
        "            # saving -----\n",
        "            unwrapped_model = model\n",
        "            unwrapped_model.save_pretrained(f\"{config['outputs']['model_dir']}/last\")\n",
        "\n",
        "            # debug --\n",
        "            # selected_adapters = list(unwrapped_model.peft_config.keys())\n",
        "            # print(f\"selected adapters: {selected_adapters}\")\n",
        "\n",
        "            # for adapter_name in selected_adapters:\n",
        "            #     peft_config = unwrapped_model.peft_config[adapter_name]\n",
        "            #     peft_config = asdict(peft_config)\n",
        "            #     print(f\"adapter: {adapter_name}\")\n",
        "            #     print(peft_config)\n",
        "            #     for k, v in peft_config.items():\n",
        "            #         print(f\"{k}: {v} ({type(v)})\")\n",
        "\n",
        "            # ------\n",
        "\n",
        "            tokenizer.save_pretrained(f\"{config['outputs']['model_dir']}/last\")\n",
        "\n",
        "            if best_lb > save_trigger:\n",
        "                # 모델 및 토크나이저를 저장하는 경로 설정\n",
        "                model_save_path = f\"{config['outputs']['model_dir']}/best\"\n",
        "                tokenizer_save_path = f\"{config['outputs']['model_dir']}/best\"\n",
        "\n",
        "                # 모델과 토크나이저 저장\n",
        "                model.save_pretrained(model_save_path)\n",
        "                tokenizer.save_pretrained(tokenizer_save_path)\n",
        "\n",
        "                # -- post eval\n",
        "                model.train()\n",
        "                torch.cuda.empty_cache()\n",
        "                print_line()\n",
        "\n",
        "                # early stopping ----\n",
        "                if patience_tracker >= config['train_params']['patience']:\n",
        "                    print(\"stopping early\")\n",
        "                    model.eval()\n",
        "                    break\n",
        "\n",
        "#     # --- end training\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "def save_model(model, tokenizer, model_dir, is_best=False):\n",
        "    \"\"\"\n",
        "    Save the model and tokenizer to the specified directory.\n",
        "\n",
        "    Args:\n",
        "    - model: The model to save.\n",
        "    - tokenizer: The tokenizer to save.\n",
        "    - model_dir: The base directory to save the model and tokenizer.\n",
        "    - is_best: If True, saves the model as the best model. Otherwise, saves as the last checkpoint.\n",
        "    \"\"\"\n",
        "    # Define save paths\n",
        "    model_save_path = os.path.join(model_dir, 'best' if is_best else 'last')\n",
        "    tokenizer_save_path = os.path.join(model_dir, 'best' if is_best else 'last')\n",
        "\n",
        "    # Create directories if they do not exist\n",
        "    os.makedirs(model_save_path, exist_ok=True)\n",
        "    os.makedirs(tokenizer_save_path, exist_ok=True)\n",
        "\n",
        "    # Save the model and tokenizer\n",
        "    model.save_pretrained(model_save_path)\n",
        "    tokenizer.save_pretrained(tokenizer_save_path)\n",
        "    print(f\"Model saved {'as best model' if is_best else 'as last checkpoint'} in {model_save_path}\")"
      ],
      "metadata": {
        "id": "Y_9j4AVnnN4I"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# After determining if it's the best model or just a regular save...\n",
        "save_model(model, tokenizer,'/content/drive/MyDrive/', is_best=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9rDyLELnPjh",
        "outputId": "c8c9ff5e-e5fa-410e-f3db-dffdd65ef9e2"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved as last checkpoint in /content/drive/MyDrive/last\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "bS-M9Bl1oTrx"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import unicodedata\n",
        "\n",
        "\n",
        "# pre-process -----\n",
        "char_to_remove = ['{', '£', '\\x97', '¹', 'å', '\\\\', '\\x85', '<', '\\x99', \\\n",
        "                  'é', ']', '+', 'Ö', '\\xa0', '>', '|', '\\x80', '~', '©', \\\n",
        "                  '/', '\\x93', '$', 'Ó', '²', '^', ';', '`', 'á', '*', '(', \\\n",
        "                  '¶', '®', '[', '\\x94', '\\x91', '#', '-', 'ó', ')', '}', '=']\n",
        "\n",
        "\n",
        "\n",
        "def preprocess_text(text, strategy='light'):\n",
        "    assert strategy in [\"none\", \"light\", \"heavy\"], \"pre-processing strategy must one of: none, light, heavy\"\n",
        "\n",
        "    if strategy == \"none\":\n",
        "        text = text\n",
        "\n",
        "    elif strategy == \"light\":\n",
        "        text = str(text)\n",
        "        text = text.strip()\n",
        "        text = text.strip(\"\\\"\")\n",
        "\n",
        "        for c in char_to_remove:\n",
        "            text = text.replace(c, \"\")\n",
        "\n",
        "        if text[-1]!=\".\":\n",
        "            text = text.split(\".\")\n",
        "            text = \".\".join(text[:-1])\n",
        "            text += \".\"\n",
        "    else:\n",
        "        text = unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('ascii')\n",
        "        text = text.lower()\n",
        "        text = re.sub(r'[^a-z0-9\\s.,;?!:()\\'\\\"%-]', '', text)\n",
        "        text = re.sub(r'\\s+', ' ', text).strip()\n",
        "\n",
        "    return text\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "infer_df = pd.read_csv('/content/korean_essay.csv')\n",
        "infer_df = infer_df[['생성 Text', 'model']]\n",
        "infer_df['label'] = infer_df['model'].apply(lambda x: 1 if 'gpt' in str(x).lower() else 0)\n",
        "infer_df = infer_df[['생성 Text','label']]\n",
        "infer_df.rename(columns={'생성 Text':'text'}, inplace = True)\n",
        "infer_df =infer_df.dropna(axis=0).reset_index(drop=True)\n",
        "infer_df['id'] = range(1, len(infer_df) + 1)\n",
        "\n",
        "infer_df['text'] = infer_df['text'].apply(lambda x: preprocess_text(x))\n",
        "infer_df.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "nB51seuyGjdM",
        "outputId": "f89bb2a1-71ab-4bc8-a72d-b6416f4a6c58"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-83-e3f72c3489c4>:1: DtypeWarning: Columns (0,1,2,3,5,6,7,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  infer_df = pd.read_csv('/content/korean_essay.csv')\n",
            "<ipython-input-83-e3f72c3489c4>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  infer_df.rename(columns={'생성 Text':'text'}, inplace = True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  text  label   id\n",
              "162  가장 먼저, 제시된 문장들을 종합적으로 살펴보면 사회과학 및 인문학 분야에서의 연구...      1  163\n",
              "394  제시문 가에서 야만인이 요구하는 '불행해질 권리'는 인간이 경험하는 다양한 감정과 ...      1  395\n",
              "396  황석영의 소설 「삼포 가는 길」과 현기영의 소설 『지상에 숟가락 하나』에서 나타난 ...      1  397\n",
              "202  세계시민주의와 국가 중심주의라는 두 가지 관점이 언어의 사용과 보존에 관한 논의에서...      1  203\n",
              "414  제시문 가, 나, 다, 라는 대상의 인식에 대해 서로 다른 시각을 보여준다. 제시문...      1  415\n",
              "175  환경 문제에 대한 윤리적 입장은 발생한 문제를 기술적으로만 해결하는 것이 아니라 인...      1  176\n",
              "392  제시문 가의 무스타파 몬드가 통치하는 사회는 ‘소마’로 상징되는 과학 기술이 사용되...      0  393\n",
              "469  제시문 가 요약:\\n제시문 가는 텔레비전 프로그램의 국가 간 유통에 경제적, 문화적...      1  470\n",
              "307  제시문 가는 과학의 발전 과정에서 패러다임의 중요성과 변화가 일어나는 방식을 설명하...      1  308\n",
              "189  가의 주제어인 ‘편견’의 개념을 중심으로 볼 때 나의 자목이 가진 관점은 일종의 편...      0  190"
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\\ub9e5\\ub77d\\uc744 \\uc9c0\\ub2cc\\ub2e4. \\uac00\\uac00 \\uacfc\\ud559\\uc801 \\ud601\\uc2e0\\uc744 \\ub2e4\\ub8e8\\ub294 \\ubc18\\uba74, \\ub2e4\\ub294 \\uacbd\\uc81c \\uc815\\ucc45\\uc758 \\uc2e4\\ud589\\uacfc \\uadf8\\uc5d0 \\ub530\\ub978 \\ubcc0\\ud654\\ub97c \\ub2e4\\ub8ec\\ub2e4.\\n\\n\\uc81c\\uc2dc\\ubb38 \\ub77c\\ub294 \\ud55c\\uc790\\uc758 \\uc0ac\\uc6a9\\uacfc \\uc5b8\\ubb38\\ud55c\\uae00\\uc758 \\uc0ac\\uc6a9\\uc744 \\ub450\\uace0 \\ubc8c\\uc5b4\\uc9c0\\ub294 \\ub17c\\uc7c1\\uc744 \\ub2e4\\ub8e8\\uace0 \\uc788\\ub2e4. \\uc5ec\\uae30\\uc11c \\ub17c\\uc810\\uc740 \\uc804\\ud1b5\\uc801\\uc778 \\ubb38\\uc790 \\uc0ac\\uc6a9\\uacfc \\uc0c8\\ub85c\\uc6b4 \\ubb38\\uc790 \\uc0ac\\uc6a9\\uc758 \\ud0c0\\ub2f9\\uc131\\uc5d0 \\ub300\\ud55c \\uac83\\uc73c\\ub85c, \\uac00\\uc758 \\uacfc\\ud559\\uc801 \\ud328\\ub7ec\\ub2e4\\uc784 \\ubcc0\\ud654\\uc640\\ub294 \\ub2e4\\ub974\\ub2e4. \\ub77c\\ub294 \\uc5b8\\uc5b4\\uc640 \\ubb38\\uc790 \\uc0ac\\uc6a9\\uc758 \\ubcc0\\ud654\\uc5d0 \\ub300\\ud55c \\ubcf4\\uc218\\uc801\\uc778 \\uc785\\uc7a5\\uc744 \\ubcf4\\uc5ec\\uc8fc\\uba70, \\uc774\\ub294 \\uacfc\\ud559\\uc801 \\ubcc0\\ud654\\uc640 \\ud601\\uc2e0\\uc744 \\uac15\\uc870\\ud558\\ub294 \\uac00\\uc640 \\ub300\\ube44\\ub41c\\ub2e4.\\n\\n\\ub530\\ub77c\\uc11c \\uac00\\ub294 \\uacfc\\ud559\\uc801 \\ud328\\ub7ec\\ub2e4\\uc784\\uc758 \\ubcc0\\ud654\\ub97c \\ub17c\\uc758\\ud558\\uace0 \\uc788\\ub294 \\ubc18\\uba74, \\ub098, \\ub2e4, \\ub77c\\ub294 \\uac01\\uac01 \\uc804\\ud1b5, \\uacbd\\uc81c \\uc815\\ucc45, \\uc5b8\\uc5b4 \\uc0ac\\uc6a9\\uc758 \\ubcc0\\ud654\\ub97c \\ub2e4\\ub8e8\\uba70, \\ubcc0\\ud654\\uc758 \\uc591\\uc0c1\\uacfc \\uc811\\uadfc \\ubc29\\uc2dd\\uc5d0\\uc11c \\uac00\\uc640 \\ucc28\\uc774\\ub97c \\ubcf4\\uc778\\ub2e4.\",\n          \"\\uc81c\\uc2dc\\ubb38 \\uac00\\uc5d0\\uc11c \\uc57c\\ub9cc\\uc778\\uc774 \\uc694\\uad6c\\ud558\\ub294 '\\ubd88\\ud589\\ud574\\uc9c8 \\uad8c\\ub9ac'\\ub294 \\uc778\\uac04\\uc774 \\uacbd\\ud5d8\\ud558\\ub294 \\ub2e4\\uc591\\ud55c \\uac10\\uc815\\uacfc \\uc0c1\\ud669, \\uc989 \\ubd88\\ud589, \\uace0\\ud1b5, \\uc704\\ud5d8, \\uc790\\uc720, \\uc8c4\\ucc45\\uac10 \\ub4f1\\uc744 \\ud3ec\\ud568\\ud55c \\uad8c\\ub9ac\\ub97c \\uc758\\ubbf8\\ud55c\\ub2e4. \\uc774\\ub294 \\uc644\\ubcbd\\ud558\\uac8c \\ud1b5\\uc81c\\ub418\\uace0 \\uc778\\uc704\\uc801\\uc73c\\ub85c \\uc870\\uc791\\ub41c \\uc548\\ub77d\\ud55c \\uc0b6\\uc744 \\uac70\\ubd80\\ud558\\uace0, \\uc778\\uac04\\uc73c\\ub85c\\uc11c\\uc758 \\uc790\\uc5f0\\uc2a4\\ub7ec\\uc6b4 \\uacbd\\ud5d8\\uacfc \\uac10\\uc815\\uc744 \\uc628\\uc804\\ud788 \\ub204\\ub9b4 \\uad8c\\ub9ac\\uc774\\ub2e4.\\n\\n\\uc774 \\uad8c\\ub9ac\\ub294 \\ud604\\ub300 \\uc0ac\\ud68c\\uac00 \\uc81c\\uacf5\\ud558\\ub294 \\uc548\\ub77d\\ud568\\uacfc \\uc778\\uc704\\uc801\\uc778 \\ud589\\ubcf5\\uc758 \\ub300\\uac00\\ub85c \\uc783\\uac8c \\ub418\\ub294 \\uc778\\uac04\\uc758 \\ubcf8\\uc5f0\\uc801\\uc778 \\uacbd\\ud5d8\\uc744 \\ub418\\ucc3e\\uace0\\uc790 \\ud558\\ub294 \\uc8fc\\uc7a5\\uc774\\ub2e4. \\uc57c\\ub9cc\\uc778\\uc740 \\uc778\\uc704\\uc801\\uc73c\\ub85c \\uc870\\uc791\\ub41c \\ud589\\ubcf5\\uc774 \\uc544\\ub2cc, \\uc9c4\\uc815\\ud55c \\uac10\\uc815\\uacfc \\uacbd\\ud5d8\\uc744 \\ud1b5\\ud574 \\uc778\\uac04\\ub2e4\\uc6b4 \\uc0b6\\uc744 \\uc0b4\\uae30\\ub97c \\uc6d0\\ud55c\\ub2e4. \\uc774\\ub97c \\ud1b5\\ud574 \\uac1c\\uc778\\uc758 \\uc9c4\\uc815\\ud55c \\uc790\\uc544\\uc640 \\uc790\\uc720\\ub97c \\ucd94\\uad6c\\ud558\\uace0, \\ubd88\\ud655\\uc2e4\\ud558\\uace0 \\uace0\\ud1b5\\uc2a4\\ub7ec\\uc6b4 \\uc0c1\\ud669\\uc744 \\uacaa\\uc73c\\uba70 \\uc131\\ucc30\\ud558\\uace0 \\uc131\\uc7a5\\ud560 \\uae30\\ud68c\\ub97c \\uc694\\uad6c\\ud558\\ub294 \\uac83\\uc774\\ub2e4.\\n\\n\\ub530\\ub77c\\uc11c '\\ubd88\\ud589\\ud574\\uc9c8 \\uad8c\\ub9ac'\\ub294 \\uc778\\uac04\\uc774 \\uace0\\uc720\\ud55c \\uac10\\uc815\\uacfc \\uacbd\\ud5d8\\uc744 \\ud1b5\\ud574 \\uc9c4\\uc815\\ud55c \\uc0b6\\uc744 \\uc0b4\\uc544\\uac00\\ub294 \\ub370 \\ud544\\uc694\\ud55c \\uad8c\\ub9ac\\ub85c, \\uc778\\uc704\\uc801\\uc778 \\ud589\\ubcf5\\uacfc \\uc548\\ub77d\\ud568\\uc744 \\uac70\\ubd80\\ud558\\uace0, \\uc790\\uc5f0\\uc2a4\\ub7fd\\uace0 \\uc9c4\\uc815\\uc131 \\uc788\\ub294 \\uc0b6\\uc744 \\ucd94\\uad6c\\ud558\\ub294 \\uad8c\\ub9ac\\uc774\\ub2e4.\",\n          \"\\ud658\\uacbd \\ubb38\\uc81c\\uc5d0 \\ub300\\ud55c \\uc724\\ub9ac\\uc801 \\uc785\\uc7a5\\uc740 \\ubc1c\\uc0dd\\ud55c \\ubb38\\uc81c\\ub97c \\uae30\\uc220\\uc801\\uc73c\\ub85c\\ub9cc \\ud574\\uacb0\\ud558\\ub294 \\uac83\\uc774 \\uc544\\ub2c8\\ub77c \\uc778\\ubb38\\uc0ac\\ud68c\\uacfc\\ud559, \\ud2b9\\ud788 \\uc724\\ub9ac\\uc801 \\uce21\\uba74\\uc744 \\uace0\\ub824\\ud574\\uc57c \\ud55c\\ub2e4. \\ud604\\ub300 \\ubb38\\uba85\\uc774 \\uc81c\\uae30\\ud558\\ub294 \\ud658\\uacbd \\ubb38\\uc81c\\ub294 \\uc9c0\\uad6c\\uc801\\uc778 \\uaddc\\ubaa8\\ub85c \\uc7a5\\uae30\\uac04\\uc5d0 \\uac78\\uccd0 \\ud6a8\\uacfc\\uac00 \\ub098\\ud0c0\\ub098\\ub294 \\ubcf5\\uc7a1\\uc131\\uc744 \\uac00\\uc9c0\\uace0 \\uc788\\uae30 \\ub54c\\ubb38\\uc5d0 \\uc774\\ub97c \\ud574\\uacb0\\ud558\\uae30 \\uc704\\ud574\\uc11c\\ub294 \\ub2e4\\uc74c \\ub450 \\uac00\\uc9c0\\uc758 \\uc724\\ub9ac\\uc801 \\uc785\\uc7a5\\uc744 \\uace0\\ub824\\ud574\\uc57c \\ud55c\\ub2e4.\\n\\n\\uac00 \\uc9c0\\uad6c \\uc628\\ub09c\\ud654\\uc640 \\uac19\\uc740 \\ud658\\uacbd \\ubb38\\uc81c\\ub294 \\uc778\\ub958\\uc758 \\uc0b6\\uc5d0 \\uc9c1\\uc811\\uc801\\uc73c\\ub85c \\uc601\\ud5a5\\uc744 \\ubbf8\\uce58\\uba70, \\uc774\\ub97c \\ud574\\uacb0\\ud558\\uae30 \\uc704\\ud574\\uc11c\\ub294 \\uc778\\uac04\\uc758 \\uc0b6\\uacfc \\uc790\\uc5f0 \\uc0ac\\uc774\\uc758 \\uc870\\ud654\\ub97c \\uc774\\ub8e8\\ub294 \\ucc45\\uc784\\uc758 \\uba85\\ubc95\\uc774 \\ud544\\uc694\\ud558\\ub2e4. \\uc694\\ub098\\uc2a4\\uc758 \\uc8fc\\uc7a5\\ucc98\\ub7fc, \\ubbf8\\ub798 \\uc138\\ub300\\uc640 \\uc790\\uc5f0\\uc5d0 \\ub300\\ud55c \\ucc45\\uc784\\uc744 \\uac16\\uace0 \\uc9c0\\uc18d \\uac00\\ub2a5\\ud55c \\ubc1c\\uc804\\uc744 \\ucd94\\uad6c\\ud574\\uc57c \\ud55c\\ub2e4.\\n\\n\\ub098 \\ub610\\ud55c, \\ud658\\uacbd \\ubb38\\uc81c\\ub294 \\uc0ac\\ud68c\\uc801 \\ubd88\\ud3c9\\ub4f1\\uc744 \\uc57c\\uae30\\ud560 \\uc218 \\uc788\\uc73c\\uba70, \\uc774\\uc5d0 \\ub300\\ud55c \\ud574\\uacb0\\ucc45\\uc73c\\ub85c \\uad6d\\uac00\\uac00 \\uc778\\uac04\\uc73c\\ub85c\\uc11c \\ucd5c\\uc18c\\ud55c\\uc758 \\uc0b6\\uc744 \\ubcf4\\uc7a5\\ud558\\ub294\\ub370 \\uc801\\uadf9\\uc801\\uc778 \\uc5ed\\ud560\\uc744 \\uc218\\ud589\\ud574\\uc57c \\ud55c\\ub2e4. \\uad6d\\ubbfc\\uc740 \\uad6d\\uac00\\uc5d0\\uac8c \\ucd5c\\uc18c\\ud55c\\uc758 \\uc0dd\\uc874\\uc744 \\ubcf4\\uc7a5\\ubc1b\\uc744 \\uad8c\\ub9ac\\ub97c \\uc8fc\\uc7a5\\ud560 \\uc218 \\uc788\\uc73c\\uba70, \\uad6d\\uac00\\ub294 \\uadf8\\uc5d0 \\uc0c1\\uc751\\ud558\\ub294 \\uc758\\ubb34\\ub97c \\uc9c0\\ub140\\uc57c \\ud55c\\ub2e4.\\n\\n\\ud658\\uacbd \\ubb38\\uc81c\\uc5d0 \\ub300\\ud55c \\uc724\\ub9ac\\uc801 \\uc785\\uc7a5\\uc740 \\ub2e8\\uc21c\\ud788 \\uae30\\uc220\\uc801 \\ud574\\uacb0\\ub9cc\\uc744 \\ub118\\uc5b4\\uc11c\\uc11c \\uc0ac\\ud68c\\uc801, \\uc724\\ub9ac\\uc801 \\uce21\\uba74\\uc744 \\ud568\\uaed8 \\uace0\\ub824\\ud558\\uc5ec \\uc9c0\\uc18d \\uac00\\ub2a5\\ud55c \\ud574\\uacb0\\ucc45\\uc744 \\ubaa8\\uc0c9\\ud574\\uc57c \\ud55c\\ub2e4.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 117,\n        \"min\": 163,\n        \"max\": 470,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          308,\n          395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "infer_ds = dataset_creator.get_dataset(infer_df)\n",
        "infer_ds = infer_ds.sort(\"input_length\")\n",
        "tokenizer = dataset_creator.tokenizer\n",
        "infer_ds.set_format(\n",
        "        type=None,\n",
        "        columns=[\n",
        "            'id',\n",
        "            'input_ids',\n",
        "            'attention_mask',\n",
        "        ]\n",
        "    )\n",
        "\n",
        "infer_ids = infer_ds[\"id\"]  # .tolist()\n",
        "\n",
        "#--\n",
        "data_collator = AiCollator(\n",
        "    tokenizer=tokenizer,\n",
        "    pad_to_multiple_of=64\n",
        ")\n",
        "\n",
        "infer_dl = DataLoader(\n",
        "    infer_ds,\n",
        "    batch_size=1,\n",
        "    shuffle=False,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "\n",
        "#----------\n",
        "for b in infer_dl:\n",
        "    break\n",
        "show_batch(b, tokenizer, task='infer', print_fn=print)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303,
          "referenced_widgets": [
            "a77fb8a430de46b9965b24fc7ef77861",
            "b0554a9511054787b1bc00b2ace24ffe",
            "3b0267afc5d74ca8a4baf8b8436ee73c",
            "6cacd7e7327d4c1da309749b9fc520fd",
            "341e8b3462ef45629f47afbcd2082c18",
            "3ba5b643d7944b1584721f863b0f2d38",
            "3c2593d1a4754b1c8b5b8134cd6c7a69",
            "a81dd230374448afbbe16c519026c6c3",
            "5944cbf5cca04f92befb567877a1ec02",
            "b3feb3a3f8a0472db1bb534f583eebcc",
            "6992498142424ec8a71034e63c77c2c1",
            "a72923743c7c4b26ac0088ab3dcdbe90",
            "2e2a813a93b74b129c525f4c8dcab939",
            "e0b0e305a50848409fd65b5860970c04",
            "cd7a751b3abe472a907d178e69f49e6e",
            "ecb2f4781dfd48dc87a54a3649d462ef",
            "08574040f707451982689e7e66ad69ab",
            "7633d25e1cb94700b725816024e4b7fa",
            "8f00aaf2004d47ad8dfae61a3e2788f8",
            "a540fc325cc34f81b038c34c179ee481",
            "15aec1496db54b1f82c1c8560b4d6097",
            "6b2e96e4fdeb4d6fab67a83d24115c48"
          ]
        },
        "id": "gbe7FSB7lAKc",
        "outputId": "40092711-41e6-462f-eff9-b13fa9ff12cf"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/501 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a77fb8a430de46b9965b24fc7ef77861"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/501 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a72923743c7c4b26ac0088ab3dcdbe90"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "batch size: 1\n",
            "shape of input_ids: torch.Size([1, 128])\n",
            "Showing 1 from a infer batch...\n",
            "\n",
            "\n",
            "\n",
            "Example 1\n",
            "Input:\n",
            "\n",
            "</s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s><s> 제시문 A는 의사소통이 개인의 삶의 질, 건강, 웰빙에 미치는 중요성을 강조하며, 긍정적인 인간관계가 특히 건강에 미치는 긍정적인 영향을 다룹니다. 제시문 B는 직장 내 의사소통 부재가 갈등을 유발하고 조직에 해를 끼칠 수 있다는 점을 강조합니다. 이 두 제시문은 의사소통의 중요성이 개인의 건강과 조직의 효율성에 큰 영향을 미친다는 공통된 논지를 가지고 있습니다.\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def run_inference(model, infer_dl, example_ids):\n",
        "    model.eval()\n",
        "    all_predictions = []\n",
        "\n",
        "    progress_bar = tqdm(range(len(infer_dl)))\n",
        "\n",
        "    for step, batch in enumerate(infer_dl):\n",
        "        with torch.no_grad():\n",
        "            outputs = model(**batch)\n",
        "\n",
        "        logits = outputs.logits.reshape(-1)\n",
        "        predictions = torch.sigmoid(logits)\n",
        "        predictions = predictions.cpu().numpy().tolist()\n",
        "\n",
        "        all_predictions.extend(predictions)\n",
        "\n",
        "        progress_bar.update(1)\n",
        "    progress_bar.close()\n",
        "\n",
        "    result_df = pd.DataFrame()\n",
        "    result_df[\"id\"] = example_ids\n",
        "    result_df[\"generated\"] = all_predictions\n",
        "\n",
        "    return result_df"
      ],
      "metadata": {
        "id": "wjD-Xku5lcmj"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 훈련 1시간, inference : 500개 3분 30초"
      ],
      "metadata": {
        "id": "6HxDTfGRYE-C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sub_df = run_inference(model, infer_dl, infer_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "81d4993c7124468a8b424e02ecf1972d",
            "bb753737a1ad40ce9aa83d26897c1487",
            "af759bca2152443fa45d92a5cf6dd1d8",
            "6af5fb49ab944b52a352a0de77e5e47d",
            "7c872cfa1f0a441f932e788b6426228f",
            "2b6e7c497f6f4162868d9b17d321dab3",
            "f85f97a5953c485482fd9d6ef7af0193",
            "462de35c2a034a698cbd64476a72a459",
            "ebacf4de56d14d73a8df406197970ca6",
            "26adfbc978e6416fae3e5b614a164313",
            "fd507fad081143a69d0c27a4f1d9bc91"
          ]
        },
        "id": "rQj5wRVwos9O",
        "outputId": "31d1dc28-68df-4390-c3f7-fc6fdf0e58f1"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/501 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81d4993c7124468a8b424e02ecf1972d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 'id' 열을 기준으로 두 DataFrame 병합\n",
        "merged_df = pd.merge(infer_df, sub_df, on='id')\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8OPnw0C0poud",
        "outputId": "d83ced2d-747f-4aa5-dffc-1365e51cf02c"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  label  id  generated\n",
              "0  제시문 가는 역사적으로 새로운 종교를 창설하려는 시도가 실패로 끝났음에도 불구하고,...      1   1   0.999220\n",
              "1  제시문 가2에서는 돈 키호테와 산초의 대화를 통해 현실과 환상 사이의 갈등이 나타나...      1   2   0.999503\n",
              "2  주제어로 선택된 공통 키워드는 \"아름다움\"입니다.\\n\\n제시문 가에서는 매화의 아름...      1   3   0.999230\n",
              "3  네 제시문들의 순서를 정하기 위해서는 각 제시문이 개인과 사회 간 영향을 어떻게 다...      1   4   0.992640\n",
              "4  네 제시문들에는 현대 사회에서의 갈등 상황에 대한 다양한 해법과 접근 방식이 내포되...      1   5   0.999798"
            ],
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              "    <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>0.999220</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>0.999503</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>0.999230</td>\n",
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              "    <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>0.992640</td>\n",
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              "    <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",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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\\ub3c5\\uc11c\\ubc95\\uc740 \\uc81c\\uc2dc\\ubb38 \\uac00\\uc5d0\\uc11c \\uc9c0\\uc801\\ud55c \\uc798\\ubabb\\ub41c \\ub3c5\\uc11c \\ud0dc\\ub3c4\\uc640\\ub294 \\ub2ec\\ub9ac, \\ub3c5\\uc11c\\uc758 \\uc9c4\\uc815\\ud55c \\ubaa9\\uc801\\uacfc \\uac00\\uce58\\ub97c \\uc2e4\\ud604\\ud558\\ub294 \\ub370 \\uae30\\uc5ec\\ud558\\ub294 \\ubc14\\ub78c\\uc9c1\\ud55c \\uc811\\uadfc \\ubc29\\uc2dd\\uc73c\\ub85c \\ud574\\uc11d\\ub420 \\uc218 \\uc788\\ub2e4.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 144,\n        \"min\": 1,\n        \"max\": 501,\n        \"num_unique_values\": 501,\n        \"samples\": [\n          363,\n          74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"generated\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2885379826191597,\n        \"min\": 0.0017009767470881343,\n        \"max\": 0.9999945163726807,\n        \"num_unique_values\": 493,\n        \"samples\": [\n          0.9951211810112,\n          0.6485471129417419\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 실제 라벨\n",
        "y_true = merged_df['label'].values\n",
        "y_preds = merged_df['generated'].values\n",
        "\n",
        "# ROC 곡선 그리기\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_preds)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# ROC 곡선 시각화\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "\n",
        "# ROC-AUC 면적 출력\n",
        "print(\"ROC-AUC 면적:\", roc_auc)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "VourwOJXp1ou",
        "outputId": "103ace51-ee95-4bdf-a49a-0fbc31f16d4d"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC-AUC 면적: 0.9760423280423279\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "# F1 스코어 계산\n",
        "y_pred = [1 if score > 0.5 else 0 for score in y_preds]  # fixed_results는 모델 예측 결과로 가정\n",
        "f1 = f1_score(y_true,  y_pred)\n",
        "\n",
        "# F1 스코어 출력\n",
        "print(\"F1 Score:\", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWEJHABnGn5j",
        "outputId": "609da86f-e31d-431b-b7a8-b97d7ffc9435"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1 Score: 0.9169764560099133\n"
          ]
        }
      ]
    },
    {
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      "source": [
        "merged_df.to_csv('1600_train_mistral_7B.csv',index=False)"
      ],
      "metadata": {
        "id": "IB6NDyRbaGEI"
      },
      "execution_count": 90,
      "outputs": []
    }
  ],
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