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install -U transformers datasets\n" + ], + "metadata": { + "id": "2eSXxqg4a9SF", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "97aa9ee5-0f17-4fe9-e452-351a400d5089" + }, + "id": "2eSXxqg4a9SF", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found existing installation: huggingface_hub 1.2.3\n", + "Uninstalling huggingface_hub-1.2.3:\n", + " Successfully uninstalled huggingface_hub-1.2.3\n", + "Collecting huggingface-hub==0.34.0\n", + " Downloading huggingface_hub-0.34.0-py3-none-any.whl.metadata (14 kB)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface-hub==0.34.0) (3.20.0)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub==0.34.0) (2025.10.0)\n", + "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from 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already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (6.7.0)\n", + "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (0.4.1)\n", + "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (1.22.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n", + "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n", + "\u001b[0m^C\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": 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": "5789ac46-c7ae-4aa9-ebad-7d5995d085fe" + }, + "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": 582, + "referenced_widgets": [ + "719be19d5cf04225a13949a448f39f50", + "cd5599337e294171b77825a002485bd9", + "54bea39ddab64f73bb52231f9b8ab8cf", + "4e3e7246f5374a9ab1647f2ff2cc860a", + "0e563d758e0e4c44aaea93472f762b82", + "a362329b42df4773ba17fd32950aefad", + "3cc829cc65454f98bcded0eef28d23e7", + "cea2ec45cdba43f0a75d24f7669fe788", + "77ca702d3df643388269eed4e9918a65", + "08c6c495107645a58a867101a3b12ed1", + "691af21aa30244e6b97c5a176c08f80f", + "da1d34d0889e479a846815387994ea39", + "2058cd9dad094f6ea0f1f2eccd17fd71", + "fe4762f3d8bd477d8ba2cd763f756f53", + "1ce97d0662bb44e19b8f0235ec46fc40", + "ae72db95876e4b3c93c503b351cc331b", + "ebf9d71699d54c6d9635d7732648956e", + "a7b98eef9d1e4158ac141a2f37b575a9", + "721bd0b1fc3f4f44a083d4c820d1dce7", + "a83c02757ec34510bfaa5ddfaa20864b", + "af4d22c333ed41d78e20ca8878f5a311", + "31b906d833f648739960e4c2734a52ab", + "c63155d4bf9c421b8871321b93d93630", + "b0dbb85587bc4514a5272332eba14618", + "1ebff294c9c44742a7d0a502f51c2d94", + "ee80b433531447cb83c0c5f6ed7731f0", + "755218c512364a0b95ca2b98f2965d7d", + "cdd91c484fe94f26a7bcaeaafe148eb1", + "66c9997bd43d44ee8257966b86190ce9", + "8736040b37f64c88bc12e3ce127ea79e", + "4d1390fe7f994276a03e38238f94be5b", + "12b91a2891aa4290b840317092789915", + "7962741d8c8b49b99ac85aac05566479", + "f3d68579ab044fa5a56de91ab173066e", + "aa30dab0a3f74969a7ad2272a13411a4", + "caad0c05ca924b7d9e13e358ef295855", + "a4bd7fb43266433f8d5856fcaadaf6e4", + "e05f6fc8f8254201a4b5ebe04efb9303", + "43a4dddb30e94fb98f2635a409ea8d52", + "87ef6cbd22544b3f989e37fa66781b75", + "7de4366f20574ec793dfb873522b64ee", + "cb12d6b63e98422e84432a5d95c25520", + "9ad9c2f31b2c42528da597277d36632a", + "7da4f60c6cf54de6929e6c8a5735b547", + "f0337f4d76cb4438bf3ac01209faa24e", + "4ae6b4c398b84a7b9fb9d8a179925592", + "ff790935e7424d00aaa7e7700570679f", + "ef0bc8cb2d0f4a32b2dfc0f64788e7b4", + "c5c6a96e2c424669b8fc0ddc2d1ac422", + "e5dae7928a74489da22a9d33601cd392", + "ceb0d4ff3d3e4a02933e97748e896c32", + "1f420636d4084e8ca2fc7acc9572e5de", + "0b6a7299f57344aab8334d960da879b7", + "ffc87db67d374249b03337a4849c34da", + "0113ced8f24c446499a61d7d7807343f", + "ced9727c3a6b414b8f1a84a8086f3a9d", + "8b2f395b4eb7478f9106b917205a771b", + "74927a2bafe74238a4857209f7bb949d", + "f3704edc3cde4c16a88cdb34b55c7ae8", + "31849e210a8a4e26a972a9b11e5bdb9b", + "5754cd597dfd48bf8fefcce935c8e426", + "802c06cb504f45d4896d8a003b64c738", + "e0c3d2ba74b34fffa6fab0e26491d97c", + "76791bdf5af649b1819dbb911cfebad1", + "00c531bc7b3246f09116722288d466f5", + "842959a2159545138e33cc7e04c2a77e", + "99a4234692c2471da99f19c0ba15ce5c", + "f0ccce3a41504225b1014798b66ee824", + "b22ee7cc092c4864859149b75870003b", + "45f77ed00dd042658d464d8cfc3d02a7", + "a367479bcfbb42968801ad302a935d9a", + "62e1e0db91d14180b7bc15f2daa5be3e", + "8576441aa1034f398917ee960b447112", + "bf6813f0f1e643888164e44402d72a12", + "844838346d474f79a7cfdb95b0662922", + "4f103f02cc004f1bbe6f108c08a31e4b", + "d9bb648b31b9423aa7555aed7184bb24" + ] + }, + "id": "QTVKkGiIflzk", + "outputId": "17b7e42e-2742-41a8-d1d9-bd6ec4216f63" + }, + "id": "QTVKkGiIflzk", + "execution_count": null, + "outputs": [ + { + "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": "719be19d5cf04225a13949a448f39f50" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "data/train-00000-of-00001.parquet: 0%| | 0.00/3.11M [00:00=0.6 in /usr/local/lib/python3.12/dist-packages (from Korpora) (0.6)\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 /usr/local/lib/python3.12/dist-packages (from requests>=2.20.0->Korpora) (3.11)\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) (2025.11.12)\n", + "\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", + "!pip install Korpora\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", + "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": "siBJfeOEm7zv", + "outputId": "eab02220-a496-4481-b990-9f1c178a73f7" + }, + "execution_count": 2, + "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": [ + "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": 642, + "referenced_widgets": [ + "130942f8d7634f8f9f3d92bda3ed081d", + "27af16f75bb2495ea1f081bb203452f7", + "39c3c2eade4046a9b19d77647444141e", + "b63603f36a10409dbf3694ac6c327471", + "36735a34ce844a66a27a707f268d6f3e", + "5131a3055cc0440ebcfbaeded504bf2c", + "3c42e498f5274b3c8330292e6181bc08", + "20365a05251c4e40b24225137515dd45", + "96f2484ff91f4dc797af065b1fe595d4", + "a4d6075c0ee645118a70af2348d9400a", + "b14eac36a3464cdcb7a68b979106e816", + "cb6a192243c841c3ac32ed2f537a5932", + "9f836c6048094314bb6480bd422d3e41", + "169704ed159d4f9b8a45ed1589849251", + "88a7149ca6f54bdd993d026f8e8a0d36", + "8e96caa7383c4721aa5d76294409e0e3", + "26ac3257d74440fb84cd796e0df311aa", + "480393fa64074b99b08f1ee3fbc339af", + "cd20fbc775c54f2681d4d7525b43d3ea", + "f083da1b1cde43229f53113d61a6d31f", + "62a0b346defd4d2792dcfd0b391f91aa", + "94a9b4dd546a45e9bf32bf81808168a9", + "77f053b2b2f142f9a4b35500b50333a1", + "8f174676e41d4a8c9063b12a9df3ecb5", + "8309793f40834ce4be64988fbd20d104", + "d6cf76576cdc49f0a170b287e2f58dd8", + "5be5f0a27b7e4a01b21f25968ea65029", + 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"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 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sha256=baa9f22440a7ca2891ee7a97bd64f9752b22619f1da4780e2162e05e514525b2\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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 326, + "referenced_widgets": [ + "29d64dd3b3f84af5adeac874dd4a3571", + "870371b95e614032bc35137920ba5c96", + "0ee8389e34ab4bd585cbdd7e87de17e3", + "d4e8489e9b1247cd8223e89315b08171", + "332828f59774463f9e0ba197ede13228", + "2c1dd1f9923a455c8ea3f5694feb6616", + "2461ae539aff444aa3c28eb4355d1b82", + "fcb9c138f2754c00b15c66b5fd16865e", + "e45e24f9966240718e12ae53aeffb5cc", + "52fc9588bb7e474b8ea8f8d2ae4bd759", + "8245122571e748cdb677a606f58d1d1c", + "1f2090141bca4e88a1bec9551d6d77f8", + "671eb066e62c42b8b50dc5eb57fb39ff", + "800e104d79584312a6848792cbc89287", + "910bb11bee7c47d4826366b9f1c2f3ae", + "b2407691afa344a8bcecf53fdd6c8470", + "f9182e8c0f3943c0bc3d8eec0c55c5e9", + "ed8c765e95574c76840acf44ad7003ad", + "fc29490417914b96bfa4cdda13d37398", + 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