From 9c6b8ea849227818efa470e804d1497ee3171f9b Mon Sep 17 00:00:00 2001
From: i090i <133558607+i090i@users.noreply.github.com>
Date: Tue, 16 Dec 2025 20:36:32 +0900
Subject: [PATCH 1/2] =?UTF-8?q?Week15=5F=EC=98=88=EC=8A=B5=EA=B3=BC?=
 =?UTF-8?q?=EC=A0=9C=5F=EC=95=88=EC=A7=80=ED=98=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
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 1 file changed, 0 insertions(+), 0 deletions(-)
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diff --git "a/Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\354\225\210\354\247\200\355\230\204.pdf" "b/Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\354\225\210\354\247\200\355\230\204.pdf"
new file mode 100644
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From 86745a589f5813d4e7507ccbcd8da84fe3c6a7cb Mon Sep 17 00:00:00 2001
From: i090i <133558607+i090i@users.noreply.github.com>
Date: Tue, 23 Dec 2025 17:39:25 +0900
Subject: [PATCH 2/2] =?UTF-8?q?Week15=5F=EB=B3=B5=EC=8A=B5=EA=B3=BC?=
 =?UTF-8?q?=EC=A0=9C=5F=EC=95=88=EC=A7=80=ED=98=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...354\225\210\354\247\200\355\230\204.ipynb" | 6628 +++++++++++++++++
 1 file changed, 6628 insertions(+)
 create mode 100644 "Week15_\353\263\265\354\212\265\352\263\274\354\240\234_\354\225\210\354\247\200\355\230\204.ipynb"

diff --git "a/Week15_\353\263\265\354\212\265\352\263\274\354\240\234_\354\225\210\354\247\200\355\230\204.ipynb" "b/Week15_\353\263\265\354\212\265\352\263\274\354\240\234_\354\225\210\354\247\200\355\230\204.ipynb"
new file mode 100644
index 0000000..83468a2
--- /dev/null
+++ "b/Week15_\353\263\265\354\212\265\352\263\274\354\240\234_\354\225\210\354\247\200\355\230\204.ipynb"
@@ -0,0 +1,6628 @@
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+    }
+  },
+  "cells": [
+    {
+      "id": "4644be45",
+      "cell_type": "markdown",
+      "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!"
+      ],
+      "metadata": {
+        "id": "4644be45"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install --upgrade --quiet datasets fsspec huggingface_hub"
+      ],
+      "metadata": {
+        "id": "2eSXxqg4a9SF"
+      },
+      "id": "2eSXxqg4a9SF",
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -U transformers huggingface_hub==0.34.4"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4oZoUkkFSWFY",
+        "outputId": "0e9722d5-0507-4567-b083-6293252a6a1b"
+      },
+      "id": "4oZoUkkFSWFY",
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (4.57.3)\n",
+            "Collecting huggingface_hub==0.34.4\n",
+            "  Downloading huggingface_hub-0.34.4-py3-none-any.whl.metadata (14 kB)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface_hub==0.34.4) (3.20.0)\n",
+            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub==0.34.4) (2025.10.0)\n",
+            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub==0.34.4) (25.0)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub==0.34.4) (6.0.3)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from huggingface_hub==0.34.4) (2.32.4)\n",
+            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub==0.34.4) (4.67.1)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub==0.34.4) (4.15.0)\n",
+            "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub==0.34.4) (1.2.0)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2.0.2)\n",
+            "Requirement already satisfied: regex!=2019.12.17 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) (0.22.1)\n",
+            "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.7.0)\n",
+            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub==0.34.4) (3.4.4)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub==0.34.4) (3.11)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub==0.34.4) (2.5.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub==0.34.4) (2025.11.12)\n",
+            "Downloading huggingface_hub-0.34.4-py3-none-any.whl (561 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m561.5/561.5 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: huggingface_hub\n",
+            "  Attempting uninstall: huggingface_hub\n",
+            "    Found existing installation: huggingface_hub 1.2.3\n",
+            "    Uninstalling huggingface_hub-1.2.3:\n",
+            "      Successfully uninstalled huggingface_hub-1.2.3\n",
+            "Successfully installed huggingface_hub-0.34.4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "---------------\n",
+        "여기까지만 실행\n",
+        "---------------\n",
+        "그 다음,  런타임 > 세션 다시 시작 > 아래 셀부터 실행"
+      ],
+      "metadata": {
+        "id": "4rq77NfBbByn"
+      },
+      "id": "4rq77NfBbByn"
+    },
+    {
+      "id": "ac52f320",
+      "cell_type": "code",
+      "metadata": {
+        "id": "ac52f320"
+      },
+      "execution_count": 1,
+      "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"
+      ],
+      "outputs": []
+    },
+    {
+      "id": "393f4136",
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "393f4136",
+        "outputId": "415d09f8-0ad3-43da-a306-1a22bdaad992"
+      },
+      "execution_count": 5,
+      "source": [
+        "# batch_size와 epochs를 조정해보세요!\n",
+        "batch_size = 16\n",
+        "epochs = 1\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(f\"Using device: {device}\")"
+      ],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using device: cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 데이터셋 로드\n",
+        "raw_datasets = load_dataset(\"sst2\")\n",
+        "raw_datasets"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QTVKkGiIflzk",
+        "outputId": "ed926525-7036-411e-9d68-cfa469cb7227"
+      },
+      "id": "QTVKkGiIflzk",
+      "execution_count": 6,
+      "outputs": [
+        {
+          "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": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 전처리\n",
+        "def tokenize_function(examples, tokenizer):\n",
+        "    return tokenizer(examples[\"sentence\"], padding=\"max_length\", truncation=True, max_length=128)"
+      ],
+      "metadata": {
+        "id": "Y_zJtt34ftoZ"
+      },
+      "id": "Y_zJtt34ftoZ",
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "id": "4e3b88e4",
+      "cell_type": "markdown",
+      "source": [
+        "## 🔹 BERT와 ELECTRA 실험"
+      ],
+      "metadata": {
+        "id": "4e3b88e4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "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",
+        "    train_dataset = train_dataset.select(range(10000))\n",
+        "\n",
+        "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
+        "    valid_loader = DataLoader(valid_dataset, batch_size=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}\"):\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\")"
+      ],
+      "metadata": {
+        "colab": {
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+        "id": "_ehi3D9Mf9ov",
+        "outputId": "fe79a9e4-f78e-4d67-e3b2-16d66db2af7e"
+      },
+      "id": "_ehi3D9Mf9ov",
+      "execution_count": 7,
+      "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]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8a35ff1731c34b228904eb416e4549e0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "94a6dd223c1748a9875b0e7a071f89fd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0ca94287103f43b7a3af3c09a1454955"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2f11cdfee91a48f8971238acd76648f3"
+            }
+          },
+          "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,
+              "version_minor": 0,
+              "model_id": "2c1a94079f5946ecb815286c52aafd44"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/872 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "472bf5c7e5594251aca81b8a53fb137b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "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": "a8c9e8092cc1498888f83cc64a8d63e4"
+            }
+          },
+          "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": "e7d6b05f81884283af1f48d837c4b8b7"
+            }
+          },
+          "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%|██████████| 625/625 [03:23<00:00,  3.07it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1 - Avg Train Loss: 0.3078\n",
+            "Validation Accuracy (bert-base-uncased): 0.8624\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": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3250679953ec4e8385d8d0b82b858f67"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/666 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "abd666259b0e445381c58547ec0f1932"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.txt: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cae5602efc124c5c9fe1cb20bf04b6d0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fc553cc1db6c4f1f9c1c011b68eb1140"
+            }
+          },
+          "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,
+              "version_minor": 0,
+              "model_id": "2cf9717fa4f94805a645fb03d6030984"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/872 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cc7b4cd78bac4f7f9bfb0bbc8edb6b9c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "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": "56cbffa1e86d46a684feea7bc6adc53e"
+            }
+          },
+          "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,
+              "version_minor": 0,
+              "model_id": "cf2ae53b9b8a4a2097397970af511f7e"
+            }
+          },
+          "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",
+            "Epoch 1:   0%|          | 2/625 [00:00<03:00,  3.46it/s]"
+          ]
+        },
+        {
+          "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": "965d3f849cd9426591ad69203f893acd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 1: 100%|██████████| 625/625 [03:34<00:00,  2.91it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1 - Avg Train Loss: 0.2758\n",
+            "Validation Accuracy (google/electra-base-discriminator): 0.9243\n"
+          ]
+        }
+      ]
+    },
+    {
+      "id": "1616c7fb",
+      "cell_type": "markdown",
+      "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"
+      ],
+      "metadata": {
+        "id": "1616c7fb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 모델 구조\n",
+        "1. BERT\n",
+        "- Masked Language Modeling 기반의 양방향 Transformer\n",
+        "- 입력 문장의 일부 토큰을 가리고 복원하며 사전학습\n",
+        "2. ELECTRA\n",
+        "- Generator와 Discriminator 구조 사용\n",
+        "- Generator가 만든 토큰이 진짜인지 판별하는 식으로 학습\n",
+        "- 모든 토큰에 학습 활용\n",
+        "\n",
+        "### 더 적합한 모델\n",
+        "- 학습시간은 약간 더 오래 걸렸지만, 적은 epoch에서도 높은 성능을 보이며 구조가 효율적인 ELECTRA가 더 적합한것으로 보인다.\n",
+        "- ELECTRA가 BERT보다 낮은 loss와 높은 accuracy를 기록함"
+      ],
+      "metadata": {
+        "id": "78IvAec4h_7H"
+      },
+      "id": "78IvAec4h_7H"
+    }
+  ]
+}
\ No newline at end of file