diff --git "a/Week13_\353\263\265\354\212\265\352\263\274\354\240\234_\352\271\200\354\234\244\354\204\234.ipynb" "b/Week13_\353\263\265\354\212\265\352\263\274\354\240\234_\352\271\200\354\234\244\354\204\234.ipynb"
new file mode 100644
index 0000000..7ba8ba6
--- /dev/null
+++ "b/Week13_\353\263\265\354\212\265\352\263\274\354\240\234_\352\271\200\354\234\244\354\204\234.ipynb"
@@ -0,0 +1,1379 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "24TpKoXHYjO9"
+      },
+      "source": [
+        "# BART 모델을 활용한 뉴스 요약 Fine-tuning\n",
+        "\n",
+        "본 실습 노트북은 교재의 실습 코드를 바탕으로 구현되었으며, **BART (Denoising Sequence-to-Sequence)** 논문의 아키텍처적 특성을 이해하고 실제 생성 요약 Tasks에 적용하는 것을 목표로 합니다.\n",
+        "\n",
+        "1. **조건부 생성 (Conditional Generation):** BART는 인코더-디코더 전체 구조를 사용하여 입력 컨텍스트를 파악하고 새로운 문장을 생성합니다. 코드에서는 `BartForConditionalGeneration` 클래스를 사용합니다.\n",
+        "2. **손실 함수와 패딩 무시 (-100):** 생성 태스크에서 가변 길이 문장을 처리할 때 패딩 토큰은 손실(Loss) 계산에서 제외해야 합니다. 코드에서는 패딩 값으로 `-100`을 설정하여 Cross Entropy 손실 계산 시 자동 무시되도록 처리합니다.\n",
+        "3. **ROUGE 평가지표:** 생성된 요약문과 정답 요약문의 텍스트 유사도를 N-gram 정밀도 및 재현율 기반으로 평가합니다.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nIAUb2mZYjPD"
+      },
+      "source": [
+        "## 0. 필수 라이브러리 설치\n",
+        "데이터세트 로드 및 루지(ROUGE) 평가를 위한 Hugging Face 라이브러리들을 설치합니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "YnHvdJudYjPE",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "230b82ad-4636-4b23-f967-217a24caaedf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n",
+            "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (5.10.2)\n",
+            "Requirement already satisfied: evaluate in /usr/local/lib/python3.12/dist-packages (0.4.6)\n",
+            "Requirement already satisfied: rouge_score in /usr/local/lib/python3.12/dist-packages (0.1.2)\n",
+            "Requirement already satisfied: absl-py in /usr/local/lib/python3.12/dist-packages (1.4.0)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from datasets) (3.29.2)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from datasets) (2.0.2)\n",
+            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (18.1.0)\n",
+            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (0.3.8)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (from datasets) (2.2.2)\n",
+            "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.12/dist-packages (from datasets) (2.32.4)\n",
+            "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.12/dist-packages (from datasets) (4.67.3)\n",
+            "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from datasets) (3.7.0)\n",
+            "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.12/dist-packages (from datasets) (0.70.16)\n",
+            "Requirement already satisfied: fsspec<=2025.3.0,>=2023.1.0 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2025.3.0)\n",
+            "Requirement already satisfied: huggingface-hub>=0.24.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (1.18.0)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from datasets) (26.2)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from datasets) (6.0.3)\n",
+            "Requirement already satisfied: regex>=2025.10.22 in /usr/local/lib/python3.12/dist-packages (from transformers) (2025.11.3)\n",
+            "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.2)\n",
+            "Requirement already satisfied: typer in /usr/local/lib/python3.12/dist-packages (from transformers) (0.25.1)\n",
+            "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.8.0)\n",
+            "Requirement already satisfied: nltk in /usr/local/lib/python3.12/dist-packages (from rouge_score) (3.9.1)\n",
+            "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.12/dist-packages (from rouge_score) (1.17.0)\n",
+            "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (3.14.1)\n",
+            "Requirement already satisfied: click>=8.4.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.24.0->datasets) (8.4.1)\n",
+            "Requirement already satisfied: hf-xet<2.0.0,>=1.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.24.0->datasets) (1.5.1)\n",
+            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.24.0->datasets) (0.28.1)\n",
+            "Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.24.0->datasets) (4.15.0)\n",
+            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.32.2->datasets) (3.4.7)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.32.2->datasets) (3.18)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.32.2->datasets) (2.5.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests>=2.32.2->datasets) (2026.5.20)\n",
+            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->transformers) (1.5.4)\n",
+            "Requirement already satisfied: rich>=13.8.0 in /usr/local/lib/python3.12/dist-packages (from typer->transformers) (13.9.4)\n",
+            "Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer->transformers) (0.0.4)\n",
+            "Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk->rouge_score) (1.5.3)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2026.2)\n",
+            "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2.6.2)\n",
+            "Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.4.0)\n",
+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (26.1.0)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.8.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (6.7.1)\n",
+            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (0.5.2)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.24.2)\n",
+            "Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.24.0->datasets) (4.13.0)\n",
+            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.24.0->datasets) (1.0.9)\n",
+            "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface-hub>=0.24.0->datasets) (0.16.0)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=13.8.0->typer->transformers) (4.2.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=13.8.0->typer->transformers) (2.20.0)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=13.8.0->typer->transformers) (0.1.2)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install datasets transformers evaluate rouge_score absl-py"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R8BbMrCWYjPG"
+      },
+      "source": [
+        "## 1. 뉴스 요약 데이터세트 불러오기 및 분할 (교재 예제 7.18)\n",
+        "미국의 AI 기업 아르길라(Argilla)가 공개한 뉴스 요약 데이터세트를 불러와 학습, 검증, 테스트 데이터로 분리합니다. 연산 속도를 확보하기 위해 5,000개의 샘플만 추출하여 사용합니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "rKyLLGXYYjPH",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b9216dd2-20d5-4ecd-c188-5986b76549d3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:112: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n",
+            "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+            "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Source News: DANANG, Vietnam (Reuters) - Russian President Vladimir Putin said on Saturday he had a normal dialogue with U.S. leader Donald Trump at a summit in Vietnam, and described Trump as civil, well-educated\n",
+            "Summarization: Putin says had useful interaction with Trump at Vi\n",
+            "Training Data Size: 3000\n",
+            "Validation Data Size: 1000\n",
+            "Testing Data Size: 1000\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
+            "  return bound(*args, **kwds)\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "# 데이터세트 불러오기\n",
+        "news = load_dataset(\"argilla/news-summary\", split=\"test\")\n",
+        "df = news.to_pandas().sample(5000, random_state=42)[[\"text\", \"prediction\"]]\n",
+        "\n",
+        "# 전처리: 중첩된 딕셔너리 구조에서 정답 텍스트만 추출\n",
+        "df[\"prediction\"] = df[\"prediction\"].map(lambda x: x[0][\"text\"])\n",
+        "\n",
+        "# 6:2:2 비율로 데이터 분할 (학습: 3000, 검증: 1000, 테스트: 1000)\n",
+        "train, valid, test = np.split(\n",
+        "    df.sample(frac=1, random_state=42),\n",
+        "    [int(0.6 * len(df)), int(0.8 * len(df))]\n",
+        ")\n",
+        "\n",
+        "print(f\"Source News: {train.text.iloc[0][:200]}\")\n",
+        "print(f\"Summarization: {train.prediction.iloc[0][:50]}\")\n",
+        "print(f\"Training Data Size: {len(train)}\")\n",
+        "print(f\"Validation Data Size: {len(valid)}\")\n",
+        "print(f\"Testing Data Size: {len(test)}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vTDI1bHwYjPI"
+      },
+      "source": [
+        "## 2. BART 입력 텐서 및 데이터로더 생성 (교재 예제 7.19)\n",
+        "BART 토크나이저를 활용하여 입력 문장과 정답 요약문을 토큰화하고 패딩을 적용합니다.\n",
+        "**BART 논문 및 교재 핵심 개념:** 교차 엔트로피 손실 함수에서 패딩된 토큰을 무시하도록 레이블 패딩 값을 `-100`으로 설정합니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "5b4BxlSgYjPK",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d0b4bae6-3cc9-4bed-fb73-dc5486c36311"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using device: cuda\n",
+            "데이터로더 구축 완료!\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from transformers import BartTokenizer\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.utils.data import RandomSampler, SequentialSampler\n",
+        "from torch.nn.utils.rnn import pad_sequence\n",
+        "\n",
+        "def make_dataset(data, tokenizer, device):\n",
+        "    # 본문 텍스트 토큰화\n",
+        "    tokenized = tokenizer(\n",
+        "        data.text.tolist(),\n",
+        "        padding=\"longest\",\n",
+        "        truncation=True,\n",
+        "        max_length=1024, # 텍스트를 그대로 토큰화하면, CUDA에서 수용 가능한 범위를 넘어가 학습 시 AcceleratorError가 발생하므로 수용 가능한 크기로 최대 길이를 제한\n",
+        "        return_tensors=\"pt\"\n",
+        "    )\n",
+        "\n",
+        "    labels = []\n",
+        "    input_ids = tokenized[\"input_ids\"].to(device)\n",
+        "    attention_mask = tokenized[\"attention_mask\"].to(device)\n",
+        "\n",
+        "    # 요약문(정답) 토큰화\n",
+        "    for target in data.prediction:\n",
+        "        labels.append(tokenizer.encode(target, truncation=True, max_length=128, return_tensors=\"pt\").squeeze())\n",
+        "\n",
+        "    # 손실 함수 계산 시 패딩 무시를 위해 padding_value=-100 사용\n",
+        "    padded_labels = pad_sequence(labels, batch_first=True, padding_value=-100).to(device)\n",
+        "    return TensorDataset(input_ids, attention_mask, padded_labels)\n",
+        "\n",
+        "def get_dataloader(dataset, sampler, batch_size):\n",
+        "    data_sampler = sampler(dataset)\n",
+        "    dataloader = DataLoader(dataset, sampler=data_sampler, batch_size=batch_size)\n",
+        "    return dataloader\n",
+        "\n",
+        "# 하이퍼파라미터 및 디바이스 설정\n",
+        "epochs = 3\n",
+        "batch_size = 8\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "print(f\"Using device: {device}\")\n",
+        "\n",
+        "# 토크나이저 초기화 및 데이터로더 생성\n",
+        "tokenizer = BartTokenizer.from_pretrained(pretrained_model_name_or_path=\"facebook/bart-base\")\n",
+        "\n",
+        "train_dataset = make_dataset(train, tokenizer, device)\n",
+        "train_dataloader = get_dataloader(train_dataset, RandomSampler, batch_size)\n",
+        "\n",
+        "valid_dataset = make_dataset(valid, tokenizer, device)\n",
+        "valid_dataloader = get_dataloader(valid_dataset, SequentialSampler, batch_size)\n",
+        "\n",
+        "test_dataset = make_dataset(test, tokenizer, device)\n",
+        "test_dataloader = get_dataloader(test_dataset, SequentialSampler, batch_size)\n",
+        "\n",
+        "print(\"데이터로더 구축 완료!\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 본문 및 요약문의 토큰화 시 개수 확인\n",
+        "# 본문 길이 분포 확인\n",
+        "text_lengths = [len(tokenizer.encode(t)) for t in train.text.tolist()]\n",
+        "print(f\"본문 - 평균: {np.mean(text_lengths):.0f}, 중앙값: {np.median(text_lengths):.0f}, 95%: {np.percentile(text_lengths, 95):.0f}, 최대: {max(text_lengths)}\")\n",
+        "\n",
+        "# 요약문 길이 분포 확인\n",
+        "summary_lengths = [len(tokenizer.encode(t)) for t in train.prediction.tolist()]\n",
+        "print(f\"요약 - 평균: {np.mean(summary_lengths):.0f}, 중앙값: {np.median(summary_lengths):.0f}, 95%: {np.percentile(summary_lengths, 95):.0f}, 최대: {max(summary_lengths)}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tSeTrABAHKPs",
+        "outputId": "3244ee4b-a513-404b-8f3e-299b1813f7ca"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "본문 - 평균: 498, 중앙값: 460, 95%: 1142, 최대: 3913\n",
+            "요약 - 평균: 15, 중앙값: 15, 95%: 20, 최대: 30\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "29ka76GWYjPM"
+      },
+      "source": [
+        "## 3. BART 조건부 생성 모델 및 최적화 함수 선언 (교재 예제 7.20)\n",
+        "조건부 생성 작업에 특화된 `BartForConditionalGeneration` 클래스를 사용해 6개 계층을 갖는 `facebook/bart-base` 모델을 인스턴스화합니다. 가중치 최적화를 위해 `AdamW` 알고리즘을 정의합니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "x-bpZIViYjPN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68,
+          "referenced_widgets": [
+            "241b49c563a5466d96699a2adc01854a",
+            "88c50fb1fffd40ea80e1cbc7451c6c07",
+            "a894cd7b0ba446b8b96d3efcaacb34ca",
+            "3f223e3dd0684d39baf222cb603f310c",
+            "6cfe35e7ab1f44c79741a9a5d0e7bccb",
+            "90d7606b346d4072a4ae44bb3ce20102",
+            "40e8ca7b0da54981ac936817e7a38c0b",
+            "a02bd94453ab499cbd000cf5cf110c7a",
+            "dcc7b59c097f43869ee0a983a8bfb02d",
+            "6588cafba8174c4eb5680c5836f9bdbd",
+            "f85f30f8848642e49bda0da8d1f6e610"
+          ]
+        },
+        "outputId": "a38b9347-8660-441d-f391-cd8e8bd18d10"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/259 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "241b49c563a5466d96699a2adc01854a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "BART 모델 및 옵티마이저 선언 완료!\n"
+          ]
+        }
+      ],
+      "source": [
+        "from torch import optim\n",
+        "from transformers import BartForConditionalGeneration\n",
+        "\n",
+        "# 모델 불러오기 및 GPU 배치\n",
+        "model = BartForConditionalGeneration.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"facebook/bart-base\"\n",
+        ").to(device)\n",
+        "\n",
+        "# 최적화 알고리즘 설정 (Learning Rate = 5e-5)\n",
+        "optimizer = optim.AdamW(model.parameters(), lr=5e-5, eps=1e-8)\n",
+        "\n",
+        "print(\"BART 모델 및 옵티마이저 선언 완료!\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o8BV_yIIYjPO"
+      },
+      "source": [
+        "## 4. 모델 학습 및 평가 루프 구성 (교재 예제 7.21 기반)\n",
+        "교재에서 수식화된 ROUGE-2 평가지표 계산 함수(`calc_rouge`)와 검증 루프(`evaluation`)를 포함하여, 전체 3 에포크 동안 파인 튜닝을 진행하는 전체 학습 파이프라인 코드입니다.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "filepath = './drive/MyDrive/Euron/data/BART/BartForConditionalGeneration.pt'"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aKiEHAbB6JxK",
+        "outputId": "e213e63d-21d6-4075-c6c2-2cc8a301edc7"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "S5zGjhyPYjPO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6dac5e5b-9361-48dc-c0fa-0893e7a97ae3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "🚀 BART 미세 조정 학습을 시작합니다. (약 20~30분 소요)\n",
+            "Epoch [1/3] | Step [100/375] | Loss: 1.9369\n",
+            "Epoch [1/3] | Step [200/375] | Loss: 1.6784\n",
+            "Epoch [1/3] | Step [300/375] | Loss: 2.2093\n",
+            "\n",
+            "[Epoch 1 결과] Train Loss: 2.1486 | Val Loss: 1.8580 | Val Rouge-2: 0.2617\n",
+            "⭐ 최고 성능 갱신! 모델 가중치 저장 완료.\n",
+            "\n",
+            "Epoch [2/3] | Step [100/375] | Loss: 1.4142\n",
+            "Epoch [2/3] | Step [200/375] | Loss: 1.5877\n",
+            "Epoch [2/3] | Step [300/375] | Loss: 1.7595\n",
+            "\n",
+            "[Epoch 2 결과] Train Loss: 1.5968 | Val Loss: 1.9024 | Val Rouge-2: 0.2594\n",
+            "Epoch [3/3] | Step [100/375] | Loss: 1.4237\n",
+            "Epoch [3/3] | Step [200/375] | Loss: 1.5095\n",
+            "Epoch [3/3] | Step [300/375] | Loss: 1.1385\n",
+            "\n",
+            "[Epoch 3 결과] Train Loss: 1.2608 | Val Loss: 1.9644 | Val Rouge-2: 0.2656\n",
+            "⭐ 최고 성능 갱신! 모델 가중치 저장 완료.\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "import evaluate\n",
+        "\n",
+        "# 허깅페이스 evaluate 라이브러리에서 ROUGE 메트릭 로드\n",
+        "rouge_score = evaluate.load(\"rouge\", tokenizer=tokenizer)\n",
+        "\n",
+        "def calc_rouge(preds, labels):\n",
+        "    # 확률 스코어가 가장 높은 토큰 인덱스 추출\n",
+        "    preds = preds.argmax(axis=-1)\n",
+        "\n",
+        "    # -100 패딩 값을 디코딩이 가능하도록 토크나이저의 pad_token_id로 복원\n",
+        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
+        "\n",
+        "    # 정수 토큰 배열을 실제 텍스트 문자열로 변환\n",
+        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
+        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
+        "\n",
+        "    # ROUGE 점수 계산\n",
+        "    rouge2 = rouge_score.compute(\n",
+        "        predictions=decoded_preds,\n",
+        "        references=decoded_labels\n",
+        "    )\n",
+        "    return rouge2[\"rouge2\"]\n",
+        "\n",
+        "def evaluation(model, dataloader):\n",
+        "    with torch.no_grad():\n",
+        "        model.eval() # 평가 모드 전환\n",
+        "        val_loss, val_rouge = 0.0, 0.0\n",
+        "\n",
+        "        for input_ids, attention_mask, labels in dataloader:\n",
+        "            outputs = model(\n",
+        "                input_ids=input_ids, attention_mask=attention_mask, labels=labels\n",
+        "            )\n",
+        "            logits = outputs.logits\n",
+        "            loss = outputs.loss\n",
+        "\n",
+        "            # 데이터 이동 및 넘파이 변환\n",
+        "            logits = logits.detach().cpu().numpy()\n",
+        "            label_ids = labels.to(\"cpu\").numpy()\n",
+        "\n",
+        "            rouge = calc_rouge(logits, label_ids)\n",
+        "            val_loss += loss.item()\n",
+        "            val_rouge += rouge\n",
+        "\n",
+        "        val_loss = val_loss / len(dataloader)\n",
+        "        val_rouge = val_rouge / len(dataloader)\n",
+        "        return val_loss, val_rouge\n",
+        "\n",
+        "# --- 본격적인 모델 학습 (Training Loop) Run ---\n",
+        "best_rouge = 0.0\n",
+        "\n",
+        "print(\"🚀 BART 미세 조정 학습을 시작합니다. (약 20~30분 소요)\")\n",
+        "for epoch in range(epochs):\n",
+        "    model.train() # 학습 모드 전환\n",
+        "    total_train_loss = 0.0\n",
+        "\n",
+        "    for step, (input_ids, attention_mask, labels) in enumerate(train_dataloader):\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
+        "        loss = outputs.loss\n",
+        "\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        total_train_loss += loss.item()\n",
+        "\n",
+        "        if (step + 1) % 100 == 0:\n",
+        "            print(f\"Epoch [{epoch+1}/{epochs}] | Step [{step+1}/{len(train_dataloader)}] | Loss: {loss.item():.4f}\")\n",
+        "\n",
+        "    # 에포크 종료 후 평가 수행\n",
+        "    avg_train_loss = total_train_loss / len(train_dataloader)\n",
+        "    val_loss, val_rouge = evaluation(model, valid_dataloader)\n",
+        "\n",
+        "    print(f\"\\n[Epoch {epoch+1} 결과] Train Loss: {avg_train_loss:.4f} | Val Loss: {val_loss:.4f} | Val Rouge-2: {val_rouge:.4f}\")\n",
+        "\n",
+        "    # 최고 성능 모델 저장\n",
+        "    if val_rouge > best_rouge:\n",
+        "        best_rouge = val_rouge\n",
+        "        torch.save(model.state_dict(), filepath)\n",
+        "        print(\"⭐ 최고 성능 갱신! 모델 가중치 저장 완료.\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4dOaOfgyYjPP"
+      },
+      "source": [
+        "## 5. 최종 테스트 데이터세트 평가 (교재 예제 7.22)\n",
+        "학습 과정에서 검증 세트 기준 성능이 가장 우수했던 모델 가중치를 로드하여, 학습에 쓰이지 않은 최종 테스트 데이터를 대상으로 평가를 수행합니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "rvV_IQXoYjPP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 86,
+          "referenced_widgets": [
+            "40cd1151b9cf45b989b76761fc7608af",
+            "9c13c216261543e99ef5eab42bb2c8d1",
+            "3035f38b29b7480c90fa395237b8de4c",
+            "4b96ca9a6c0a484aa99b3858cfd51c6f",
+            "6ff067161b3c45f48f130215f68c48e0",
+            "8e1c3a9b79e94ace89392a27392d1dce",
+            "43bc9160c08f4bc082712c08db4f4141",
+            "08c22a9c6cca4bcfa410f87a0edff02e",
+            "c70ffe2c706040288dca43710f8e07d9",
+            "a41499ed1a1a4cd88951136d09cd783e",
+            "2657f0d9160b4492ac3cdd32af5c1057"
+          ]
+        },
+        "outputId": "007448c7-b883-4ba4-a929-5a519093ecb1"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/259 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "40cd1151b9cf45b989b76761fc7608af"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss: 1.9147\n",
+            "Test ROUGE-2 Score: 0.2654\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 저장된 최고의 가중치 가동\n",
+        "model = BartForConditionalGeneration.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"facebook/bart-base\"\n",
+        ").to(device)\n",
+        "model.load_state_dict(torch.load(filepath))\n",
+        "\n",
+        "# 최종 평가 실행\n",
+        "test_loss, test_rouge_score = evaluation(model, test_dataloader)\n",
+        "print(f\"Test Loss: {test_loss:.4f}\")\n",
+        "print(f\"Test ROUGE-2 Score: {test_rouge_score:.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g9i9ZBatYjPP"
+      },
+      "source": [
+        "## 6. 모델 요약 생성 문장 실제 비교 (교재 예제 7.23)\n",
+        "Hugging Face의 `pipeline` 추론 함수를 활용해 실제 뉴스 원문(Test 데이터)에 대한 요약문을 생성하고, 이를 정답 요약문과 직접 눈으로 비교하며 품질을 확인합니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "Duxpci-UYjPP",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b86244db-2039-438b-cfa2-d993d2412ae6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1번 뉴스 요약 비교]\n",
+            "정답 요약문 : Clinton leads Trump by 4 points in Washington Post: ABC News poll\n",
+            "모델 요약문 : Clinton leads Trump by 4 points in four-war race: Washington Post-ABC News poll\n",
+            "--------------------------------------------------\n",
+            "[2번 뉴스 요약 비교]\n",
+            "정답 요약문 : Democrats question independence of Trump Supreme Court nominee\n",
+            "모델 요약문 : Democrats sharpen attack on Supreme Court nominee\n",
+            "--------------------------------------------------\n",
+            "[3번 뉴스 요약 비교]\n",
+            "정답 요약문 : In push for Yemen aid, U.S. warned Saudis of threats in Congress\n",
+            "모델 요약문 : U.S. warns Saudi Arabia against blocking aid access: official\n",
+            "--------------------------------------------------\n",
+            "[4번 뉴스 요약 비교]\n",
+            "정답 요약문 : Romanian ruling party leader investigated over 'criminal group'\n",
+            "모델 요약문 : Romania investigates leader of ruling Social Democrat Party\n",
+            "--------------------------------------------------\n",
+            "[5번 뉴스 요약 비교]\n",
+            "정답 요약문 : Billionaire environmental activist Tom Steyer endorses Clinton\n",
+            "모델 요약문 : Environmental activist Steyer endorsed Hillary Clinton for president\n",
+            "--------------------------------------------------\n"
+          ]
+        }
+      ],
+      "source": [
+        "### transformers.pipeline의 \"summarization\" task가 5.x 버전에서 제거되어, encoder-decoder 구조에 적합한 model.generate()로 대체\n",
+        "\n",
+        "# 상위 5개 데이터 추출 및 비교 분석\n",
+        "for index in range(5):\n",
+        "    news_text = test.text.iloc[index]\n",
+        "    summarization = test.prediction.iloc[index]\n",
+        "\n",
+        "    # 입력 토큰화\n",
+        "    inputs = tokenizer(news_text, return_tensors=\"pt\", truncation=True, max_length=512).to(device)\n",
+        "\n",
+        "    # 모델로 요약 생성\n",
+        "    summary_ids = model.generate(\n",
+        "        inputs[\"input_ids\"],\n",
+        "        max_length=54,\n",
+        "        num_beams=4,\n",
+        "        early_stopping=True\n",
+        "    )\n",
+        "\n",
+        "    # 토큰 → 텍스트 변환\n",
+        "    predicted_summarization = tokenizer.decode(summary_ids[0], skip_special_tokens=True)\n",
+        "\n",
+        "    print(f\"[{index+1}번 뉴스 요약 비교]\")\n",
+        "    print(f\"정답 요약문 : {summarization}\")\n",
+        "    print(f\"모델 요약문 : {predicted_summarization}\")\n",
+        "    print(\"-\" * 50)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU",
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "241b49c563a5466d96699a2adc01854a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_88c50fb1fffd40ea80e1cbc7451c6c07",
+              "IPY_MODEL_a894cd7b0ba446b8b96d3efcaacb34ca",
+              "IPY_MODEL_3f223e3dd0684d39baf222cb603f310c"
+            ],
+            "layout": "IPY_MODEL_6cfe35e7ab1f44c79741a9a5d0e7bccb"
+          }
+        },
+        "88c50fb1fffd40ea80e1cbc7451c6c07": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_90d7606b346d4072a4ae44bb3ce20102",
+            "placeholder": "​",
+            "style": "IPY_MODEL_40e8ca7b0da54981ac936817e7a38c0b",
+            "value": "Loading weights: 100%"
+          }
+        },
+        "a894cd7b0ba446b8b96d3efcaacb34ca": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "success",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_a02bd94453ab499cbd000cf5cf110c7a",
+            "max": 259,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_dcc7b59c097f43869ee0a983a8bfb02d",
+            "value": 259
+          }
+        },
+        "3f223e3dd0684d39baf222cb603f310c": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_6588cafba8174c4eb5680c5836f9bdbd",
+            "placeholder": "​",
+            "style": "IPY_MODEL_f85f30f8848642e49bda0da8d1f6e610",
+            "value": " 259/259 [00:00&lt;00:00, 5057.47it/s]"
+          }
+        },
+        "6cfe35e7ab1f44c79741a9a5d0e7bccb": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "90d7606b346d4072a4ae44bb3ce20102": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "40e8ca7b0da54981ac936817e7a38c0b": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "a02bd94453ab499cbd000cf5cf110c7a": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "dcc7b59c097f43869ee0a983a8bfb02d": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "6588cafba8174c4eb5680c5836f9bdbd": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "f85f30f8848642e49bda0da8d1f6e610": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "40cd1151b9cf45b989b76761fc7608af": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_9c13c216261543e99ef5eab42bb2c8d1",
+              "IPY_MODEL_3035f38b29b7480c90fa395237b8de4c",
+              "IPY_MODEL_4b96ca9a6c0a484aa99b3858cfd51c6f"
+            ],
+            "layout": "IPY_MODEL_6ff067161b3c45f48f130215f68c48e0"
+          }
+        },
+        "9c13c216261543e99ef5eab42bb2c8d1": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_8e1c3a9b79e94ace89392a27392d1dce",
+            "placeholder": "​",
+            "style": "IPY_MODEL_43bc9160c08f4bc082712c08db4f4141",
+            "value": "Loading weights: 100%"
+          }
+        },
+        "3035f38b29b7480c90fa395237b8de4c": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "success",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_08c22a9c6cca4bcfa410f87a0edff02e",
+            "max": 259,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_c70ffe2c706040288dca43710f8e07d9",
+            "value": 259
+          }
+        },
+        "4b96ca9a6c0a484aa99b3858cfd51c6f": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_a41499ed1a1a4cd88951136d09cd783e",
+            "placeholder": "​",
+            "style": "IPY_MODEL_2657f0d9160b4492ac3cdd32af5c1057",
+            "value": " 259/259 [00:00&lt;00:00, 3933.21it/s]"
+          }
+        },
+        "6ff067161b3c45f48f130215f68c48e0": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "8e1c3a9b79e94ace89392a27392d1dce": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "43bc9160c08f4bc082712c08db4f4141": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "08c22a9c6cca4bcfa410f87a0edff02e": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "c70ffe2c706040288dca43710f8e07d9": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "a41499ed1a1a4cd88951136d09cd783e": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "2657f0d9160b4492ac3cdd32af5c1057": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git "a/Week13_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\354\234\244\354\204\234.pdf" "b/Week13_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\354\234\244\354\204\234.pdf"
new file mode 100644
index 0000000..b8affaf
Binary files /dev/null and "b/Week13_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\354\234\244\354\204\234.pdf" differ