diff --git "a/Week13_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\354\230\210\353\202\230.ipynb" "b/Week13_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\354\230\210\353\202\230.ipynb"
new file mode 100644
index 0000000..dbafea9
--- /dev/null
+++ "b/Week13_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\354\230\210\353\202\230.ipynb"
@@ -0,0 +1,14060 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "BERT"
+      ],
+      "metadata": {
+        "id": "zkzsaAgjPO5W"
+      },
+      "id": "zkzsaAgjPO5W"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install Korpora"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 440
+        },
+        "id": "jigj2nVsPJnI",
+        "outputId": "d9729f8c-89f7-41da-c229-48f772d04bfa"
+      },
+      "id": "jigj2nVsPJnI",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting Korpora\n",
+            "  Downloading Korpora-0.2.0-py3-none-any.whl.metadata (26 kB)\n",
+            "Collecting dataclasses>=0.6 (from Korpora)\n",
+            "  Downloading dataclasses-0.6-py3-none-any.whl.metadata (3.0 kB)\n",
+            "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.12/dist-packages (from Korpora) (2.0.2)\n",
+            "Requirement already satisfied: tqdm>=4.46.0 in /usr/local/lib/python3.12/dist-packages (from Korpora) (4.67.3)\n",
+            "Requirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.12/dist-packages (from Korpora) (2.32.4)\n",
+            "Requirement already satisfied: xlrd>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from Korpora) (2.0.2)\n",
+            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.20.0->Korpora) (3.4.7)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.20.0->Korpora) (3.15)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.20.0->Korpora) (2.5.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests>=2.20.0->Korpora) (2026.5.20)\n",
+            "Downloading Korpora-0.2.0-py3-none-any.whl (57 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.8/57.8 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading dataclasses-0.6-py3-none-any.whl (14 kB)\n",
+            "Installing collected packages: dataclasses, Korpora\n",
+            "Successfully installed Korpora-0.2.0 dataclasses-0.6\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "pip_warning": {
+                "packages": [
+                  "dataclasses"
+                ]
+              },
+              "id": "67768d920ffb4454bdf0e98c15426240"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f0a583dd-df09-42a4-bed9-cbe3270fd368",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f0a583dd-df09-42a4-bed9-cbe3270fd368",
+        "outputId": "f162b6d5-068e-489e-bf3c-c843149706de"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "    Korpora 는 다른 분들이 연구 목적으로 공유해주신 말뭉치들을\n",
+            "    손쉽게 다운로드, 사용할 수 있는 기능만을 제공합니다.\n",
+            "\n",
+            "    말뭉치들을 공유해 주신 분들에게 감사드리며, 각 말뭉치 별 설명과 라이센스를 공유 드립니다.\n",
+            "    해당 말뭉치에 대해 자세히 알고 싶으신 분은 아래의 description 을 참고,\n",
+            "    해당 말뭉치를 연구/상용의 목적으로 이용하실 때에는 아래의 라이센스를 참고해 주시기 바랍니다.\n",
+            "\n",
+            "    # Description\n",
+            "    Author : e9t@github\n",
+            "    Repository : https://github.com/e9t/nsmc\n",
+            "    References : www.lucypark.kr/docs/2015-pyconkr/#39\n",
+            "\n",
+            "    Naver sentiment movie corpus v1.0\n",
+            "    This is a movie review dataset in the Korean language.\n",
+            "    Reviews were scraped from Naver Movies.\n",
+            "\n",
+            "    The dataset construction is based on the method noted in\n",
+            "    [Large movie review dataset][^1] from Maas et al., 2011.\n",
+            "\n",
+            "    [^1]: http://ai.stanford.edu/~amaas/data/sentiment/\n",
+            "\n",
+            "    # License\n",
+            "    CC0 1.0 Universal (CC0 1.0) Public Domain Dedication\n",
+            "    Details in https://creativecommons.org/publicdomain/zero/1.0/\n",
+            "\n",
+            "[Korpora] Corpus `nsmc` is already installed at /root/Korpora/nsmc/ratings_train.txt\n",
+            "[Korpora] Corpus `nsmc` is already installed at /root/Korpora/nsmc/ratings_test.txt\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from Korpora import Korpora\n",
+        "\n",
+        "\n",
+        "corpus = Korpora.load(\"nsmc\")\n",
+        "df = pd.DataFrame(corpus.test).sample(20000, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "aab87a20",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aab87a20",
+        "outputId": "d2b40d81-7e1f-4f7c-ec80-440a0baa9d73"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "|       | text                                                     |   label |\n",
+            "|------:|:---------------------------------------------------------|--------:|\n",
+            "| 26891 | 역시 코믹액션은 성룡, 홍금보, 원표 삼인방이 최고지!!     |       1 |\n",
+            "| 25024 | 점수 후하게 줘야것네 별 반개~                            |       0 |\n",
+            "| 11666 | 오랜만에 느낄수 있는 [감독] 구타욕구.                    |       0 |\n",
+            "| 40303 | 본지는 좀 됬지만 극장서 돈주고 본게 아직까지 아까운 영화 |       0 |\n",
+            "| 18010 | 징키스칸이란 소재를 가지고 이것밖에 못만드냐             |       0 |\n",
+            "Training Data Size : 12000\n",
+            "Validation Data Size : 4000\n",
+            "Testing Data Size : 4000\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": [
+        "train, valid, test = np.split(\n",
+        "    df.sample(frac=1, random_state=42), [int(0.6 * len(df)), int(0.8 * len(df))]\n",
+        ")\n",
+        "\n",
+        "print(train.head(5).to_markdown())\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": "code",
+      "execution_count": null,
+      "id": "61a66d8a-bb12-4b74-9ea7-529192ee562e",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "61a66d8a-bb12-4b74-9ea7-529192ee562e",
+        "outputId": "ba2230f4-77d1-4ed3-eae6-f31165845005"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(tensor([   101,  58466,   9812, 118956, 119122,  59095,  10892,   9434, 118888,\n",
+            "           117,   9992,  40032,  30005,    117,   9612,  37824,   9410,  12030,\n",
+            "         42337,  10739,  83491,  12508,    106,    106,    102,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
+            "             0,      0,      0,      0,      0,      0,      0],\n",
+            "       device='cuda:0'), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+            "        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+            "        0, 0, 0, 0], device='cuda:0'), tensor(1, device='cuda:0'))\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from transformers import BertTokenizer\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.utils.data import RandomSampler, SequentialSampler\n",
+        "\n",
+        "\n",
+        "def make_dataset(data, tokenizer, device):\n",
+        "    tokenized = tokenizer(\n",
+        "        text=data.text.tolist(),\n",
+        "        padding=\"longest\",\n",
+        "        truncation=True,\n",
+        "        return_tensors=\"pt\"\n",
+        "    )\n",
+        "    input_ids = tokenized[\"input_ids\"].to(device)\n",
+        "    attention_mask = tokenized[\"attention_mask\"].to(device)\n",
+        "    labels = torch.tensor(data.label.values, dtype=torch.long).to(device)\n",
+        "    return TensorDataset(input_ids, attention_mask, labels)\n",
+        "\n",
+        "\n",
+        "def get_datalodader(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",
+        "\n",
+        "epochs = 5\n",
+        "batch_size = 32\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "tokenizer = BertTokenizer.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"bert-base-multilingual-cased\",\n",
+        "    do_lower_case=False\n",
+        ")\n",
+        "\n",
+        "train_dataset = make_dataset(train, tokenizer, device)\n",
+        "train_dataloader = get_datalodader(train_dataset, RandomSampler, batch_size)\n",
+        "\n",
+        "valid_dataset = make_dataset(valid, tokenizer, device)\n",
+        "valid_dataloader = get_datalodader(valid_dataset, SequentialSampler, batch_size)\n",
+        "\n",
+        "test_dataset = make_dataset(test, tokenizer, device)\n",
+        "test_dataloader = get_datalodader(test_dataset, SequentialSampler, batch_size)\n",
+        "\n",
+        "print(train_dataset[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b04f95af-2ed6-4ee4-a293-218280946df2",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355,
+          "referenced_widgets": [
+            "bb5b2007245a4ec0956acf8c8b4add2c",
+            "3f371eac218a4cecaf4f0e75c0f2e82e",
+            "2a6fd3ce99994f1b9041341c77fb41ba",
+            "df1192b72fb546ef9929d8d24a370fb8",
+            "ef7a20f97c87409f81ee765f797f65e1",
+            "95ca79c96f0349389a17b0977a9f5bdd",
+            "f9bf19ea697a4606b08a08625ce76c7d",
+            "ee30f4a55cfc4bc9ac4c97667c01f781",
+            "92ee632a4be14e6f9dc37a78fd7eaf9f",
+            "282d90ba69ca4703bf585425a6c20676",
+            "c9d7a2dd08b1443a818025fac2c59d3a"
+          ]
+        },
+        "id": "b04f95af-2ed6-4ee4-a293-218280946df2",
+        "outputId": "9f4e33fa-6eb7-4d87-c429-f5d3f88ab7da"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "bb5b2007245a4ec0956acf8c8b4add2c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "BertForSequenceClassification LOAD REPORT from: bert-base-multilingual-cased\n",
+            "Key                                        | Status     | \n",
+            "-------------------------------------------+------------+-\n",
+            "cls.predictions.transform.dense.weight     | UNEXPECTED | \n",
+            "cls.predictions.transform.LayerNorm.weight | UNEXPECTED | \n",
+            "cls.seq_relationship.weight                | UNEXPECTED | \n",
+            "cls.predictions.bias                       | UNEXPECTED | \n",
+            "cls.seq_relationship.bias                  | UNEXPECTED | \n",
+            "cls.predictions.transform.LayerNorm.bias   | UNEXPECTED | \n",
+            "cls.predictions.transform.dense.bias       | UNEXPECTED | \n",
+            "classifier.bias                            | MISSING    | \n",
+            "classifier.weight                          | MISSING    | \n",
+            "\n",
+            "Notes:\n",
+            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
+            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
+          ]
+        }
+      ],
+      "source": [
+        "from torch import optim\n",
+        "from transformers import BertForSequenceClassification\n",
+        "\n",
+        "\n",
+        "model = BertForSequenceClassification.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"bert-base-multilingual-cased\",\n",
+        "    num_labels=2\n",
+        ").to(device)\n",
+        "optimizer = optim.AdamW(model.parameters(), lr=1e-5, eps=1e-8)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "df6177ae-ac4c-44bd-bbe6-6dd43d8570c4",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "df6177ae-ac4c-44bd-bbe6-6dd43d8570c4",
+        "outputId": "f4326bd0-bf07-416d-ef1c-fe1258321ee6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "bert\n",
+            "└ embeddings\n",
+            "│  └ word_embeddings\n",
+            "│  └ position_embeddings\n",
+            "│  └ token_type_embeddings\n",
+            "│  └ LayerNorm\n",
+            "│  └ dropout\n",
+            "└ encoder\n",
+            "│  └ layer\n",
+            "│  │  └ 0\n",
+            "│  │  └ 1\n",
+            "│  │  └ 2\n",
+            "│  │  └ 3\n",
+            "│  │  └ 4\n",
+            "│  │  └ 5\n",
+            "│  │  └ 6\n",
+            "│  │  └ 7\n",
+            "│  │  └ 8\n",
+            "│  │  └ 9\n",
+            "│  │  └ 10\n",
+            "│  │  └ 11\n",
+            "└ pooler\n",
+            "│  └ dense\n",
+            "│  └ activation\n",
+            "dropout\n",
+            "classifier\n"
+          ]
+        }
+      ],
+      "source": [
+        "for main_name, main_module in model.named_children():\n",
+        "    print(main_name)\n",
+        "    for sub_name, sub_module in main_module.named_children():\n",
+        "        print(\"└\", sub_name)\n",
+        "        for ssub_name, ssub_module in sub_module.named_children():\n",
+        "            print(\"│  └\", ssub_name)\n",
+        "            for sssub_name, sssub_module in ssub_module.named_children():\n",
+        "                print(\"│  │  └\", sssub_name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "602d1390-bd62-4436-afc0-c5c7568ce3fd",
+      "metadata": {
+        "id": "602d1390-bd62-4436-afc0-c5c7568ce3fd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b2958952-5d00-43d7-bcf6-f14b5efcc844"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1: Train Loss: 0.4134 Val Loss: 0.4163 Val Accuracy 0.8120\n",
+            "Saved the model weights\n",
+            "Epoch 2: Train Loss: 0.3366 Val Loss: 0.4163 Val Accuracy 0.8183\n",
+            "Saved the model weights\n",
+            "Epoch 3: Train Loss: 0.2670 Val Loss: 0.4470 Val Accuracy 0.8150\n",
+            "Epoch 4: Train Loss: 0.2028 Val Loss: 0.5372 Val Accuracy 0.8197\n",
+            "Epoch 5: Train Loss: 0.1516 Val Loss: 0.5934 Val Accuracy 0.8157\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from torch import nn\n",
+        "import os # Import the os module\n",
+        "\n",
+        "def calc_accuracy(preds, labels):\n",
+        "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
+        "    labels_flat = labels.flatten()\n",
+        "    return np.sum(pred_flat == labels_flat) / len(labels_flat)\n",
+        "\n",
+        "def train(model, optimizer, dataloader):\n",
+        "    model.train()\n",
+        "    train_loss = 0.0\n",
+        "\n",
+        "    for input_ids, attention_mask, labels in dataloader:\n",
+        "        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
+        "\n",
+        "        loss = outputs.loss\n",
+        "        train_loss += loss.item()\n",
+        "\n",
+        "        optimizer.zero_grad()\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "    train_loss = train_loss / len(dataloader)\n",
+        "    return train_loss\n",
+        "\n",
+        "def evaluation(model, dataloader):\n",
+        "    with torch.no_grad():\n",
+        "        model.eval()\n",
+        "        criterion = nn.CrossEntropyLoss()\n",
+        "        val_loss, val_accuracy = 0.0, 0.0\n",
+        "\n",
+        "        for input_ids, attention_mask, labels in dataloader:\n",
+        "            outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
+        "            logits = outputs.logits\n",
+        "\n",
+        "            loss = criterion(logits, labels)\n",
+        "            logits = logits.detach().cpu().numpy()\n",
+        "            label_ids = labels.to(\"cpu\").numpy()\n",
+        "            accuracy = calc_accuracy(logits, label_ids)\n",
+        "\n",
+        "            val_loss += loss.item()\n",
+        "            val_accuracy += accuracy\n",
+        "\n",
+        "    val_loss = val_loss/len(dataloader)\n",
+        "    val_accuracy = val_accuracy/len(dataloader)\n",
+        "    return val_loss, val_accuracy\n",
+        "\n",
+        "\n",
+        "best_loss = 10000\n",
+        "for epoch in range(epochs):\n",
+        "    train_loss = train(model, optimizer, train_dataloader)\n",
+        "    val_loss, val_accuracy = evaluation(model, valid_dataloader)\n",
+        "    print(f\"Epoch {epoch + 1}: Train Loss: {train_loss:.4f} Val Loss: {val_loss:.4f} Val Accuracy {val_accuracy:.4f}\")\n",
+        "\n",
+        "    if val_loss < best_loss:\n",
+        "        best_loss = val_loss\n",
+        "        # Create the directory if it doesn't exist\n",
+        "        os.makedirs(\"../models\", exist_ok=True)\n",
+        "        torch.save(model.state_dict(), \"../models/BertForSequenceClassification.pt\")\n",
+        "        print(\"Saved the model weights\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3c20f415-90c1-49db-981b-f1625ea6e699",
+      "metadata": {
+        "id": "3c20f415-90c1-49db-981b-f1625ea6e699",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 390,
+          "referenced_widgets": [
+            "1de4ff7d9af446d9839b180510c98d5e",
+            "71d74ffb690d4c9ba3852df87124d1bc",
+            "2f2e5e41ec8a40a69f669ce5e1ceb184",
+            "ced05a499b91440690552488b348d4c8",
+            "a7aae07d595c47a9b486f10e91c746c3",
+            "1c218b3e90ff4acfb883a54fbd831af2",
+            "dd69614c04dd472e957b109d0e15d893",
+            "e9dedf3998de41f38245803c7bce6728",
+            "d102466c7a344f8c9cbf4db5392fb33e",
+            "f0517cef67cd470f8984a12678299120",
+            "d9ea40f85a8c4c37b72310e042349494"
+          ]
+        },
+        "outputId": "b3fced61-d2fd-4bde-fc3e-ca3c6173ddb6"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1de4ff7d9af446d9839b180510c98d5e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "BertForSequenceClassification LOAD REPORT from: bert-base-multilingual-cased\n",
+            "Key                                        | Status     | \n",
+            "-------------------------------------------+------------+-\n",
+            "cls.predictions.transform.dense.weight     | UNEXPECTED | \n",
+            "cls.predictions.transform.LayerNorm.weight | UNEXPECTED | \n",
+            "cls.seq_relationship.weight                | UNEXPECTED | \n",
+            "cls.predictions.bias                       | UNEXPECTED | \n",
+            "cls.seq_relationship.bias                  | UNEXPECTED | \n",
+            "cls.predictions.transform.LayerNorm.bias   | UNEXPECTED | \n",
+            "cls.predictions.transform.dense.bias       | UNEXPECTED | \n",
+            "classifier.bias                            | MISSING    | \n",
+            "classifier.weight                          | MISSING    | \n",
+            "\n",
+            "Notes:\n",
+            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
+            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss : 0.4221\n",
+            "Test Accuracy : 0.8110\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = BertForSequenceClassification.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"bert-base-multilingual-cased\",\n",
+        "    num_labels=2\n",
+        ").to(device)\n",
+        "model.load_state_dict(torch.load(\"../models/BertForSequenceClassification.pt\"))\n",
+        "\n",
+        "test_loss, test_accuracy = evaluation(model, test_dataloader)\n",
+        "print(f\"Test Loss : {test_loss:.4f}\")\n",
+        "print(f\"Test Accuracy : {test_accuracy:.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "BART"
+      ],
+      "metadata": {
+        "id": "_3gZN7X_PY5-"
+      },
+      "id": "_3gZN7X_PY5-"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install evaluate rouge_score absl-py"
+      ],
+      "metadata": {
+        "id": "nlal3xADPcV0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9651c6ac-0ba5-4aa7-837e-c9f7d5340361"
+      },
+      "id": "nlal3xADPcV0",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "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: datasets>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from evaluate) (4.0.0)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from evaluate) (2.0.2)\n",
+            "Requirement already satisfied: dill in /usr/local/lib/python3.12/dist-packages (from evaluate) (0.3.8)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (from evaluate) (2.2.2)\n",
+            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.12/dist-packages (from evaluate) (2.32.4)\n",
+            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.12/dist-packages (from evaluate) (4.67.3)\n",
+            "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from evaluate) (3.7.0)\n",
+            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.12/dist-packages (from evaluate) (0.70.16)\n",
+            "Requirement already satisfied: fsspec>=2021.05.0 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]>=2021.05.0->evaluate) (2025.3.0)\n",
+            "Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from evaluate) (1.16.1)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from evaluate) (26.2)\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: filelock in /usr/local/lib/python3.12/dist-packages (from datasets>=2.0.0->evaluate) (3.29.0)\n",
+            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.12/dist-packages (from datasets>=2.0.0->evaluate) (18.1.0)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from datasets>=2.0.0->evaluate) (6.0.3)\n",
+            "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]>=2021.05.0->evaluate) (3.13.5)\n",
+            "Requirement already satisfied: hf-xet<2.0.0,>=1.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.7.0->evaluate) (1.5.0)\n",
+            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.7.0->evaluate) (0.28.1)\n",
+            "Requirement already satisfied: typer>=0.20.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.7.0->evaluate) (0.25.1)\n",
+            "Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.7.0->evaluate) (4.15.0)\n",
+            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.19.0->evaluate) (3.4.7)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.19.0->evaluate) (3.15)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.19.0->evaluate) (2.5.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests>=2.19.0->evaluate) (2026.5.20)\n",
+            "Requirement already satisfied: click in /usr/local/lib/python3.12/dist-packages (from nltk->rouge_score) (8.4.0)\n",
+            "Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk->rouge_score) (1.5.3)\n",
+            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.12/dist-packages (from nltk->rouge_score) (2025.11.3)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas->evaluate) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas->evaluate) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas->evaluate) (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]>=2021.05.0->evaluate) (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]>=2021.05.0->evaluate) (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]>=2021.05.0->evaluate) (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]>=2021.05.0->evaluate) (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]>=2021.05.0->evaluate) (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]>=2021.05.0->evaluate) (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]>=2021.05.0->evaluate) (1.24.2)\n",
+            "Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.7.0->evaluate) (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>=0.7.0->evaluate) (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>=0.7.0->evaluate) (0.16.0)\n",
+            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer>=0.20.0->huggingface-hub>=0.7.0->evaluate) (1.5.4)\n",
+            "Requirement already satisfied: rich>=13.8.0 in /usr/local/lib/python3.12/dist-packages (from typer>=0.20.0->huggingface-hub>=0.7.0->evaluate) (13.9.4)\n",
+            "Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer>=0.20.0->huggingface-hub>=0.7.0->evaluate) (0.0.4)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=13.8.0->typer>=0.20.0->huggingface-hub>=0.7.0->evaluate) (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>=0.20.0->huggingface-hub>=0.7.0->evaluate) (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>=0.20.0->huggingface-hub>=0.7.0->evaluate) (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[31m3.8 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=26991bf05bd6360cc34a1613b894f9d63d142ffe04e36a886a12ed1e054b4909\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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "\n",
+        "news = load_dataset(\"argilla/news-summary\", split=\"test\")\n",
+        "df = news.to_pandas().sample(5000, random_state=42)[[\"text\", \"prediction\"]]\n",
+        "df[\"prediction\"] = df[\"prediction\"].map(lambda x: x[0][\"text\"])\n",
+        "train, valid, test = np.split(\n",
+        "    df.sample(frac=1, random_state=42), [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)}\")"
+      ],
+      "metadata": {
+        "id": "GeJyAfazPd4u",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 465,
+          "referenced_widgets": [
+            "a379c25a8888457eb793210fce6749c1",
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+            "3aa99498eb5848a983f36875d77cbc74",
+            "98cae9d8f69b43b0bb214c45edf8a41b",
+            "5f2973ec80fa44eab05f7fe7aa30efd9",
+            "c32c27bdd40e40a5a5acdc4585fcfd43",
+            "25a74aa1a3984bbd93b65efdbf05a22b",
+            "61d7e39b2eee431786d735b76505fc51",
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+            "a67ff25639dd46159c3867c5096f76e5",
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+            "f5d70eaf05944fe0bcf56dc43ac310d0",
+            "0c14fdba719f47ca8421430536b5cbf9",
+            "0156632be3d8468fbe35522fe06bf2ab",
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+            "8f62f7c7fe144b3fb8a9300acda7bedb",
+            "6924da2b9ce94ed9b20f61be5d4b491a",
+            "5ff6e9f5dae945058a9b666023befe9b",
+            "d369af61630746a2998c4345978617da",
+            "4b5751ab3b6345d797a297374b5b76be",
+            "4759096f00d24fe7bbdb53d5080b94ea",
+            "fd0d85a2292840949b83f8408b6f4330",
+            "4a0d94db2bf6400791a87c4056a79547",
+            "9dfd49d0e03c45c79b4f386ac692be42",
+            "31fd9c7561d348c4a4bf5c470a9b3fbb",
+            "087a51e6f5be4ebdbe5b513724baf9a3",
+            "2037d6a4228444069422a38ad1820ba2",
+            "4b509266c7134ea08b63246897d56e4e",
+            "11a38fec630a4c068e2b1945e898ed8b",
+            "8eae634301b04949b7785f2ec5589e0a",
+            "eedddd83a6e6421fbb5694b320c0b8a7",
+            "8913279a84ac4a0bbdc349f47ec66891"
+          ]
+        },
+        "outputId": "b2e9520b-f9ff-4099-fa84-adebc024a46f"
+      },
+      "id": "GeJyAfazPd4u",
+      "execution_count": null,
+      "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": "a379c25a8888457eb793210fce6749c1"
+            }
+          },
+          "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/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": "7f9f719952764f89a7024e743b31a607"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "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": "c38b48fec3fc49ee9043ce5176534867"
+            }
+          },
+          "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": "f5d70eaf05944fe0bcf56dc43ac310d0"
+            }
+          },
+          "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": "fd0d85a2292840949b83f8408b6f4330"
+            }
+          },
+          "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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "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",
+        "\n",
+        "def make_dataset(data, tokenizer, device):\n",
+        "    tokenized = tokenizer(\n",
+        "        text=data.text.tolist(),\n",
+        "        padding=\"longest\",\n",
+        "        truncation=True,\n",
+        "        return_tensors=\"pt\",\n",
+        "        max_length=1024\n",
+        "    )\n",
+        "    labels = []\n",
+        "    input_ids = tokenized[\"input_ids\"].to(device)\n",
+        "    attention_mask = tokenized[\"attention_mask\"].to(device)\n",
+        "    for target in data.prediction:\n",
+        "        labels.append(tokenizer.encode(target, return_tensors=\"pt\").squeeze())\n",
+        "    labels = pad_sequence(labels, batch_first=True, padding_value=-100).to(device)\n",
+        "    return TensorDataset(input_ids, attention_mask, labels)\n",
+        "\n",
+        "\n",
+        "\n",
+        "def get_datalodader(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",
+        "\n",
+        "epochs = 5\n",
+        "batch_size = 8\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "tokenizer = BartTokenizer.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"facebook/bart-base\"\n",
+        ")\n",
+        "\n",
+        "train_dataset = make_dataset(train, tokenizer, device)\n",
+        "train_dataloader = get_datalodader(train_dataset, RandomSampler, batch_size)\n",
+        "\n",
+        "valid_dataset = make_dataset(valid, tokenizer, device)\n",
+        "valid_dataloader = get_datalodader(valid_dataset, SequentialSampler, batch_size)\n",
+        "\n",
+        "test_dataset = make_dataset(test, tokenizer, device)\n",
+        "test_dataloader = get_datalodader(test_dataset, SequentialSampler, batch_size)\n",
+        "\n",
+        "print(train_dataset[0])"
+      ],
+      "metadata": {
+        "id": "fTevWJjMPfUL",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204,
+          "referenced_widgets": [
+            "fd4bd5d7bcec4c88b89f7ad2ac2fcd35",
+            "d61cc96d530148cc827c801aa69be43f",
+            "0f1a97b698e04ca9aec2c44e35ab75e5",
+            "58b7f54409d94fe4844c34318c9a104b",
+            "2b553773db1e496f83306d2ff7ba7334",
+            "029d72674a904530b07cd1e39ff03e94",
+            "cc4b468a74e24b1bbc04bf7456db9b31",
+            "ae5b0fd1aa9a46e4abec21d7e03afb4c",
+            "17abff638e4e4aafaa7c441d39ecd05d",
+            "e504d8c67f52404693b1053be9c4d998",
+            "9b3870399c70401aa8eb4b1a281aedda",
+            "39727b45a7594d7db9ccbff852564e07",
+            "d1ce187d8d684c46a2deb7529be39114",
+            "934657ee00af4b44882850942e7fd130",
+            "9fc279c5b2814af49635e1c498011357",
+            "1c0c66f0b2fc498996291db5689f25c2",
+            "1001426478854016a6382c714e14400c",
+            "d97458b4351a42e2bcadaf66323227dd",
+            "7375817337ca43fdb4c92656e7ae3514",
+            "c0cd75d453094f239eaa8853e590dedc",
+            "32ea64aca1f74c7aa8310cf699614b12",
+            "577c44b896084833abb7f4ce16bbf45e",
+            "49a33423d10744debffea2cd98189956",
+            "74f9b9fe7a6a4a30b47b8c240473acfa",
+            "e382ed88585d4cca849808a7d4396202",
+            "3b5de2985d6e42ab896dea6362d1f932",
+            "685e18d8968b4a0584efbf59b3bf4e3f",
+            "342b34d2ccb34f49b7827f767ac2cac1",
+            "c37b5fb89f4047a382574bacab7477c7",
+            "30f01360f35c4396be935bec3ed5e70f",
+            "960283a00d7f44b58e381493b05fd9cb",
+            "d456670a58744011a074aa05e507260b",
+            "1a849871a08f47b49f1c69c335c6a49a"
+          ]
+        },
+        "outputId": "7c07fe03-a546-4116-de57-6f68f7640689"
+      },
+      "id": "fTevWJjMPfUL",
+      "execution_count": null,
+      "outputs": [
+        {
+          "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": "fd4bd5d7bcec4c88b89f7ad2ac2fcd35"
+            }
+          },
+          "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": "39727b45a7594d7db9ccbff852564e07"
+            }
+          },
+          "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": "49a33423d10744debffea2cd98189956"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(tensor([   0,  495, 1889,  ...,    1,    1,    1], device='cuda:0'), tensor([1, 1, 1,  ..., 0, 0, 0], device='cuda:0'), tensor([    0, 35891,   161,    56,  5616, 10405,    19,   140,    23,  5490,\n",
+            "         3564,     2,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
+            "         -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100],\n",
+            "       device='cuda:0'))\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from torch import optim\n",
+        "from transformers import BartForConditionalGeneration\n",
+        "\n",
+        "\n",
+        "model = BartForConditionalGeneration.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"facebook/bart-base\"\n",
+        ").to(device)\n",
+        "optimizer = optim.AdamW(model.parameters(), lr=5e-5, eps=1e-8)"
+      ],
+      "metadata": {
+        "id": "c5_ltphDPhRJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
+            "b7e273975ab742db932e56d722fb6139",
+            "d55d21f1f50b4720b2afba822bb438c3",
+            "b743a04e5d8a48ca8b718449caba1b7c",
+            "0ef1b3fa123e49eaac549745bef890a8",
+            "91978784e7cf437abfb2d569dcae4f66",
+            "a1c084be09ee4a09a5921802ec3ed187",
+            "de8a7645dae04d4e8eff32ca434ba1bb",
+            "8152f5fe7ba549dd9c58ec8f115a4143",
+            "bb4c4252368c4129b738a2cd0a8f641b",
+            "7e64b1ed4e85413aba84230c9c75fbde",
+            "1931a8e514c44bc7814b0ba428a2919c",
+            "9976b0a537824e1393c558a44a776428",
+            "61943221ce9e48958c8ea5eee472aed0",
+            "885fba72258f4618afca8ed47ab94ea9",
+            "2762ac005c644a5f9b41e5c4304307a1",
+            "870686b5ead24ff7afae0aa7bbce1f17",
+            "cd15cf83793c45cda753362a397f1f1e",
+            "005c1c7362c844ceb3b804f8fef8cffd",
+            "4ffa0ac0e0c447e9aefe3da9b47334c8",
+            "dacbd5613d1241da904fc19d1a7bffcd",
+            "c845e7bdbb7643a4b14ab249ae6cfecb",
+            "f89ae01831d44afeb5009fa61fab4839",
+            "524c0abd35b84fb9933ede72701349f3",
+            "879dec02fe6c484083a95bae7d0921af",
+            "cc03e51b56264b9092123140eb15788a",
+            "0c7a04b8fb9e4f6988dafc896e66ec1e",
+            "2e8b2f570af9435abc093718f56bdaa5",
+            "b69a5f4682e1489cbac3e1133c00dee2",
+            "d9c50d267d4e4d55b076b8c969686822",
+            "8825fd3b544e4cf7830fdc4e318a49cf",
+            "58207507a35b47d6a1683b30ccbb667a",
+            "887214ad62f64b3b986f8fc3280502df",
+            "baa8ed861aeb4c948734fbe1573bde5b"
+          ]
+        },
+        "outputId": "322cfbb1-883f-475e-de1a-7a3893da9e50"
+      },
+      "id": "c5_ltphDPhRJ",
+      "execution_count": null,
+      "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": "b7e273975ab742db932e56d722fb6139"
+            }
+          },
+          "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": "9976b0a537824e1393c558a44a776428"
+            }
+          },
+          "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": "524c0abd35b84fb9933ede72701349f3"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for main_name, main_module in model.named_children():\n",
+        "    print(main_name)\n",
+        "    for sub_name, sub_module in main_module.named_children():\n",
+        "        print(\"└\", sub_name)\n",
+        "        for ssub_name, ssub_module in sub_module.named_children():\n",
+        "            print(\"│  └\", ssub_name)\n",
+        "            for sssub_name, sssub_module in ssub_module.named_children():\n",
+        "                print(\"│  │  └\", sssub_name)"
+      ],
+      "metadata": {
+        "id": "TTc2IbWhPiQb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "acb8276a-6f46-45d3-aa3d-70bd5725a75d"
+      },
+      "id": "TTc2IbWhPiQb",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model\n",
+            "└ shared\n",
+            "└ encoder\n",
+            "│  └ embed_tokens\n",
+            "│  └ embed_positions\n",
+            "│  └ layers\n",
+            "│  │  └ 0\n",
+            "│  │  └ 1\n",
+            "│  │  └ 2\n",
+            "│  │  └ 3\n",
+            "│  │  └ 4\n",
+            "│  │  └ 5\n",
+            "│  └ layernorm_embedding\n",
+            "└ decoder\n",
+            "│  └ embed_tokens\n",
+            "│  └ embed_positions\n",
+            "│  └ layers\n",
+            "│  │  └ 0\n",
+            "│  │  └ 1\n",
+            "│  │  └ 2\n",
+            "│  │  └ 3\n",
+            "│  │  └ 4\n",
+            "│  │  └ 5\n",
+            "│  └ layernorm_embedding\n",
+            "lm_head\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install evaluate rouge_score absl-py\n",
+        "import numpy as np\n",
+        "import evaluate\n",
+        "import os # Import os module\n",
+        "\n",
+        "def calc_rouge(preds, labels):\n",
+        "    preds = preds.argmax(axis=-1)\n",
+        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
+        "\n",
+        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
+        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
+        "\n",
+        "    rouge2 = rouge_score.compute(\n",
+        "        predictions=decoded_preds,\n",
+        "        references=decoded_labels\n",
+        "    )\n",
+        "    return rouge2[\"rouge2\"]\n",
+        "\n",
+        "def train(model, optimizer, dataloader):\n",
+        "    model.train()\n",
+        "    train_loss = 0.0\n",
+        "\n",
+        "    for input_ids, attention_mask, labels in dataloader:\n",
+        "        outputs = model(\n",
+        "            input_ids=input_ids,\n",
+        "            attention_mask=attention_mask,\n",
+        "            labels=labels\n",
+        "        )\n",
+        "\n",
+        "        loss = outputs.loss\n",
+        "        train_loss += loss.item()\n",
+        "\n",
+        "        optimizer.zero_grad()\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "    train_loss = train_loss / len(dataloader)\n",
+        "    return train_loss\n",
+        "\n",
+        "def evaluation(model, dataloader):\n",
+        "    with torch.no_grad():\n",
+        "        model.eval()\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,\n",
+        "                attention_mask=attention_mask,\n",
+        "                labels=labels\n",
+        "            )\n",
+        "            logits = outputs.logits\n",
+        "            loss = outputs.loss\n",
+        "\n",
+        "            logits = logits.detach().cpu().numpy()\n",
+        "            label_ids = labels.to(\"cpu\").numpy()\n",
+        "            rouge = calc_rouge(logits, label_ids)\n",
+        "\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",
+        "rouge_score = evaluate.load(\"rouge\", tokenizer=tokenizer)\n",
+        "best_loss = 10000\n",
+        "for epoch in range(epochs):\n",
+        "    train_loss = train(model, optimizer, train_dataloader)\n",
+        "    val_loss, val_accuracy = evaluation(model, valid_dataloader)\n",
+        "    print(f\"Epoch {epoch + 1}: Train Loss: {train_loss:.4f} Val Loss: {val_loss:.4f} Val Rouge {val_accuracy:.4f}\")\n",
+        "\n",
+        "    if val_loss < best_loss:\n",
+        "        best_loss = val_loss\n",
+        "        # Create the directory if it doesn't exist\n",
+        "        os.makedirs(\"../models\", exist_ok=True)\n",
+        "        torch.save(model.state_dict(), \"../models/BartForConditionalGeneration.pt\")\n",
+        "        print(\"Saved the model weights\")"
+      ],
+      "metadata": {
+        "id": "svzLKDJsPixC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "67c75236-1c48-4859-d2d5-ba65fdf402b6"
+      },
+      "id": "svzLKDJsPixC",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "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: datasets>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from evaluate) (4.0.0)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from evaluate) (2.0.2)\n",
+            "Requirement already satisfied: dill in /usr/local/lib/python3.12/dist-packages (from evaluate) (0.3.8)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (from evaluate) (2.2.2)\n",
+            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.12/dist-packages (from evaluate) (2.32.4)\n",
+            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.12/dist-packages (from evaluate) (4.67.3)\n",
+            "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from evaluate) (3.7.0)\n",
+            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.12/dist-packages (from evaluate) (0.70.16)\n",
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+            "Requirement already satisfied: hf-xet<2.0.0,>=1.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.7.0->evaluate) (1.5.0)\n",
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+            "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]>=2021.05.0->evaluate) (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]>=2021.05.0->evaluate) (0.5.2)\n",
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+            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer>=0.20.0->huggingface-hub>=0.7.0->evaluate) (1.5.4)\n",
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+            "Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer>=0.20.0->huggingface-hub>=0.7.0->evaluate) (0.0.4)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=13.8.0->typer>=0.20.0->huggingface-hub>=0.7.0->evaluate) (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>=0.20.0->huggingface-hub>=0.7.0->evaluate) (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>=0.20.0->huggingface-hub>=0.7.0->evaluate) (0.1.2)\n",
+            "Epoch 1: Train Loss: 1.6152 Val Loss: 1.8923 Val Rouge 0.2633\n",
+            "Saved the model weights\n",
+            "Epoch 2: Train Loss: 1.2504 Val Loss: 1.9201 Val Rouge 0.2361\n",
+            "Epoch 3: Train Loss: 0.9689 Val Loss: 2.1170 Val Rouge 0.2444\n",
+            "Epoch 4: Train Loss: 0.7942 Val Loss: 2.2227 Val Rouge 0.2433\n",
+            "Epoch 5: Train Loss: 0.5598 Val Loss: 2.3783 Val Rouge 0.2405\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "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",
+        "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}\")"
+      ],
+      "metadata": {
+        "id": "meNCXm_FPkOR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85,
+          "referenced_widgets": [
+            "c0d529a03b5e487a9f8ddd54b6ce7152",
+            "e022583a113d43cb9edb5a39f6dea0d7",
+            "628da7b360da41578e5aecb3cce85e7f",
+            "504334f312834edbbef9a49fca1dcc1d",
+            "7f8d297fe66147a7b7c73cf5a613b076",
+            "782874293a7d4f7790e64d0f0ed2c8c2",
+            "23edb7e65d924e5cbabe9670220da479",
+            "2064eee26c084991802a5b35b4a69187",
+            "ef0d1c4bd82947f2a7d66359ec972a79",
+            "8241412e7ffd4ee4b1e31186a09d848d",
+            "891f976d7f504ae28036ad1058f40974"
+          ]
+        },
+        "outputId": "c1be94db-38dc-4627-f6c5-8b42c223a3b9"
+      },
+      "id": "meNCXm_FPkOR",
+      "execution_count": null,
+      "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": "c0d529a03b5e487a9f8ddd54b6ce7152"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss : 1.8408\n",
+            "Test ROUGE-2 Score : 0.2762\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for index in range(5):\n",
+        "    news_text = test.text.iloc[index]\n",
+        "    summarization = test.prediction.iloc[index]\n",
+        "\n",
+        "    # Tokenize the input text\n",
+        "    input_ids = tokenizer(news_text, return_tensors=\"pt\").input_ids.to(device)\n",
+        "\n",
+        "    # Generate summary using the model's generate method\n",
+        "    generated_ids = model.generate(\n",
+        "        input_ids=input_ids,\n",
+        "        max_length=54, # Use the max_length from the original pipeline setup\n",
+        "        num_beams=4, # A common value for summarization\n",
+        "        early_stopping=True\n",
+        "    )\n",
+        "\n",
+        "    predicted_summarization = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\n",
+        "\n",
+        "    print(f\"정답 요약문 : {summarization}\")\n",
+        "    print(f\"모델 요약문 : {predicted_summarization}\\n\")"
+      ],
+      "metadata": {
+        "id": "0hVHlye6PmNi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b882509f-8b7f-4423-9e64-d2d959dc6681"
+      },
+      "id": "0hVHlye6PmNi",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "정답 요약문 : Clinton leads Trump by 4 points in Washington Post: ABC News poll\n",
+            "모델 요약문 : Clinton leads Trump by 4 points in Washington Post-ABC News poll\n",
+            "\n",
+            "정답 요약문 : Democrats question independence of Trump Supreme Court nominee\n",
+            "모델 요약문 : Democrats sharpen line against Supreme Court nominee\n",
+            "\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",
+            "정답 요약문 : Romanian ruling party leader investigated over 'criminal group'\n",
+            "모델 요약문 : Romanian anti-graft prosecutors probe party leader\n",
+            "\n",
+            "정답 요약문 : Billionaire environmental activist Tom Steyer endorses Clinton\n",
+            "모델 요약문 : Steyer backs Hillary Clinton for U.S. president\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "KoElectra"
+      ],
+      "metadata": {
+        "id": "tbyW6k6XPmps"
+      },
+      "id": "tbyW6k6XPmps"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Korpora 다시 로드"
+      ],
+      "metadata": {
+        "id": "e0-WxRINOS6b"
+      },
+      "id": "e0-WxRINOS6b"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from Korpora import Korpora\n",
+        "\n",
+        "\n",
+        "corpus = Korpora.load(\"nsmc\")\n",
+        "df = pd.DataFrame(corpus.test).sample(20000, random_state=42)\n",
+        "train, valid, test = np.split(\n",
+        "    df.sample(frac=1, random_state=42), [int(0.6 * len(df)), int(0.8 * len(df))]\n",
+        ")\n",
+        "\n",
+        "print(train.head(5).to_markdown())\n",
+        "print(f\"Training Data Size : {len(train)}\")\n",
+        "print(f\"Validation Data Size : {len(valid)}\")\n",
+        "print(f\"Testing Data Size : {len(test)}\")"
+      ],
+      "metadata": {
+        "id": "ntFHYb0nPrte",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6bba2d96-8ef1-4b3b-87b0-477b0f4e18df"
+      },
+      "id": "ntFHYb0nPrte",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "    Korpora 는 다른 분들이 연구 목적으로 공유해주신 말뭉치들을\n",
+            "    손쉽게 다운로드, 사용할 수 있는 기능만을 제공합니다.\n",
+            "\n",
+            "    말뭉치들을 공유해 주신 분들에게 감사드리며, 각 말뭉치 별 설명과 라이센스를 공유 드립니다.\n",
+            "    해당 말뭉치에 대해 자세히 알고 싶으신 분은 아래의 description 을 참고,\n",
+            "    해당 말뭉치를 연구/상용의 목적으로 이용하실 때에는 아래의 라이센스를 참고해 주시기 바랍니다.\n",
+            "\n",
+            "    # Description\n",
+            "    Author : e9t@github\n",
+            "    Repository : https://github.com/e9t/nsmc\n",
+            "    References : www.lucypark.kr/docs/2015-pyconkr/#39\n",
+            "\n",
+            "    Naver sentiment movie corpus v1.0\n",
+            "    This is a movie review dataset in the Korean language.\n",
+            "    Reviews were scraped from Naver Movies.\n",
+            "\n",
+            "    The dataset construction is based on the method noted in\n",
+            "    [Large movie review dataset][^1] from Maas et al., 2011.\n",
+            "\n",
+            "    [^1]: http://ai.stanford.edu/~amaas/data/sentiment/\n",
+            "\n",
+            "    # License\n",
+            "    CC0 1.0 Universal (CC0 1.0) Public Domain Dedication\n",
+            "    Details in https://creativecommons.org/publicdomain/zero/1.0/\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nsmc] download ratings_train.txt: 14.6MB [00:00, 15.4MB/s]                            \n",
+            "[nsmc] download ratings_test.txt: 4.90MB [00:00, 30.9MB/s]                            \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "|       | text                                                     |   label |\n",
+            "|------:|:---------------------------------------------------------|--------:|\n",
+            "| 26891 | 역시 코믹액션은 성룡, 홍금보, 원표 삼인방이 최고지!!     |       1 |\n",
+            "| 25024 | 점수 후하게 줘야것네 별 반개~                            |       0 |\n",
+            "| 11666 | 오랜만에 느낄수 있는 [감독] 구타욕구.                    |       0 |\n",
+            "| 40303 | 본지는 좀 됬지만 극장서 돈주고 본게 아직까지 아까운 영화 |       0 |\n",
+            "| 18010 | 징키스칸이란 소재를 가지고 이것밖에 못만드냐             |       0 |\n",
+            "Training Data Size : 12000\n",
+            "Validation Data Size : 4000\n",
+            "Testing Data Size : 4000\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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "from transformers import ElectraTokenizer\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from torch.utils.data import RandomSampler, SequentialSampler\n",
+        "\n",
+        "\n",
+        "def make_dataset(data, tokenizer, device):\n",
+        "    tokenized = tokenizer(\n",
+        "        text=data.text.tolist(),\n",
+        "        padding=\"longest\",\n",
+        "        truncation=True,\n",
+        "        return_tensors=\"pt\"\n",
+        "    )\n",
+        "    input_ids = tokenized[\"input_ids\"].to(device)\n",
+        "    attention_mask = tokenized[\"attention_mask\"].to(device)\n",
+        "    labels = torch.tensor(data.label.values, dtype=torch.long).to(device)\n",
+        "    return TensorDataset(input_ids, attention_mask, labels)\n",
+        "\n",
+        "\n",
+        "def get_datalodader(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",
+        "\n",
+        "epochs = 5\n",
+        "batch_size = 32\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "tokenizer = ElectraTokenizer.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"monologg/koelectra-base-v3-discriminator\",\n",
+        "    do_lower_case=False,\n",
+        ")\n",
+        "\n",
+        "train_dataset = make_dataset(train, tokenizer, device)\n",
+        "train_dataloader = get_datalodader(train_dataset, RandomSampler, batch_size)\n",
+        "\n",
+        "valid_dataset = make_dataset(valid, tokenizer, device)\n",
+        "valid_dataloader = get_datalodader(valid_dataset, SequentialSampler, batch_size)\n",
+        "\n",
+        "test_dataset = make_dataset(test, tokenizer, device)\n",
+        "test_dataloader = get_datalodader(test_dataset, SequentialSampler, batch_size)\n",
+        "\n",
+        "print(train_dataset[0])"
+      ],
+      "metadata": {
+        "id": "nE9x4Ro3PsuN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 565,
+          "referenced_widgets": [
+            "badb3d436e814da0a3719777dc42ac1e",
+            "33cd1e0243f34802a3e4d75cd211eb24",
+            "2ea9e3c1909b4d6d9ce957670ba6894a",
+            "c240c8e19a964872950d77949be076ef",
+            "c13e190fc1954e4c98d7b71cde65e268",
+            "bba5d888926f45cd8f1f6026ce36eb64",
+            "3e4ae540f5bf4eb4a290d88a33b9587f",
+            "b08cb81cdd5840c5a427aca293760a0c",
+            "95a3d6280f414c6a900163fd47552488",
+            "be4cd2473b97435aa84f0494b627ac15",
+            "27f07250fc144e208d97c1cf81f55ead",
+            "344c9141e5f0444983738178c754079c",
+            "4c61bcf12fdb46a3bb57ed7fb48b5070",
+            "fdb95a6472274d30a9df940de59cf7d5",
+            "940e08c885124028a60e4f717fda0fa4",
+            "ce2fe00e1aa0402e8407f9e932abac54",
+            "f659c1c439174292a88d4f9973a20b8b",
+            "a78624b388bd4a21affe0885a1f953d4",
+            "17e8514389bb4643a801a2711633dd83",
+            "576fa75759564276b928f5f4a7573bef",
+            "43606536b15d4f90a229223707ec0546",
+            "86b777c0391a4d3dbfe5af52d9d048a5"
+          ]
+        },
+        "outputId": "b4b68ed4-412f-4093-a540-cf5b7c0514e9"
+      },
+      "id": "nE9x4Ro3PsuN",
+      "execution_count": null,
+      "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": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/61.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "badb3d436e814da0a3719777dc42ac1e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.txt:   0%|          | 0.00/263k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "344c9141e5f0444983738178c754079c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(tensor([    2,  6511, 14347,  4087,  4665,  4112,  2924,  4806,    16,  3809,\n",
+            "         4309,  4275,    16,  3201,  4376,  2891,  4139,  4212,  4007,  6557,\n",
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+            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
+            "            0], device='cuda:0'), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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+            "        0], device='cuda:0'), tensor(1, device='cuda:0'))\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from torch import optim\n",
+        "from transformers import ElectraForSequenceClassification\n",
+        "\n",
+        "\n",
+        "model = ElectraForSequenceClassification.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"monologg/koelectra-base-v3-discriminator\",\n",
+        "    num_labels=2\n",
+        ").to(device)\n",
+        "optimizer = optim.AdamW(model.parameters(), lr=1e-5, eps=1e-8)"
+      ],
+      "metadata": {
+        "id": "AyOiSpHJPuKu",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 451,
+          "referenced_widgets": [
+            "837cbb058a724a319787cc77a1dfd69f",
+            "e1145045781a4c6ca24506003a8bff63",
+            "b1d65634bde64338949052858b2fdf9d",
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+            "ea31ec5bf5fa4ff69b27197b929a8e26",
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+            "4852e5dc6af9418487193a9cbfe27ca2",
+            "0cef6762346f45a48121ae678172c0e1",
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+            "ede8f259674140b6b09ee6c02aaf1642",
+            "644b208091234ce7ba4119c7e3798fee"
+          ]
+        },
+        "outputId": "43e7cb12-e261-459f-a3ce-b2e064db10c8"
+      },
+      "id": "AyOiSpHJPuKu",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/467 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "837cbb058a724a319787cc77a1dfd69f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "pytorch_model.bin:   0%|          | 0.00/452M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c941479a374b490da39949d62ecdb5f1"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/197 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "98e616ed42144e7aa13b82d5abef215d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/452M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "27e2debea4484b4da2bfc08ac4a1651f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "ElectraForSequenceClassification LOAD REPORT from: monologg/koelectra-base-v3-discriminator\n",
+            "Key                                               | Status     | \n",
+            "--------------------------------------------------+------------+-\n",
+            "discriminator_predictions.dense_prediction.bias   | UNEXPECTED | \n",
+            "electra.embeddings.position_ids                   | UNEXPECTED | \n",
+            "discriminator_predictions.dense.weight            | UNEXPECTED | \n",
+            "discriminator_predictions.dense_prediction.weight | UNEXPECTED | \n",
+            "discriminator_predictions.dense.bias              | UNEXPECTED | \n",
+            "classifier.out_proj.bias                          | MISSING    | \n",
+            "classifier.dense.bias                             | MISSING    | \n",
+            "classifier.dense.weight                           | MISSING    | \n",
+            "classifier.out_proj.weight                        | MISSING    | \n",
+            "\n",
+            "Notes:\n",
+            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
+            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for main_name, main_module in model.named_children():\n",
+        "    print(main_name)\n",
+        "    for sub_name, sub_module in main_module.named_children():\n",
+        "        print(\"└\", sub_name)\n",
+        "        for ssub_name, ssub_module in sub_module.named_children():\n",
+        "            print(\"│  └\", ssub_name)\n",
+        "            for sssub_name, sssub_module in ssub_module.named_children():\n",
+        "                print(\"│  │  └\", sssub_name)"
+      ],
+      "metadata": {
+        "id": "AgYkq-AqPukL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8bf2f9cd-3669-4578-dc06-7cb09f26762c"
+      },
+      "id": "AgYkq-AqPukL",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "electra\n",
+            "└ embeddings\n",
+            "│  └ word_embeddings\n",
+            "│  └ position_embeddings\n",
+            "│  └ token_type_embeddings\n",
+            "│  └ LayerNorm\n",
+            "│  └ dropout\n",
+            "└ encoder\n",
+            "│  └ layer\n",
+            "│  │  └ 0\n",
+            "│  │  └ 1\n",
+            "│  │  └ 2\n",
+            "│  │  └ 3\n",
+            "│  │  └ 4\n",
+            "│  │  └ 5\n",
+            "│  │  └ 6\n",
+            "│  │  └ 7\n",
+            "│  │  └ 8\n",
+            "│  │  └ 9\n",
+            "│  │  └ 10\n",
+            "│  │  └ 11\n",
+            "classifier\n",
+            "└ dense\n",
+            "└ activation\n",
+            "└ dropout\n",
+            "└ out_proj\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from torch import nn\n",
+        "import os # Import os module\n",
+        "\n",
+        "def calc_accuracy(preds, labels):\n",
+        "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
+        "    labels_flat = labels.flatten()\n",
+        "    return np.sum(pred_flat == labels_flat) / len(labels_flat)\n",
+        "\n",
+        "def train(model, optimizer, dataloader):\n",
+        "    model.train()\n",
+        "    train_loss = 0.0\n",
+        "\n",
+        "    for input_ids, attention_mask, labels in dataloader:\n",
+        "        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
+        "\n",
+        "        loss = outputs.loss\n",
+        "        train_loss += loss.item()\n",
+        "\n",
+        "        optimizer.zero_grad()\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "    train_loss = train_loss / len(dataloader)\n",
+        "    return train_loss\n",
+        "\n",
+        "def evaluation(model, dataloader):\n",
+        "    with torch.no_grad():\n",
+        "        model.eval()\n",
+        "        criterion = nn.CrossEntropyLoss()\n",
+        "        val_loss, val_accuracy = 0.0, 0.0\n",
+        "\n",
+        "        for input_ids, attention_mask, labels in dataloader:\n",
+        "            outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
+        "            logits = outputs.logits\n",
+        "\n",
+        "            loss = criterion(logits, labels)\n",
+        "            logits = logits.detach().cpu().numpy()\n",
+        "            label_ids = labels.to(\"cpu\").numpy()\n",
+        "            accuracy = calc_accuracy(logits, label_ids)\n",
+        "\n",
+        "            val_loss += loss.item()\n",
+        "            val_accuracy += accuracy\n",
+        "\n",
+        "    val_loss = val_loss/len(dataloader)\n",
+        "    val_accuracy = val_accuracy/len(dataloader)\n",
+        "    return val_loss, val_accuracy\n",
+        "\n",
+        "\n",
+        "best_loss = 10000\n",
+        "for epoch in range(epochs):\n",
+        "    train_loss = train(model, optimizer, train_dataloader)\n",
+        "    val_loss, val_accuracy = evaluation(model, valid_dataloader)\n",
+        "    print(f\"Epoch {epoch + 1}: Train Loss: {train_loss:.4f} Val Loss: {val_loss:.4f} Val Accuracy {val_accuracy:.4f}\")\n",
+        "\n",
+        "    if val_loss < best_loss:\n",
+        "        best_loss = val_loss\n",
+        "        # Create the directory if it doesn't exist\n",
+        "        os.makedirs(\"../models\", exist_ok=True)\n",
+        "        torch.save(model.state_dict(), \"../models/ElectraForSequenceClassification.pt\")\n",
+        "        print(\"Saved the model weights\")"
+      ],
+      "metadata": {
+        "id": "fZuGfdtkPvlC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "48c02489-ace7-4c5b-dd00-8bd96f63eb07"
+      },
+      "id": "fZuGfdtkPvlC",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1: Train Loss: 0.2808 Val Loss: 0.3079 Val Accuracy 0.8805\n",
+            "Saved the model weights\n",
+            "Epoch 2: Train Loss: 0.2075 Val Loss: 0.3230 Val Accuracy 0.8792\n",
+            "Epoch 3: Train Loss: 0.1564 Val Loss: 0.3281 Val Accuracy 0.8835\n",
+            "Epoch 4: Train Loss: 0.1068 Val Loss: 0.4038 Val Accuracy 0.8782\n",
+            "Epoch 5: Train Loss: 0.0794 Val Loss: 0.4947 Val Accuracy 0.8665\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = ElectraForSequenceClassification.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"monologg/koelectra-base-v3-discriminator\",\n",
+        "    num_labels=2\n",
+        ").to(device)\n",
+        "model.load_state_dict(torch.load(\"../models/ElectraForSequenceClassification.pt\"))\n",
+        "\n",
+        "test_loss, test_accuracy = evaluation(model, test_dataloader)\n",
+        "print(f\"Test Loss : {test_loss:.4f}\")\n",
+        "print(f\"Test Accuracy : {test_accuracy:.4f}\")"
+      ],
+      "metadata": {
+        "id": "Iq4RANJIPxJl",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 390,
+          "referenced_widgets": [
+            "ae8766b93d714048a5462df40b9ef21e",
+            "45a7dc7de0864bb0880ab4ee0fdbe2d7",
+            "a76c9759e2724d449341988b40028815",
+            "e55dc5be6c734428a399f28fd49068aa",
+            "11932333c5ea4734a921d7d8bbe14fe3",
+            "60bc7bc545be481eb7bc584e0bf47577",
+            "e0c388317ca349a0a8c38e6bb376955b",
+            "cea51461b7054c51bacd8a9a516445d0",
+            "05f4a3fe786843db86394e27fd01f9e7",
+            "9c15cbb2d9ae4a408270cda0c346bd83",
+            "5862e250f4df4aa590240e86d4ef9759"
+          ]
+        },
+        "outputId": "01a47500-9d30-43fe-ffa8-e4921a53601d"
+      },
+      "id": "Iq4RANJIPxJl",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/197 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ae8766b93d714048a5462df40b9ef21e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "ElectraForSequenceClassification LOAD REPORT from: monologg/koelectra-base-v3-discriminator\n",
+            "Key                                               | Status     | \n",
+            "--------------------------------------------------+------------+-\n",
+            "discriminator_predictions.dense_prediction.bias   | UNEXPECTED | \n",
+            "electra.embeddings.position_ids                   | UNEXPECTED | \n",
+            "discriminator_predictions.dense.weight            | UNEXPECTED | \n",
+            "discriminator_predictions.dense_prediction.weight | UNEXPECTED | \n",
+            "discriminator_predictions.dense.bias              | UNEXPECTED | \n",
+            "classifier.out_proj.bias                          | MISSING    | \n",
+            "classifier.dense.bias                             | MISSING    | \n",
+            "classifier.dense.weight                           | MISSING    | \n",
+            "classifier.out_proj.weight                        | MISSING    | \n",
+            "\n",
+            "Notes:\n",
+            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
+            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss : 0.3201\n",
+            "Test Accuracy : 0.8760\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "T5"
+      ],
+      "metadata": {
+        "id": "6ckCCdvnP7mW"
+      },
+      "id": "6ckCCdvnP7mW"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "\n",
+        "news = load_dataset(\"argilla/news-summary\", split=\"test\")\n",
+        "df = news.to_pandas().sample(5000, random_state=42)[[\"text\", \"prediction\"]]\n",
+        "df[\"text\"] = \"summarize: \" + df[\"text\"]\n",
+        "df[\"prediction\"] = df[\"prediction\"].map(lambda x: x[0][\"text\"])\n",
+        "train, valid, test = np.split(\n",
+        "    df.sample(frac=1, random_state=42), [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)}\")"
+      ],
+      "metadata": {
+        "id": "uy4CX8dVP9gb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 322,
+          "referenced_widgets": [
+            "272b1525d91248dda44fdf7ba7b59962",
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+        },
+        "outputId": "00c8429e-067e-4ece-8a91-86e176f1c3b6"
+      },
+      "id": "uy4CX8dVP9gb",
+      "execution_count": null,
+      "outputs": [
+        {
+          "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": "272b1525d91248dda44fdf7ba7b59962"
+            }
+          },
+          "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": "1d193a44ca36414b87127465d4738046"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "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": "8fb5d0e6968949b7a3ce6a25346e8edb"
+            }
+          },
+          "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": "4ff84e2b73514c5295e241e976209650"
+            }
+          },
+          "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": "206b247e339549b98753d17d9b406d7a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Source News : summarize: 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, we\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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "from transformers import T5Tokenizer\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",
+        "\n",
+        "def make_dataset(data, tokenizer, device):\n",
+        "    source = tokenizer(\n",
+        "        text=data.text.tolist(),\n",
+        "        padding=\"max_length\",\n",
+        "        max_length=128,\n",
+        "        pad_to_max_length=True,\n",
+        "        truncation=True,\n",
+        "        return_tensors=\"pt\"\n",
+        "    )\n",
+        "\n",
+        "    target = tokenizer(\n",
+        "        text=data.prediction.tolist(),\n",
+        "        padding=\"max_length\",\n",
+        "        max_length=128,\n",
+        "        pad_to_max_length=True,\n",
+        "        truncation=True,\n",
+        "        return_tensors=\"pt\"\n",
+        "    )\n",
+        "\n",
+        "    source_ids = source[\"input_ids\"].squeeze().to(device)\n",
+        "    source_mask = source[\"attention_mask\"].squeeze().to(device)\n",
+        "    target_ids = target[\"input_ids\"].squeeze().to(device)\n",
+        "    target_mask = target[\"attention_mask\"].squeeze().to(device)\n",
+        "    return TensorDataset(source_ids, source_mask, target_ids, target_mask)\n",
+        "\n",
+        "def get_datalodader(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",
+        "\n",
+        "epochs = 5\n",
+        "batch_size = 8\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "tokenizer = T5Tokenizer.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"t5-small\"\n",
+        ")\n",
+        "\n",
+        "train_dataset = make_dataset(train, tokenizer, device)\n",
+        "train_dataloader = get_datalodader(train_dataset, RandomSampler, batch_size)\n",
+        "\n",
+        "valid_dataset = make_dataset(valid, tokenizer, device)\n",
+        "valid_dataloader = get_datalodader(valid_dataset, SequentialSampler, batch_size)\n",
+        "\n",
+        "test_dataset = make_dataset(test, tokenizer, device)\n",
+        "test_dataloader = get_datalodader(test_dataset, SequentialSampler, batch_size)\n",
+        "\n",
+        "print(next(iter(train_dataloader)))\n",
+        "print(tokenizer.convert_ids_to_tokens(21603))\n",
+        "print(tokenizer.convert_ids_to_tokens(10))"
+      ],
+      "metadata": {
+        "id": "Ym1OL3MaP-qI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 595,
+          "referenced_widgets": [
+            "e8c217baef634cbd9062d63b2e0a9c69",
+            "0fbb01aba97b48c3adcd9d27006aa1fb",
+            "785514e52b2b4939bcc94a9e8b7517e0",
+            "8781315a02c447d197cdf01f1423b68b",
+            "60a3c38dd244430d9607c2d6d6171b4c",
+            "70d5ce3e3e76428a8796eb7a8b8b1e6b",
+            "04061012d7da416093830b6c11d88e17",
+            "b1559c108c4f47558a6ea3d724045442",
+            "a5d4104b519b477395137411e2a8012c",
+            "2a658c94b247423a9e4b6b4194ee54c2",
+            "80e99e7719bc4f3db1a26d8261f41b56",
+            "6ac92e897567420e84cfaf1fc42c85a2",
+            "313d77a5de2a40558feb39555abb02ea",
+            "875d59f24c484fc384dfa6033cb3522c",
+            "b28b08f830c14f258d6678047cf3ba6d",
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+            "ab8dc6ad0b3f47709410f28a8823d529",
+            "0cf839a3a14646339f5dc52ca96cce86",
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+            "201f12142c1547448bab152679e8029d",
+            "5dfa6d8940804f3b91ca041da602130d",
+            "f6b4f379149f4975a99ee664b5e27914",
+            "91d5b2318e7b44599e6b6ea2a34a6f10",
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+            "dc88b804da1b44088ec41c4228a3871f",
+            "152c8aee34b948c4a8bec1252c8c4959",
+            "64d7ceaa183744a1926edd6fad1b19f5",
+            "c6f944c236264452afbfc85c3a45bb76",
+            "4b1cd7a1fb6a4d70b781b3960850644d",
+            "384cbf6a0b8644c0b22adbb626668522"
+          ]
+        },
+        "outputId": "2ebc5280-93b6-43d0-a7b8-e236b0ba6a25"
+      },
+      "id": "Ym1OL3MaP-qI",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e8c217baef634cbd9062d63b2e0a9c69"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6ac92e897567420e84cfaf1fc42c85a2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f6b4f379149f4975a99ee664b5e27914"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[tensor([[21603,    10,   445,  ...,   356,    95,     1],\n",
+            "        [21603,    10,  4083,  ...,    11, 18915,     1],\n",
+            "        [21603,    10,   180,  ...,  4759,    45,     1],\n",
+            "        ...,\n",
+            "        [21603,    10,  8747,  ...,    16,  1671,     1],\n",
+            "        [21603,    10,  4935,  ...,   647,    19,     1],\n",
+            "        [21603,    10,   283,  ...,  3555,    57,     1]], device='cuda:0'), tensor([[1, 1, 1,  ..., 1, 1, 1],\n",
+            "        [1, 1, 1,  ..., 1, 1, 1],\n",
+            "        [1, 1, 1,  ..., 1, 1, 1],\n",
+            "        ...,\n",
+            "        [1, 1, 1,  ..., 1, 1, 1],\n",
+            "        [1, 1, 1,  ..., 1, 1, 1],\n",
+            "        [1, 1, 1,  ..., 1, 1, 1]], device='cuda:0'), tensor([[ 9995,    31,     7,  ...,     0,     0,     0],\n",
+            "        [14298,   343,     7,  ...,     0,     0,     0],\n",
+            "        [ 3746,  2095,   142,  ...,     0,     0,     0],\n",
+            "        ...,\n",
+            "        [25833,    17,    15,  ...,     0,     0,     0],\n",
+            "        [16388,     7,    10,  ...,     0,     0,     0],\n",
+            "        [16040,    53,     6,  ...,     0,     0,     0]], device='cuda:0'), tensor([[1, 1, 1,  ..., 0, 0, 0],\n",
+            "        [1, 1, 1,  ..., 0, 0, 0],\n",
+            "        [1, 1, 1,  ..., 0, 0, 0],\n",
+            "        ...,\n",
+            "        [1, 1, 1,  ..., 0, 0, 0],\n",
+            "        [1, 1, 1,  ..., 0, 0, 0],\n",
+            "        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0')]\n",
+            "▁summarize\n",
+            ":\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from torch import optim\n",
+        "from transformers import T5ForConditionalGeneration\n",
+        "\n",
+        "\n",
+        "model = T5ForConditionalGeneration.from_pretrained(\n",
+        "    pretrained_model_name_or_path=\"t5-small\",\n",
+        ").to(device)\n",
+        "optimizer = optim.AdamW(model.parameters(), lr=1e-5, eps=1e-8)"
+      ],
+      "metadata": {
+        "id": "22X3gS04P_1J",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 145,
+          "referenced_widgets": [
+            "d4a9980097b34145b3c2bb4d1a55be3e",
+            "21524634c0f1418b9458a593e24e63ab",
+            "02058f613ecd4934a836d7921eb2918a",
+            "b1de75a84be947389512c0ddef5eab37",
+            "79cf7777eaeb474fb64a722702944a7b",
+            "5f3de4821c6c44f7a4b738f0d6180ae7",
+            "76086792d7c54d48a093d2cfe495a299",
+            "1f34fb608ac0461d81f58008a60ca651",
+            "f484331e25f8466d9f1f756767a3c413",
+            "3b43fb8a57094cdb944e27a1697c453d",
+            "98cc5cdb341545d089d8198700d21188",
+            "0434eb9458764a5ebefdc4f3a400d6e9",
+            "3c01e3baf12c4dc0a3bee7d7ad81bc42",
+            "bbfc79453ff24e609299aca0352b3369",
+            "3bf30e6be8574730b5a878145cd46fe5",
+            "d5d26fb65b984a6d87d550adea6294ee",
+            "337b4cd365464acaa70cba83afd87d91",
+            "e90c6d12aed44e8a8f848f9af4afd3d4",
+            "6db65388e7604bd5afe0b25ed2a78756",
+            "2d04ba10586344d4a89847abab94671b",
+            "44b142708bc34b69b4417e55c82d201e",
+            "fca542f8e5204664ad714693633b2b95",
+            "9c70634510c44ffc9e7104b4c961da20",
+            "2f0b36033e894dfeb72efafcab13c1e8",
+            "a3f6f3fad6134f66b56cd399fd80a056",
+            "8cc13b59f9f8418ca9aa98dd5cd6d5d4",
+            "48dbba0e006c4caaa35489c197da9a2b",
+            "2f780bdc5c3c484cab597fbcdc4c8eff",
+            "c625a14e77f2418bbffe506a79672c65",
+            "dec3bc5a772440fb977e0ffece6402f2",
+            "8145ff5eb21649efa96b4150e51ddb6a",
+            "68e7f2d8e1c247919d29bd84c3289064",
+            "529a0efa6b874c9a9fe9cad64f8f9b56",
+            "8633222fe8b74373818d4062c013d808",
+            "62ce5ea9562d4ff0b00f907e75650572",
+            "a1c672915aea47818252c7f65c054c02",
+            "ffa1bef6364b44ee9c756ebfc2ef9ba4",
+            "d2ff91c19d00497bbceca9ae44128967",
+            "60836b8ca1b740cc8a065918ac49ebc0",
+            "018a15f841644acaacbda6c4d13b42a7",
+            "87f5d84b668d4541bc63620b8e20f836",
+            "f788824791d74fc8a4f545601048a189",
+            "a2b78f37802e4be0a9e30fbc611da756",
+            "d4580d25769c47dca9ca2013b011a3a4"
+          ]
+        },
+        "outputId": "afcd08f4-8aae-44b7-aafe-63a0a0a24ecf"
+      },
+      "id": "22X3gS04P_1J",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d4a9980097b34145b3c2bb4d1a55be3e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0434eb9458764a5ebefdc4f3a400d6e9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/131 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9c70634510c44ffc9e7104b4c961da20"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8633222fe8b74373818d4062c013d808"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from torch import nn\n",
+        "\n",
+        "\n",
+        "def calc_accuracy(preds, labels):\n",
+        "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
+        "    labels_flat = labels.flatten()\n",
+        "    return np.sum(pred_flat == labels_flat) / len(labels_flat)\n",
+        "\n",
+        "\n",
+        "def train(model, optimizer, dataloader):\n",
+        "    model.train()\n",
+        "    train_loss = 0.0\n",
+        "\n",
+        "    for source_ids, source_mask, target_ids, target_mask in dataloader:\n",
+        "        decoder_input_ids = target_ids[:, :-1].contiguous()\n",
+        "        labels = target_ids[:, 1:].clone().detach()\n",
+        "        labels[target_ids[:, 1:] == tokenizer.pad_token_id] = -100\n",
+        "\n",
+        "        outputs = model(\n",
+        "            input_ids=source_ids,\n",
+        "            attention_mask=source_mask,\n",
+        "            decoder_input_ids=decoder_input_ids,\n",
+        "            labels=labels,\n",
+        "        )\n",
+        "\n",
+        "        loss = outputs.loss\n",
+        "        train_loss += loss.item()\n",
+        "\n",
+        "        optimizer.zero_grad()\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "    train_loss = train_loss / len(dataloader)\n",
+        "    return train_loss\n",
+        "\n",
+        "\n",
+        "def evaluation(model, dataloader):\n",
+        "    with torch.no_grad():\n",
+        "        model.eval()\n",
+        "        val_loss = 0.0\n",
+        "\n",
+        "        for source_ids, source_mask, target_ids, target_mask in dataloader:\n",
+        "            decoder_input_ids = target_ids[:, :-1].contiguous()\n",
+        "            labels = target_ids[:, 1:].clone().detach()\n",
+        "            labels[target_ids[:, 1:] == tokenizer.pad_token_id] = -100\n",
+        "\n",
+        "            outputs = model(\n",
+        "                input_ids=source_ids,\n",
+        "                attention_mask=source_mask,\n",
+        "                decoder_input_ids=decoder_input_ids,\n",
+        "                labels=labels,\n",
+        "            )\n",
+        "\n",
+        "            loss = outputs.loss\n",
+        "            val_loss += loss.item()\n",
+        "\n",
+        "    val_loss = val_loss / len(dataloader)\n",
+        "    return val_loss\n",
+        "\n",
+        "\n",
+        "best_loss = 10000\n",
+        "for epoch in range(epochs):\n",
+        "    train_loss = train(model, optimizer, train_dataloader)\n",
+        "    val_loss = evaluation(model, valid_dataloader)\n",
+        "    print(f\"Epoch {epoch + 1}: Train Loss: {train_loss:.4f} Val Loss: {val_loss:.4f}\")\n",
+        "\n",
+        "    if val_loss < best_loss:\n",
+        "        best_loss = val_loss\n",
+        "        torch.save(model.state_dict(), \"../models/T5ForConditionalGeneration.pt\")\n",
+        "        print(\"Saved the model weights\")"
+      ],
+      "metadata": {
+        "id": "wOeVJZ6eQA4U",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e4c8b77a-575e-4f4a-ae3d-6334516ef4fb"
+      },
+      "id": "wOeVJZ6eQA4U",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1: Train Loss: 4.3448 Val Loss: 3.3392\n",
+            "Saved the model weights\n",
+            "Epoch 2: Train Loss: 3.4406 Val Loss: 2.9248\n",
+            "Saved the model weights\n",
+            "Epoch 3: Train Loss: 3.1536 Val Loss: 2.7739\n",
+            "Saved the model weights\n",
+            "Epoch 4: Train Loss: 3.0037 Val Loss: 2.6848\n",
+            "Saved the model weights\n",
+            "Epoch 5: Train Loss: 2.8876 Val Loss: 2.6200\n",
+            "Saved the model weights\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.eval()\n",
+        "with torch.no_grad():\n",
+        "    for source_ids, source_mask, target_ids, target_mask in test_dataloader:\n",
+        "        generated_ids = model.generate(\n",
+        "            input_ids=source_ids,\n",
+        "            attention_mask=source_mask,\n",
+        "            max_length=128,\n",
+        "            num_beams=3,\n",
+        "            repetition_penalty=2.5,\n",
+        "            length_penalty=1.0,\n",
+        "            early_stopping=True,\n",
+        "        )\n",
+        "\n",
+        "        for generated, target in zip(generated_ids, target_ids):\n",
+        "            pred = tokenizer.decode(\n",
+        "                generated, skip_special_tokens=True, clean_up_tokenization_spaces=True\n",
+        "            )\n",
+        "            actual = tokenizer.decode(\n",
+        "                target, skip_special_tokens=True, clean_up_tokenization_spaces=True\n",
+        "            )\n",
+        "            print(\"Generated Headline Text:\", pred)\n",
+        "            print(\"Actual Headline Text   :\", actual)\n",
+        "        break"
+      ],
+      "metadata": {
+        "id": "LsFoo72iQCY6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a0bc5c0d-62a8-473b-cc1a-40cac346ef7d"
+      },
+      "id": "LsFoo72iQCY6",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Generated Headline Text: Clinton leads Trump by 4 percentage points in four-war race for Nov. 8 election\n",
+            "Actual Headline Text   : Clinton leads Trump by 4 points in Washington Post: ABC News poll\n",
+            "Generated Headline Text: U.S. senators sharpen potential line of attack against Gorsuch's nomination to Supreme Court\n",
+            "Actual Headline Text   : Democrats question independence of Trump Supreme Court nominee\n",
+            "Generated Headline Text: U.S. warns Saudi Arabia over humanitarian situation in Yemen could constrain U.S. aid.\n",
+            "Actual Headline Text   : In push for Yemen aid, U.S. warned Saudis of threats in Congress\n",
+            "Generated Headline Text: Romanian anti-corruption prosecutors open investigation into Liviu Dragnea on suspicion of forming criminal group to siphon off cash from state projects\n",
+            "Actual Headline Text   : Romanian ruling party leader investigated over 'criminal group'\n",
+            "Generated Headline Text: environmental activist endorsed Hillary Clinton for U.S. president\n",
+            "Actual Headline Text   : Billionaire environmental activist Tom Steyer endorses Clinton\n",
+            "Generated Headline Text: tv presenter delivers news of Pyongyang nuclear test with her usual gusto.\n",
+            "Actual Headline Text   : Voice of triumph or doom: North Korean presenter back in limelight for nuclear test\n",
+            "Generated Headline Text: two activists jailed for more than a year after being accused of plotting against Maduro\n",
+            "Actual Headline Text   : Venezuela frees two anti-Maduro activists; scores still jailed\n",
+            "Generated Headline Text: House Majority Leader says he still troubled by Clinton email server\n",
+            "Actual Headline Text   : House No. 2 Republican says still questions Clinton's judgment in email matter\n"
+          ]
+        }
+      ]
+    }
+  ],
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+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
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+          "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": ""
+          }
+        }
+      }
+    }
+  },
+  "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": null,
+      "outputs": []
+    },
+    {
+      "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": null,
+      "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": "c274cc6e-86e5-454b-ca2c-4a76852dfef0"
+      },
+      "execution_count": null,
+      "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}\")"
+      ],
+      "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/",
+          "height": 608,
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+        "id": "QTVKkGiIflzk",
+        "outputId": "a335eac6-c541-49cb-f573-08211009218d"
+      },
+      "id": "QTVKkGiIflzk",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/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%|          | 0.00/5.27k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "14fe1eeaab11475c92df6a6870535073"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "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": "acbe4035bc8d4e69ad786a14c151425d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "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": "51b55b21f30041dfaf97532b7b6ccc8d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "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": "927b6e80d2384dda8651651ad772e21b"
+            }
+          },
+          "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": "ab35faf9cec640f7ad9be63c58739175"
+            }
+          },
+          "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": "fed4f9c2ecad4060aed0685d131ff87b"
+            }
+          },
+          "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": "62dca9a20a9145f8a7c31a2606068d59"
+            }
+          },
+          "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
+        }
+      ]
+    },
+    {
+      "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": null,
+      "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",
+        "\n",
+        "    train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
+        "    valid_loader = ###답안###\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(3):\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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 706,
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+        "id": "_ehi3D9Mf9ov",
+        "outputId": "475aef62-151b-4d78-d714-baec1de8fbbb"
+      },
+      "id": "_ehi3D9Mf9ov",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "======== Training: bert-base-uncased ========\n"
+          ]
+        },
+        {
+          "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": "ec81128410d9416f9f2419c4fee25225"
+            }
+          },
+          "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 [22:38<00:00,  3.10it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1 - Avg Train Loss: 0.2021\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 2: 100%|██████████| 4210/4210 [22:37<00:00,  3.10it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 2 - Avg Train Loss: 0.1073\n",
+            "Validation Accuracy (bert-base-uncased): 0.9197\n",
+            "\n",
+            "======== 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": "b33828b1fb55401c8b5680f2ef255521"
+            }
+          },
+          "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": "c4d2282d215b4fd3b17158e2aca2858d"
+            }
+          },
+          "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": "840f6040b9cd4f2bb824cb59bf5cca34"
+            }
+          },
+          "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": "b58315167edb4c249f224f3849f3e11a"
+            }
+          },
+          "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": "a357873cd8f6438b8028a5169def00b2"
+            }
+          },
+          "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": "933f19d1c97246b0ac790cac3c9eefd0"
+            }
+          },
+          "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": "9a4e92d63bbc4379b089ae0c4581ede9"
+            }
+          },
+          "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": "1333d257c124432ea04287f173aa674c"
+            }
+          },
+          "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%|          | 0/4210 [00:00<?, ?it/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": "bd2af7e90bd949de996cb9d1f0a8114c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 1: 100%|██████████| 4210/4210 [22:51<00:00,  3.07it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1 - Avg Train Loss: 0.1812\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Epoch 2: 100%|██████████| 4210/4210 [22:51<00:00,  3.07it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 2 - Avg Train Loss: 0.1112\n",
+            "Validation Accuracy (google/electra-base-discriminator): 0.9404\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": [
+        "- BERT (bert-base-uncased)\n",
+        "\n",
+        "Transformer의 Encoder만 사용한 양방향(Bidirectional) 언어 모델\n",
+        "문장의 앞뒤 문맥을 동시에 고려하여 의미를 학습\n",
+        "다양한 NLP 분류, 질의응답, 감성분석 등에 널리 사용되는 기본 모델\n",
+        "\n",
+        "\n",
+        "-ELECTRA (google/electra-base-discriminator)\n",
+        "\n",
+        "BERT와 유사한 Transformer Encoder 구조 사용\n",
+        "MLM(Masked Language Modeling) 대신 RTD(Replaced Token Detection) 방식 사용\n",
+        "생성기(Generator)가 바꿔 놓은 토큰을 판별기(Discriminator)가 찾아내도록 학습\n",
+        "동일한 데이터와 학습량에서 BERT보다 효율적으로 학습 가능\n",
+        "\n",
+        "\n",
+        "BERT Accuracy: 91.97% , ELECTRA Accuracy: 94.04%\n",
+        "ELECTRA가 약 2.07%p 높은 성능을 보임\n",
+        "따라서 ELECTRA가 사전학습 방식이 더 효율적이고 더 높은 정확도를 보이므로 더 적합하다고 생각한다. 학습속도도 비슷하였다.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "78IvAec4h_7H"
+      },
+      "id": "78IvAec4h_7H"
+    }
+  ]
+}
\ No newline at end of file