diff --git "a/Week15_\353\263\265\354\212\265\352\263\274\354\240\234_\353\260\225\354\230\210\353\202\230.ipynb" "b/Week15_\353\263\265\354\212\265\352\263\274\354\240\234_\353\260\225\354\230\210\353\202\230.ipynb"
new file mode 100644
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+++ "b/Week15_\353\263\265\354\212\265\352\263\274\354\240\234_\353\260\225\354\230\210\353\202\230.ipynb"
@@ -0,0 +1,9247 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "4644be45",
+      "metadata": {
+        "id": "4644be45"
+      },
+      "source": [
+        "# BERT와 ELECTRA 모델 비교 실습\n",
+        "\n",
+        "- 이번 복습과제에서는 SST-2 데이터셋을 기반으로 BERT와 ELECTRA 모델을 학습시켜보고 성능과 구조의 차이를 알아보겠습니다.\n",
+        "- 코드 실행시간이 매우 길 수 있습니다.\n",
+        "  - 최대한 끝까지 실행해보시되, 시간 부족으로 인해 중간에 중지하신 실행 결과를 제출하셔도 괜찮습니다.\n",
+        "  - 제출 이후에는 꼭 끝까지 실행시켜 비교해보시기 바랍니다!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2eSXxqg4a9SF",
+      "metadata": {
+        "id": "2eSXxqg4a9SF"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install --upgrade --quiet datasets fsspec huggingface_hub"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "Pe9t4ac76iP8",
+      "metadata": {
+        "id": "Pe9t4ac76iP8"
+      },
+      "outputs": [],
+      "source": [
+        "!pip uninstall transformers huggingface-hub -y\n",
+        "!pip install transformers\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4rq77NfBbByn",
+      "metadata": {
+        "id": "4rq77NfBbByn"
+      },
+      "source": [
+        "---------------\n",
+        "여기까지만 실행\n",
+        "---------------\n",
+        "그 다음,  런타임 > 세션 다시 시작 > 아래 셀부터 실행"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "ac52f320",
+      "metadata": {
+        "id": "ac52f320"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from torch.utils.data import DataLoader\n",
+        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+        "from datasets import load_dataset\n",
+        "from torch.optim import AdamW\n",
+        "from tqdm import tqdm"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "393f4136",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "393f4136",
+        "outputId": "a334045f-0d81-4be2-963f-501fd3f0598c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using device: cuda\n"
+          ]
+        }
+      ],
+      "source": [
+        "# batch_size와 epochs를 조정해보세요!\n",
+        "batch_size = 16\n",
+        "epochs = 2\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(f\"Using device: {device}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "QTVKkGiIflzk",
+      "metadata": {
+        "colab": {
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+          "height": 615,
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+          ]
+        },
+        "id": "QTVKkGiIflzk",
+        "outputId": "2d09f6ef-7f10-4ec5-dc7c-5fbd5da148e1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: 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: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "10c02cbc562c4f49bc7a3821d0628f87"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "data/train-00000-of-00001.parquet:   0%|          | 0.00/3.11M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "560fa346d8d04e12ad202497534b19c0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "data/validation-00000-of-00001.parquet:   0%|          | 0.00/72.8k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "314e0752ac2b4c09a494ab1a80922ae4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "data/test-00000-of-00001.parquet:   0%|          | 0.00/148k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7275a3b45170494d830c05d90dbba5af"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/67349 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "76bec94149b5405da20788ca84afb706"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split:   0%|          | 0/872 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5e6656a52d4c4805a57f7b61fc5b487e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split:   0%|          | 0/1821 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "65f2d5d60f8a48be8e824fe7ca87dead"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DatasetDict({\n",
+              "    train: Dataset({\n",
+              "        features: ['idx', 'sentence', 'label'],\n",
+              "        num_rows: 67349\n",
+              "    })\n",
+              "    validation: Dataset({\n",
+              "        features: ['idx', 'sentence', 'label'],\n",
+              "        num_rows: 872\n",
+              "    })\n",
+              "    test: Dataset({\n",
+              "        features: ['idx', 'sentence', 'label'],\n",
+              "        num_rows: 1821\n",
+              "    })\n",
+              "})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "# 데이터셋 로드\n",
+        "raw_datasets = load_dataset(\"sst2\")\n",
+        "raw_datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "Y_zJtt34ftoZ",
+      "metadata": {
+        "id": "Y_zJtt34ftoZ"
+      },
+      "outputs": [],
+      "source": [
+        "# 전처리\n",
+        "def tokenize_function(examples, tokenizer):\n",
+        "    return tokenizer(examples[\"sentence\"], padding=\"max_length\", truncation=True, max_length=128)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4e3b88e4",
+      "metadata": {
+        "id": "4e3b88e4"
+      },
+      "source": [
+        "## 🔹 BERT와 ELECTRA 실험"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "_ehi3D9Mf9ov",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
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+        "collapsed": true,
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+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "======== Now Training: bert-base-uncased ========\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
+            ],
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+            "text/plain": [
+              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+          "data": {
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+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/67349 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/872 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1821 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e7ed9ebe8dd84c92bcb0b32b516a3c40"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6977d8fe6488447088b726993497c222"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
+            "Epoch 1: 100%|██████████| 4210/4210 [23:41<00:00,  2.96it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1 - Avg Train Loss: 0.2025\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 2: 100%|██████████| 4210/4210 [23:42<00:00,  2.96it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 2 - Avg Train Loss: 0.1059\n",
+            "Validation Accuracy (bert-base-uncased): 0.9140\n",
+            "\n",
+            "======== Now Training: google/electra-base-discriminator ========\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/67349 [00:00<?, ? examples/s]"
+            ],
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+        {
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+            "text/plain": [
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+            ],
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+              "model_id": "dbcea47f56fd472b917d8b72bfeb02cd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "pytorch_model.bin:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at google/electra-base-discriminator and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
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+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "Epoch 1:   0%|          | 0/4210 [00:00<?, ?it/s]\u001b[A\n",
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+            "Epoch 1:  84%|████████▎ | 3523/4210 [20:17<03:54,  2.93it/s]\u001b[A\n",
+            "Epoch 1:  88%|████████▊ | 3699/4210 [21:03<02:54,  2.93it/s]\u001b[A\n",
+            "Epoch 1:  88%|████████▊ | 3699/4210 [21:17<02:54,  2.93it/s]\u001b[A\n",
+            "Epoch 1:  92%|█████████▏| 3876/4210 [22:04<01:53,  2.93it/s]\u001b[A\n",
+            "Epoch 1:  92%|█████████▏| 3876/4210 [22:17<01:53,  2.93it/s]\u001b[A\n",
+            "Epoch 1:  96%|█████████▌| 4052/4210 [23:04<00:53,  2.93it/s]\u001b[A\n",
+            "Epoch 1: 100%|██████████| 4210/4210 [23:58<00:00,  2.93it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1 - Avg Train Loss: 0.1834\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 2: 100%|██████████| 4210/4210 [23:56<00:00,  2.93it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 2 - Avg Train Loss: 0.1097\n",
+            "Validation Accuracy (google/electra-base-discriminator): 0.9427\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 학습 함수 정의\n",
+        "def train_and_evaluate(model_name):\n",
+        "    print(f\"\\n======== Now Training: {model_name} ========\")\n",
+        "\n",
+        "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+        "    tokenized_datasets = raw_datasets.map(lambda x: tokenize_function(x, tokenizer), batched=True)\n",
+        "\n",
+        "    tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
+        "    tokenized_datasets.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n",
+        "    train_dataset = tokenized_datasets[\"train\"]\n",
+        "    valid_dataset = tokenized_datasets[\"validation\"]\n",
+        "\n",
+        "    train_loader = DataLoader(train_dataset, batch_size)\n",
+        "    valid_loader = DataLoader(valid_dataset, batch_size)\n",
+        "\n",
+        "    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2).to(device)\n",
+        "    optimizer = AdamW(model.parameters(), lr=2e-5)\n",
+        "\n",
+        "    model.train()\n",
+        "    for epoch in range(epochs):\n",
+        "        total_loss = 0\n",
+        "        for batch in tqdm(train_loader, desc=f\"Epoch {epoch+1}\", mininterval=60):\n",
+        "            batch = {k: v.to(device) for k, v in batch.items()}\n",
+        "            outputs = model(\n",
+        "                input_ids=batch[\"input_ids\"],\n",
+        "                attention_mask=batch[\"attention_mask\"],\n",
+        "                labels=batch[\"labels\"],\n",
+        "            )\n",
+        "            loss = outputs.loss\n",
+        "            total_loss += loss.item()\n",
+        "\n",
+        "            loss.backward()\n",
+        "            optimizer.step()\n",
+        "            optimizer.zero_grad()\n",
+        "\n",
+        "        avg_loss = total_loss / len(train_loader)\n",
+        "        print(f\"Epoch {epoch+1} - Avg Train Loss: {avg_loss:.4f}\")\n",
+        "\n",
+        "    model.eval()\n",
+        "    correct = 0\n",
+        "    total = 0\n",
+        "    with torch.no_grad():\n",
+        "        for batch in valid_loader:\n",
+        "            batch = {k: v.to(device) for k, v in batch.items()}\n",
+        "            outputs = model(**batch)\n",
+        "            predictions = torch.argmax(outputs.logits, dim=-1)\n",
+        "            correct += (predictions == batch[\"labels\"]).sum().item()\n",
+        "            total += batch[\"labels\"].size(0)\n",
+        "\n",
+        "    acc = correct / total\n",
+        "    print(f\"Validation Accuracy ({model_name}): {acc:.4f}\")\n",
+        "    return acc\n",
+        "\n",
+        "# 실행 및 평가\n",
+        "bert_acc = train_and_evaluate(\"bert-base-uncased\")\n",
+        "electra_acc = train_and_evaluate(\"google/electra-base-discriminator\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1616c7fb",
+      "metadata": {
+        "id": "1616c7fb"
+      },
+      "source": [
+        "## 📊 결과 비교 및 분석\n",
+        "\n",
+        "아래 항목에 대한 답을 간략히 적어주세요:\n",
+        "\n",
+        "1. 각 모델 구조 설명\n",
+        "2. 어떤 모델이 적합한지에 대한 본인의 의견\n",
+        "  - 학습 속도, accuracy 등 고려\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "78IvAec4h_7H",
+      "metadata": {
+        "id": "78IvAec4h_7H"
+      },
+      "source": [
+        "\n",
+        "---\n",
+        "1.\n",
+        "BERT는 마스킹된 언어 모델링 방식으로 문장의 일부 토큰을 마스킹하여 예측하는 MLM 방식으로 학습하는 트랜스포머 기반 양방향 인코더 구조의 모델이다.\n",
+        "ELECTRA는 생성자와 판별자를 사용해 토큰이 원본인지 생성된 것인지 판별해 학습하는 트랜스포머 인코더 구조를 따르는 모델이다.\n",
+        "\n",
+        "\n",
+        "2.\n",
+        "실험 결과 두 모델의 학습 시간은 비슷했지만, ELECTRA가 BERT보다 더 높은 Validation Accuracy(0.9427 vs 0.9140)를 보였다. ELECTRA의 학습 방식이 SST-2의 감성 분류 문제에 더 적합하게 작용했기 때문으로 볼 수 있을 것 같다.\n",
+        "따라서 ELECTRA가 BERT보다 더 적합하다고 생각한다."
+      ]
+    }
+  ],
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