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@@ -0,0 +1,5404 @@
+{
+  "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": "5cf7a70b-e2c4-4970-9bd4-6f13d4b703e6"
+      },
+      "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",
+            "Collecting evaluate\n",
+            "  Downloading evaluate-0.4.6-py3-none-any.whl.metadata (9.5 kB)\n",
+            "Collecting rouge_score\n",
+            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\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",
+            "Downloading evaluate-0.4.6-py3-none-any.whl (84 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hBuilding wheels for collected packages: rouge_score\n",
+            "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for rouge_score: filename=rouge_score-0.1.2-py3-none-any.whl size=24934 sha256=a3c78a9e7ef9798bf5269c45ac9778d587d0110b0fcdf4e6e675ffc80adfdddc\n",
+            "  Stored in directory: /root/.cache/pip/wheels/85/9d/af/01feefbe7d55ef5468796f0c68225b6788e85d9d0a281e7a70\n",
+            "Successfully built rouge_score\n",
+            "Installing collected packages: rouge_score, evaluate\n",
+            "Successfully installed evaluate-0.4.6 rouge_score-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/",
+          "height": 533,
+          "referenced_widgets": [
+            "9b428c251fb4403caf0a7faa608ba63d",
+            "dc1adeab56d749b79958ef05e17b2dba",
+            "211f8695a7cb424cb1d2a2b942168b98",
+            "2c8e5849b1ea461794eaf330da6991eb",
+            "9bc4603fe5c74a409490f5848598d2c3",
+            "3bf4a3fb458748408b4b594e52dfa710",
+            "a75f9023adac4a008ec0f559ee93673e",
+            "d8921ee59e134e6397c907761e4284c1",
+            "207c2c053113440a9c809c423e0ed5f1",
+            "83c55f3d8ea3416295446d289bce15c4",
+            "a6b4236f16c141c592ed06e032c30634",
+            "b86fedff9b84451d9e6313725e71c64d",
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+            "ef6f15a423d746488fc0a7a237fc1cbd",
+            "24915bfd227f4fdfad7151b99aee4b38",
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+            "b1a417064c08432486cf91c806a983cb",
+            "565c06d5355e4eb4942e1582598dd6f9",
+            "3bfbcc5855d142a9b182cf7b4794b83c",
+            "29dc692a564a48ca92d88aee35664ec8",
+            "909104d9718f41ffa3b4336882b99c84",
+            "f0f36c6ea7db4bdf8202ef23c529eae9",
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+            "8ac75cc3c8d84a9aba5b5d2b413bd71a",
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+            "2b8e3eb34d154cc29bd798a92320e07e",
+            "e2e1096108e9402196b809c1c8c1de9c"
+          ]
+        },
+        "outputId": "e28fad27-01c6-4705-aaf7-45cdf3fc7f3b"
+      },
+      "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"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/2.02k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9b428c251fb4403caf0a7faa608ba63d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "data/train-00000-of-00001-ebc48879f34571(…):   0%|          | 0.00/1.54M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b86fedff9b84451d9e6313725e71c64d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "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": "display_data",
+          "data": {
+            "text/plain": [
+              "data/test-00000-of-00001-6227bd8eb10a9b5(…):   0%|          | 0.00/31.7M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8636eca42b034a70b4fe5c7ba353ed7f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1a3389a9dbf94a45bffb3f546ce97836"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split:   0%|          | 0/20417 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3bfbcc5855d142a9b182cf7b4794b83c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "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/",
+          "height": 150,
+          "referenced_widgets": [
+            "4fb1aff6cfbf49e48c49084ecb8c8f8f",
+            "286190ae9b29488eb60be20574e23007",
+            "124d7d925167483493660c476c3c1c7f",
+            "74394383bd3f4f5bbe501c348140519c",
+            "e4c90217eadb43a1b6b85d3d811915da",
+            "f21a223531fd4886abc22110b9608f0d",
+            "12561433b6874aa99b6f6df3868d88c2",
+            "5b98cde592c44d379a90b1af5ae82f55",
+            "ce62812743474394a877e52e805653e9",
+            "02d87f32a2f84831962d973524d9f3cf",
+            "00a9fad3b3d24316bad24a40537727af",
+            "a7e397cddddd479797cad5fe1456815e",
+            "2e97007223e14846bd212d76c03829f0",
+            "ba8f29fe07654071909641d61836fc2d",
+            "0eb92603a5f64f58941de5f85760f070",
+            "a704a9006f0141bc90be67aa9e8ebfc2",
+            "bd5c99ea91154b4cbc2b5dae9131b7af",
+            "82315b86468443beb15c8af07c57091b",
+            "a2596a6adf4c424f945c7502eb20ece8",
+            "43cbe94f6aab4a56b5158cdcab934abe",
+            "8b46a209963945ec8b48e06ff1a80be0",
+            "1c64d85d26b248a1a4d61dff8d9e385b",
+            "83654a82dde647b6be5fabfb2e5953ec",
+            "85d76356d41b4bff94b12e0479b73d32",
+            "ceb7b7f15fde44849a235596b29ad75a",
+            "07d742d8806848fdb35c0c425f73a512",
+            "9a92493478004c69a6ae41f0ca316c15",
+            "a7305629b45f46309ec66565fd71ac54",
+            "a36649401cdc4a40bb985531c20fb6a9",
+            "27fc44da3e024612af43e777ad6a5d63",
+            "b377766c75e24a078a8369c13d4756f1",
+            "6b77c9c7d3814931bbcee05a18caed05",
+            "b7a7f466ed6b4f56a5be60363ad765b5"
+          ]
+        },
+        "outputId": "b9e9fa6c-b272-4c39-9c24-8275c4bc5e94"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using device: cuda\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4fb1aff6cfbf49e48c49084ecb8c8f8f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a7e397cddddd479797cad5fe1456815e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "83654a82dde647b6be5fabfb2e5953ec"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "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,\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, 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": "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": 4,
+      "metadata": {
+        "id": "x-bpZIViYjPN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 132,
+          "referenced_widgets": [
+            "4dde683665e247cba279c805789e1d04",
+            "dd40f0715f3b4460af960c850414a7d2",
+            "ad51530f5d3f4929b25629564658d6c2",
+            "34372ba409a04d1f98379e9931c018fa",
+            "66b0e021571441efbe4e5c47a117c7f3",
+            "7795f71312884af1bcc70136447cfd58",
+            "907f2449eef847a7907bacf63c8d3f91",
+            "dae2fe5d5eed4e26a24bf5d0c34e5de7",
+            "b183065a9cb042fbb8176a92d8aa140b",
+            "fbd1720db72645b881247076e2746faa",
+            "422505bb4ae34cb6b581561d69a8f5f2",
+            "2d7349bf89cf475f98e7927a675cbfa3",
+            "27f320f40a694ca59ede2fb91dd178be",
+            "2d220c28319b4a5db9ce2edd946c5d2c",
+            "4aba299f302c4236a63965cd202ff55b",
+            "d919685d581440238ea658c097bb2fc8",
+            "7054c5893a0240d9ad2d947494870780",
+            "a664433c13da4280a2578e9f0cfd657b",
+            "939a119cfd9c4287941950c21523f02d",
+            "ef8706e7909a45a3aef5992c5e8c4d27",
+            "e9f7fc64c9c54baba5713b03e4918721",
+            "dd75d3e6761f4f6aaa60ee6078c77194",
+            "a4fed4ecd82542fa929d8532a52f005e",
+            "3b05cee14f674994a00c0fff9af9965f",
+            "92ca6eebbbdb4d8ab2f42c317c48c34d",
+            "d6a27b9356f5435d81dec623e44d16f7",
+            "f6ed465544964b3d86dd82f2ccf85a39",
+            "9159d59e55a54430b45d4d8a613c9840",
+            "7232962fb85a4916ac511d57592a85ce",
+            "a0426326de7f4becb5c277d2f0593913",
+            "8a130a294c0e45208a4a1c3309bca90a",
+            "bb94e43f2dff4131951fd2239256b910",
+            "62cbbaebecc144f1a32eef4875d6c736"
+          ]
+        },
+        "outputId": "8b5dd2f7-afe6-4c08-9111-f82fd5583a87"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/1.72k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4dde683665e247cba279c805789e1d04"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/558M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2d7349bf89cf475f98e7927a675cbfa3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "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": "a4fed4ecd82542fa929d8532a52f005e"
+            }
+          },
+          "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",
+      "execution_count": 5,
+      "metadata": {
+        "id": "S5zGjhyPYjPO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423,
+          "referenced_widgets": [
+            "18a601a62f1e46439e32dca43d228276",
+            "79d23968184a40ecb988fdaa3367556c",
+            "d966841144844b4398072d8e208042b7",
+            "c85c6420442c443f9d59dc239292e6a6",
+            "c75b137434204c8191606547a004326c",
+            "ce12502b7298484fbb08837973986db8",
+            "fd09762c7ed542a489a2d1e7c1dedde7",
+            "7a018b0107fc4a14be5013452b2cdaac",
+            "4da997356ddb47b089b7cdd277e595d8",
+            "2da5db99723a4f719c8ac633418f9022",
+            "6e34703a605549d1957df8747dcc96c8"
+          ]
+        },
+        "outputId": "bbe6c23f-3783-43f3-c58d-feabcc06edfd"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "18a601a62f1e46439e32dca43d228276"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "🚀 BART 미세 조정 학습을 시작합니다. (약 20~30분 소요)\n",
+            "Epoch [1/3] | Step [100/375] | Loss: 1.7564\n",
+            "Epoch [1/3] | Step [200/375] | Loss: 2.7217\n",
+            "Epoch [1/3] | Step [300/375] | Loss: 2.5350\n",
+            "\n",
+            "[Epoch 1 결과] Train Loss: 2.1690 | Val Loss: 1.8551 | Val Rouge-2: 0.2574\n",
+            "⭐ 최고 성능 갱신! 모델 가중치 저장 완료.\n",
+            "\n",
+            "Epoch [2/3] | Step [100/375] | Loss: 1.5581\n",
+            "Epoch [2/3] | Step [200/375] | Loss: 1.5573\n",
+            "Epoch [2/3] | Step [300/375] | Loss: 1.6601\n",
+            "\n",
+            "[Epoch 2 결과] Train Loss: 1.6026 | Val Loss: 1.8727 | Val Rouge-2: 0.2651\n",
+            "⭐ 최고 성능 갱신! 모델 가중치 저장 완료.\n",
+            "\n",
+            "Epoch [3/3] | Step [100/375] | Loss: 0.9571\n",
+            "Epoch [3/3] | Step [200/375] | Loss: 1.2082\n",
+            "Epoch [3/3] | Step [300/375] | Loss: 1.1788\n",
+            "\n",
+            "[Epoch 3 결과] Train Loss: 1.2372 | Val Loss: 1.9365 | Val Rouge-2: 0.2550\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",
+        "import os\n",
+        "os.makedirs(\"../models\", exist_ok=True)\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(), \"../models/BartForConditionalGeneration.pt\")\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": 6,
+      "metadata": {
+        "id": "rvV_IQXoYjPP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 86,
+          "referenced_widgets": [
+            "0c6293ca44294cd584db2ed62223b132",
+            "c00d7847b59446c8bcd7781d27ba38ef",
+            "c7e943f25b7845dbbe589260fd41b7b3",
+            "f075643910f94aeb9446cda06624cc05",
+            "93f0fe1390724d3b83fdea6fa01a4585",
+            "1ea0624acdf54913910e04f17f7ec565",
+            "bea733735361491e8136e2af29b0c182",
+            "a77de872637e4b5b87b33a5e86aaf806",
+            "e2522e2fdb3446d1b3b6523fde888b50",
+            "bdada99845c04be7a98cf25a81615f3e",
+            "79ddae16b152428a8031155cb9b3d21d"
+          ]
+        },
+        "outputId": "e0268ea0-7279-4990-9665-24e3166feb73"
+      },
+      "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": "0c6293ca44294cd584db2ed62223b132"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss: 1.8237\n",
+            "Test ROUGE-2 Score: 0.2632\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(\"../models/BartForConditionalGeneration.pt\"))\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": "97ea848e-3692-46a1-c05e-3138a0b9d21d"
+      },
+      "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 percentage points in 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 sharpens line on Gorsuch's Supreme Court confirmation\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 over humanitarian situation in Yemen\n",
+            "--------------------------------------------------\n",
+            "[4번 뉴스 요약 비교]\n",
+            "정답 요약문 : Romanian ruling party leader investigated over 'criminal group'\n",
+            "모델 요약문 : Romania opens criminal probe into ruling party leader over graft\n",
+            "--------------------------------------------------\n",
+            "[5번 뉴스 요약 비교]\n",
+            "정답 요약문 : Billionaire environmental activist Tom Steyer endorses Clinton\n",
+            "모델 요약문 : Steyer endorsed Hillary Clinton for U.S. president: statement\n",
+            "--------------------------------------------------\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "model.to(device)\n",
+        "model.eval()\n",
+        "\n",
+        "def summarize(text, max_length=54, max_input_length=1024):\n",
+        "    inputs = tokenizer(\n",
+        "        text,\n",
+        "        return_tensors=\"pt\",\n",
+        "        truncation=True,\n",
+        "        max_length=max_input_length  # 모델 입력 최대 길이를 직접 지정\n",
+        "    ).to(device)\n",
+        "\n",
+        "    with torch.no_grad():\n",
+        "        summary_ids = model.generate(\n",
+        "            **inputs,\n",
+        "            max_length=max_length,\n",
+        "            num_beams=4,\n",
+        "            no_repeat_ngram_size=3,\n",
+        "            early_stopping=True\n",
+        "        )\n",
+        "\n",
+        "    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)\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",
+        "    predicted_summarization = summarize(news_text, max_length=54)\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"
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