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"70151f7e1a01482ab8ed21ebf571d1d6": { + "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 \"huggingface-hub<1.0\" \"fsspec==2025.3.0\" transformers -U" + ], + "metadata": { + "id": "2eSXxqg4a9SF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "31462e87-bc42-484b-fad7-4168af4df9f9" + }, + "id": "2eSXxqg4a9SF", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: huggingface-hub<1.0 in /usr/local/lib/python3.12/dist-packages (0.36.0)\n", + "Requirement already satisfied: fsspec==2025.3.0 in /usr/local/lib/python3.12/dist-packages (2025.3.0)\n", + "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (4.57.3)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0) (3.20.0)\n", + "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0) (25.0)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0) (6.0.3)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0) (2.32.4)\n", + "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0) (4.67.1)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0) (4.15.0)\n", + "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0) (1.2.0)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2.0.2)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2025.11.3)\n", + "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.1)\n", + "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.7.0)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub<1.0) (3.4.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub<1.0) (3.11)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub<1.0) (2.5.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub<1.0) (2025.11.12)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "---------------\n", + "여기까지만 실행\n", + "---------------\n", + "그 다음, 런타임 > 세션 다시 시작 > 아래 셀부터 실행" + ], + "metadata": { + "id": "4rq77NfBbByn" + }, + "id": "4rq77NfBbByn" + }, + { + "id": "ac52f320", + "cell_type": "code", + "metadata": { + "id": "ac52f320" + }, + "execution_count": 2, + "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": "14c552e5-610b-4719-9636-da86ff82dbbb" + }, + "execution_count": 3, + "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": 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"9438eaefb2e2429c81560a39f4198d34" + ] + }, + "id": "QTVKkGiIflzk", + "outputId": "f8fd4638-4aac-46bb-afb6-7d86a3418d1a" + }, + "id": "QTVKkGiIflzk", + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "49cc161560ed430681c479fd32ff2f43" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "data/train-00000-of-00001.parquet: 0%| | 0.00/3.11M [00:00=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) (4.67.1)\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) (3.4.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in 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+ "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "dataclasses" + ] + }, + "id": "65560674d78347debc4b013f7ff1b1ad" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from Korpora import Korpora\n", + "\n", + "corpus = Korpora.load(\"nsmc\")\n", + "df = pd.DataFrame(corpus.test).sample(20000, random_state=42)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xQnLyCIpEeOs", + "outputId": "4db60a4c-5932-4d02-d50e-d093845e2a87" + }, + "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, 70.9MB/s] \n", + "[nsmc] download ratings_test.txt: 4.90MB [00:00, 37.5MB/s] \n" + ] + } + ] + }, + { + "cell_type": "code", + "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)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Q4HO1uXeEtkA", + "outputId": "501053b2-f71a-4566-b140-76c3c9e43c91" + }, + "execution_count": null, + "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" + ] + } + ] + }, + { + "cell_type": "code", + "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)}\")\n", + "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])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 808, + "referenced_widgets": [ + "0abdc43027be48baa03f2b525b36ef2d", + "449571cd6acc46e099688a226791b579", + "0d3ee7346c3746a78bf54e0db804eb12", + "c1524410fa9f4bfa8caa60f4eb67133f", + "e63e85ba57fb494f9d8d534750be8373", + "ad5b290cc7f645ddaad174f192aed55b", + "f3a8aa9a63d547fb960842673b29dafd", + "01835fdf6d5e497baf151cee44e1bc98", + "d15a448fc4304cf195298b94b7e51c78", + "98c6afb1685647559ed7a97466644d3f", + "180c3d1e1be2439584280d0a08032d4f", + "ba242d4d6bad48a0abadebacc0a79082", + "9fe88b838b414cd7bd25a75b7464677b", + "51de33c4655145a48f9db516102e1899", + "e36a9a698c6c43d68e9d16295caf68db", + "384179063eda48c39c936fc7f9c96a0b", + "8f88a0e6abf6433cbf30720d4274b49b", + "ec7422f9d2ac4097a3e72ad26ab76aca", + "19fddc33a071488582376a524566d6de", + "6fecfdc252064c5693391c08a4571a18", + "106532a770ae441b9c499bf3595b8cd9", + "9d61d470d9b840e0ad279b9f37204e92", + "f139a95c2d734e3698b154be81c4f99f", + "b6217ca23b8d4c20a0a0b607876c4f1b", + "a278d78b0af345b7b8f6cd2ec79e4b2a", + "d8fae5e234b14f3d9c801fc8c143ce57", + "305bbc4823d5454bbfd94ab3f7ff05b7", + "71b0c22512fa4d9bbab8798b6a941d51", + "d580028ae5ee4760aad28f286e58ddbd", + "cf3a5a9e3a244059b0f652da3712f66b", + "307292baa9d74fa78dd19a99537aed80", + "3c05d336306c4742a2f3be0da4387da6", + "957a46b3bc704dae8ed328aca5f2961f", + "4ffd04972f024ea9883b655da9ecfdb1", + "39f210bf54eb4992aff06c34048a7e36", + "4d42dde424b44c7d94df04928a6ad389", + "16f02ee14fa34e22b0ea357346e9ced6", + "6a648550c1c44dbd867f24b5de9de81a", + "c64c45d6afb348c4986e13b427145903", + "18ab259adffe41f58f83cdea4ac5c37c", + "0e77de468b4e4fce9a5f5f32cd5a6b51", + "7961fa90f82f43f0bbc0dfa0cc848e65", + "5577b01527fd411cbb1fbd88ac643a0b", + "4cf0919103814e81b907a9822b9a8d4a" + ] + }, + "id": "tvgwBnEGE6Iv", + "outputId": "55741d52-287b-4178-f6ab-0e8aa7abd4c2" + }, + "execution_count": null, + "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/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": [ + "tokenizer_config.json: 0%| | 0.00/49.0 [00:00=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", + 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sha256=eae123d897a18e7a6599c6f19b3472a3a2ddfbd9501c09faed029e543163e641\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", + "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": "7kY27pFBFRmS", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350, + "referenced_widgets": [ + "735022d99dde4a89bbace49f737389e8", + "b9e23d0ad67a48ac9f5d7a11fe26955f", + "441541e07b234789a3d7c2a61b6539d1", + "3d710b424e2047ecb2319bbd24191e6a", + "54aef50b5cbf4bd59586c88c359cdb3c", + "91d157785a8a4986b5d4e6729b692c73", + "9f8dce90b6b446b7b293f96764026c86", + "c4f8f678ad7b45019734675ccfb0de91", + "30469a62917146d8a3bf90e098736681", + "c17ef1c2a43a460ab2bf9dc3bfcc43a5", + "b9199e3e9e7c44459321ab6ec1a94495", + "e5fa9af0891e446d88b1671f25c5b134", + "e1ff5f81ffea4d31ac77eebd4dc26f0d", + "730f13c68d2445839d368e3a40ccccce", + "36be00e2ab9943b2b220a95cf05209dc", + "9fe6701cf7674fecbcc85bd5613b9fef", + "9d9bdb179eee42988c900f7c7db59c4b", + "2ffd8a4f8ade4978b95698c59f5576cd", + 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