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"height": 524 + }, + "id": "WuW2z6Q06H6V", + "outputId": "09d6f532-80c2-4d8c-c46c-14e52dc07252" + }, + "id": "WuW2z6Q06H6V", + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting huggingface-hub<1.0\n", + " Downloading huggingface_hub-0.36.0-py3-none-any.whl.metadata (14 kB)\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: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0) (2025.10.0)\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: 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", + "Downloading huggingface_hub-0.36.0-py3-none-any.whl (566 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/566.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m566.1/566.1 kB\u001b[0m \u001b[31m24.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: huggingface-hub\n", + " Attempting uninstall: huggingface-hub\n", + " Found existing installation: huggingface_hub 1.2.3\n", + " Uninstalling huggingface_hub-1.2.3:\n", + " Successfully uninstalled huggingface_hub-1.2.3\n", + "Successfully installed huggingface-hub-0.36.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "huggingface_hub" + ] + }, + "id": "e526c3012db64982887b7b4ab94bde46" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "---------------\n", + "여기까지만 실행\n", + "---------------\n", + "그 다음, 런타임 > 세션 다시 시작 > 아래 셀부터 실행" + ], + "metadata": { + "id": "4rq77NfBbByn" + }, + "id": "4rq77NfBbByn" + }, + { + "id": "ac52f320", + "cell_type": "code", + "metadata": { + "id": "ac52f320" + }, + "execution_count": 1, + "source": [ + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n", + "from datasets import load_dataset\n", + "from torch.optim import AdamW\n", + "from tqdm import tqdm" + ], + "outputs": [] + }, + { + "id": "393f4136", + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "393f4136", + "outputId": "682af377-ef50-4271-b4f6-866ac900aebc" + }, + "execution_count": 2, + "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": 628, + "referenced_widgets": [ + "223229e9d3e14cdb9970ee3d81cd2af9", + "d0ec699c8e974ee0b8a14f02c31023c0", + "8eb37f223f46433cafcf449e3e646009", + "4644360885534062b518a1e156f24f09", + "b2250b685a50465f9aab41aecbe56d92", + "eea6c2f9eaed4b0d88b0df430a79c593", + "5d868574ab844bf2a949a9b15f768ded", + "c5c78f2a0cfc4ef59470de6e2465877f", + "46a61451f3f94e639f824cea6c11fbb7", + "aed6e8962be64fb4b5abc4a54d4c0c6d", + "e8d518325fae4e7d9d5e86fc63ff92de", + "fc8fd85b71d24372be310bd4db013bdc", + "f372be87afdb47c0bd3f25daa48bdc0e", + "e2dc75e0702f4bf39f36f533462c3d50", + "e07a946b82b142cd9fb77403bb81edab", + "3739a71326604e79bca29aaa2eef5303", + "652de85c94304eaebd6b67975a7ab5e4", + "65254015920343e28bacbf0cb810d065", + "dfdc037f1b9a44e489039037250b457c", + "3c13d5bcc59249ae9c50afec476fbc90", + "915a63da44104b97a81e74eb6d46ec7e", + "2a7bd0e9b6cc418a87bc9febbefb3ab0", + "525baf17cb1b4a639f98e1430f6bd758", + "fbeb8752f0c64e00b8c27c852c17734e", + "caac30d0cc914b5f889dcd0618ff0208", + "ec776a8b24554b11819e10318bccbb0d", + "9f2e33b356d94ab489acbe2417c681a9", + "b694949c0a9f49cb90db631ac36f440b", + "842a41e5e11e49e394c42361fb1bec3f", + "be9312558c3445ce94b5220bea3014f7", + "be9596cabfcf42089662c5aa90760ea8", + "da1112f59e2b4f959928ef61fc408717", + "a025282a56604f678437082e248ac61c", + "b3d306234ce24f2a979d41c97257400c", + "1b2cd44aee3840569e8232fccd68125a", + "7868b746afc144868b9c01d43e63e89e", + "8c23816bf23f4ef798f478ac44802f7c", + "3b2388d0eec941cfa66b31ae6363ac06", + "c43cbd70fa1943f09d708fe3a77af78a", + "e9ef132d909943839acb06f583fc9884", + "2b6d6a9de69f4680b710c89fd7fbcfb8", + "50767b37bb0741a29af156d283209968", + "4f1c490b6bfe4d72a1fd23926b771f07", + "da70603219664b3c9f52f3cbcb109cb1", + "0c651f9994b942239d09a13505b2805a", + "bd007804d1754953b8cf33bd42d21454", + "7de36f9c8f8f425ebe16ee990ad10ae4", + "980316b1d1ca4f6bb3054463679eaaee", + "d79e2346875048e2aa2f57113ae1c87e", + "16acbb0b92ba4055ad898c6277546b8b", + "89d0c3655b214830a7ebcdd2c2fc1306", + "27d40376889744eab2a69e97285ff7f7", + "88049e5fa1454bcebdb1e8de561c1c77", + "035e506ac6fb436a97301d5fabb0ad56", + "d783dedda30a4706bdd170c0e50b6d1b", + "ed20615539f34592805ea614f98412dc", + "a79197a43e8844a49d4cf3ae3227ac29", + "fe77fcb4b8e0446eb641fe84ee1a795e", + "22f5f11ef4534138b654545da02d753a", + "6a80871eb6fd49f5a64d1533b48f600d", + "c02a5930ce8448928f0bd5cee2dfab0e", + "c20fb15a96b141db98013916b102bf09", + "58ea73717a554f13b7a954c3c989ad87", + "b036310e09434e698d94461e3431212a", + "87260e58f9d7472dae87b057e005d75e", + "2f63d28789dc42258836e09e4781a7f1", + "6413fa7f16ac48fca2d6acc15350ab3a", + "573dfe0b72634502a686fdff22f5d342", + "9fa7a7250b53466d86612b91013d85f3", + "7a53370598a4415eb460730c997b1d8c", + "1036014d4bb14c848c84b80308291895", + "50b8c7459f5748f197b33d9b0a880819", + "cfe3857df2534cf8adae70110e02a99b", + "d821471cef5f4d8dbf7781c1ceae78b1", + "12c6e0a1481d4bf492e03dfb32ef0d49", + "1e092ffe70d64582aac76b63565efbb1", + "97ec84b7fdfb481399008bade2b0ee9c" + ] + }, + "id": "QTVKkGiIflzk", + "outputId": "d6702cf3-5fee-4ec0-b4f1-198acaa7e429" + }, + "id": "QTVKkGiIflzk", + "execution_count": 3, + "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": "223229e9d3e14cdb9970ee3d81cd2af9" + } + }, + "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/python3.11/dist-packages (from Korpora) (0.6)\n", + "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.11/dist-packages (from Korpora) (2.0.2)\n", + "Requirement already satisfied: tqdm>=4.46.0 in /usr/local/lib/python3.11/dist-packages (from Korpora) (4.67.1)\n", + "Requirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.11/dist-packages (from Korpora) (2.32.3)\n", + "Requirement already satisfied: xlrd>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from Korpora) (2.0.1)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.20.0->Korpora) (3.4.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.20.0->Korpora) (3.10)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.20.0->Korpora) (2.4.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.20.0->Korpora) (2025.4.26)\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wjfhebT3uKJH", + "outputId": "062de73b-ad83-4b63-dde1-d7ec1db72e44" + }, + "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", + "Training Data Size: 12000\n", + "Validation Data Size: 4000\n", + "Testing Data Size: 4000\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.11/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": [ + "# 7.15 네이버 영화 리뷰 데이터 불러오기\n", + "\n", + "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)\n", + "train_df, valid_df, test_df = np.split(\n", + " df.sample(frac=1, random_state=42),[int(0.6*len(df)), int(0.8*len(df))]\n", + ")\n", + "#print(train_df.head(5).to_markdown())\n", + "\n", + "# 20,000개의 데이터세트를 6:2:2로 분리\n", + "print(f\"Training Data Size: {len(train_df)}\")\n", + "print(f\"Validation Data Size: {len(valid_df)}\")\n", + "print(f\"Testing Data Size: {len(test_df)}\")" + ] + }, + { + "cell_type": "code", + "source": [ + "# 7.16 BERT 입력 텐서 생성\n", + "\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", + "# 토크나이저를 텐서 데이터세트로 반환\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", + "# 샘플러 클래스를 활용해 데이터를 목적에 따라 샘플링\n", + "def get_dataloader(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", + "epochs = 3\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_dataloader(train_dataset, RandomSampler, batch_size)\n", + "# RandomSampler: 데이터를 무작위로 샘플링 -> 학습에 적용\n", + "\n", + "valid_dataset = make_dataset(valid, tokenizer, device)\n", + "valid_dataloader = get_dataloader(valid_dataset, SequentialSampler, batch_size)\n", + "# SequentialSampler: 데이터를 고정된 순서로 반환 -> 검증 & 평가 배치에 적용\n", + "\n", + "test_dataset = make_dataset(test, tokenizer, device)\n", + "test_dataloader = get_dataloader(test_dataset, SequentialSampler, batch_size)\n", + "\n", + "print(train_dataset[0])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 651, + "referenced_widgets": [ + "2c9104b4dd6c4b95924f30a1dc3f781a", + "36d9f70be42a47e59539b96747380ab4", + "453c764e13d6404da0932be9037ca233", + "1b41c11689f34321889c83e205f8477c", + "d6b50b6fd5be42c894be49f4e72c1cb8", + "ec91c789783b4039ab803773f730f057", + "54c613420e0f46b385065f043e9c47de", + "4fd856797e7946c9b54b6c7595d850df", + "7678bcbb1edb4c76a15027a473581dac", + "c4edd5fb948c4fa685d8a605afd570ca", + "f9b7a2fa674a4036ac7b79d3e660304d", + "b056e454d5a340448239c0659462d96d", + "a82ec3e772ea43809171ddf530431e11", + "25a0cd327e0b43d9adc1796792b464a1", + "04e1c2021c394ab9993daf7a913166e4", + "2242d418b6b642708d251286a7667267", + "6bb2840db4f84d2fbc41b71ae68d470e", + "0a3aa0a96ec0419d8d07f22bb6d356d7", + "d82b0fc64c6449229e95ba747f8d3ff9", + "f1d1dcbbccf741e48bfa5cc086b510df", + "793635ddf7a14a27a21eada447766cc3", + "b767fdc2d1ec430e8508653a6f79b29a", + "3130415bcd7a4c14bc2347d6422a2ccd", + "60b06a6038174845ae05453d4e632d1c", + "d1b0fd2647c2496693b0cf6ffdf73183", + "c4090e2014104c7693a70d5f59ce64b6", + "c3def1d0ccb649e59bf500c62530e9bf", + "6d886e338d7b4af3b89a9f3b4e7f974f", + "2be018c13c584afbb88adbdc018715c0", + "327f6a80b618403c9f236aa9992d97c0", + "9437a61f83314d73ac18dd6d489ab347", + "62747744beca4aaba9cf69986d5a715d", + "56ccc5ac974d46f991f0df2e8b5cf640", + "4b5655fabd14476ea21ebb2d5945c118", + "9eb221b98228457da6a8b67d0c0a5b42", + "bda25375550347d8b32fc104d04fe530", + "7083b3dfe13f48c0b51a32206e117ae9", + "af24b639d4254b05a6158e338395e26c", + "bbd98b057251459aad3427df5f7ab092", + "7d5960b8b7dd4ae08454eb5d2567b945", + "4a16800040f4407ca170e748aa1b235c", + "2f7b504a41ea43c5b475c8c9b4ab17c2", + "412228e646174d259db8398b93476a14", + "ae1ba5fc1c3e47a79613ee1b9fd7854e" + ] + }, + "id": "FEh1vhBQuUub", + "outputId": "d50b970c-5800-4446-93a4-6637d40573e7" + }, + "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": [ + "tokenizer_config.json: 0%| | 0.00/49.0 [00:00=1.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.0.2)\n", + "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (18.1.0)\n", + "Requirement already satisfied: dill<0.3.8,>=0.3.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.3.7)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n", + "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.32.3)\n", + 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fsspec-2025.3.2:\n", + " Successfully uninstalled fsspec-2025.3.2\n", + " Attempting uninstall: datasets\n", + " Found existing installation: datasets 2.14.4\n", + " Uninstalling datasets-2.14.4:\n", + " Successfully uninstalled datasets-2.14.4\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "gcsfs 2025.3.2 requires fsspec==2025.3.2, but you have fsspec 2025.3.0 which is incompatible.\n", + "torch 2.6.0+cu124 requires nvidia-cublas-cu12==12.4.5.8; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cublas-cu12 12.5.3.2 which is incompatible.\n", + "torch 2.6.0+cu124 requires nvidia-cuda-cupti-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-cupti-cu12 12.5.82 which is incompatible.\n", + "torch 2.6.0+cu124 requires nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-nvrtc-cu12 12.5.82 which is incompatible.\n", + "torch 2.6.0+cu124 requires nvidia-cuda-runtime-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-runtime-cu12 12.5.82 which is incompatible.\n", + "torch 2.6.0+cu124 requires nvidia-cudnn-cu12==9.1.0.70; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cudnn-cu12 9.3.0.75 which is incompatible.\n", + "torch 2.6.0+cu124 requires nvidia-cufft-cu12==11.2.1.3; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cufft-cu12 11.2.3.61 which is incompatible.\n", + "torch 2.6.0+cu124 requires nvidia-curand-cu12==10.3.5.147; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-curand-cu12 10.3.6.82 which is incompatible.\n", + "torch 2.6.0+cu124 requires nvidia-cusolver-cu12==11.6.1.9; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusolver-cu12 11.6.3.83 which is incompatible.\n", + "torch 2.6.0+cu124 requires nvidia-cusparse-cu12==12.3.1.170; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusparse-cu12 12.5.1.3 which is incompatible.\n", + "torch 2.6.0+cu124 requires nvidia-nvjitlink-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-nvjitlink-cu12 12.5.82 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed datasets-3.6.0 fsspec-2025.3.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "datasets" + ] + }, + "id": "e4e79dfa536042cea8ab86fc32d21440" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 7.18 뉴스 요약 데이터세트 불러오기\n", + "import numpy as np\n", + "from datasets import load_dataset\n", + "\n", + "# 뉴스 요약 데이터 세트: (입력) 뉴스 본문 + (출력) 요약한 짧은 텍스트\n", + "# 5,000개를 샘플링해 6:2:2 비율로 사용\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_df, 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_df.text.iloc[0][:200]}\")\n", + "print(f\"Summarization: {train_df.prediction.iloc[0][:50]}\")\n", + "print(f\"Training Data size: {len(train_df)}\")\n", + "print(f\"Validation Data Size : {len(valid)}\")\n", + "print(f\"Testing Data Size: {len(test)}\")" + ], + "metadata": { + "id": "qkGESaTf03YC", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 496, + "referenced_widgets": [ + "12f88e15672440e1833bb97add30a380", + "97191f631d1b47a580f25d0342151445", + "0e1a8ee63229438b943c2ee503a83c65", + "245f11b2f22f4fd5a6a71c0840a4c0e4", + "e0b890e80e0646d9a65d8b585e82c7ad", + "1d42634c900345ca853c284766d9b245", + "4d185edb4cfd4120be4e289718c15cd9", + "18f0cfe3d797404ab9793e4a8a612ede", + "62cdbbfa360a4304858f95b319d5d125", + "9fece50eb6df401680fb45a738387072", + "70ebcdce04e44f5b91459c22dc6c1efc", + "15b20258fea541a799e7c681a6ceae9f", + "04d1eda459354e9db646c0ed1fb2c71b", + "33c894b799134ca9b6384aceda29bf6c", + "3f4f203aaa0b4a8ebcceaa01ae350478", + "ecfa7ed96dcd4ac9b3dfbaa4e89d40fa", + "2bbaf73ded3a4bea81b40fb1d3320287", + "539a0a7a4c8c495f9a1e55a2b785e049", + "db3fa885552544beb85b4ea334b6c56e", + "795709c660314580b9c6915e79fd9e6c", + "ee1345d89dc64953ac504eb5babfedb8", + "bbc6f9aa47b042c696b5c1e0a9da03f3", + "84268f9f844e4a61a14629884c63ce41", + "8083ba62e49242688e7fd04901b4a3d0", + "03b09a510bda404b8ea1f7378ca57314", + "3125b75687ab4ed6b079b999d15fabac", + "f8a8a2f24b574f3099d0c0802d1c588c", + "0b46814056624ef6bec9be4409f568fa", + "18672a7addec47bc9a08fb15cae6ac6a", + "31624ae172134144beb72bb041cc5528", + "00c6269b8fde4578a6a6e10cdb4bfc5b", + "0b0e2869a9584cad996a355f9f0d8bfa", + "b482eaa2dc83479892e8f141b2b5e509", + "2b3719b9d643433ebc3d7f2b456722e5", + "3f6f028d9f904723a0ff530822459f52", + "74ced353b083475facae20df0cbfd4eb", + "6f03cf84d26f4d488261b1c51f445d2f", + "c47d03378df2481493b05597376ef4cd", + "19325745ba2145798e3238ded62a6464", + "02586f0857b24929a3a0d2bd76e50d27", + "d2afe8e8d9344228bc304827329d8151", + "8115ac4efd8746e488c3fcf1c792ac54", + "3f71dd2b6e654c4da3c8fd929b6c5008", + "2fa6976bff734eed9d471b409cf3550a", + "492d607d65e64502b35121412832d5c5", + "fe147d6930f04bdbb8ae16a67e607ffe", + "ba5c2cc551454e7f9454dfcb5e463caf", + "3be82242a971463eb7c307f2b7aa6941", + "cc89f3fb8a7141c9a5a8dffbb636ab96", + "cad884e27151460fb7b5406c35a29c3b", + "df583325984042eb80a786043c9b98d2", + "a632ec8e21da489888be7b2f83f8562a", + "dfab153a64534eca8c5375de73534753", + "bba3ace52d8c4ebba662187515ce5dbd", + "4074424741c64368b1aa067d40c61e55" + ] + }, + "outputId": "1c00f28f-ccb2-488b-8217-4c5dc275d1db" + }, + "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%| | 0.00/2.02k [00:00 학습에 적용\n", + "\n", + "valid_dataset = make_dataset(valid, tokenizer, device)\n", + "valid_dataloader = get_dataloader(valid_dataset, SequentialSampler, batch_size)\n", + "# SequentialSampler: 데이터를 고정된 순서로 반환 -> 검증 & 평가 배치에 적용\n", + "\n", + "test_dataset = make_dataset(test, tokenizer, device)\n", + "test_dataloader = get_dataloader(test_dataset, SequentialSampler, batch_size)\n", + "\n", + "print(train_dataset[0])" + ], + "metadata": { + "id": "xIZRMcnZ2Ayu", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4e254d20-f477-43e1-9c6d-d5acd4c372b7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(tensor([ 0, 495, 1889, ..., 1, 1, 1]), tensor([1, 1, 1, ..., 0, 0, 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" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 7.20 BART 모델 선언\n", + "from torch import optim\n", + "from transformers import BartForConditionalGeneration\n", + "\n", + "# 12개의 인코더/디코더 계층이 아닌 6개의 계층을 사용함\n", + "# facebook/bart-large로 12개 계층 사용 모델을 불러올 수 있음.\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)\n", + "\n", + "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(\"L\",sub_name)\n", + " for ssub_name, ssub_module in sub_module.named_children():\n", + " print(\"| L\", ssub_name)\n", + " for sssub_name, sssub_module in ssub_module.named_children():\n", + " print(\"| | L\", sssub_name)" + ], + "metadata": { + "id": "r-gUwzif3Be2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "941d8a8d-758e-4cc2-cc0c-e1feb0847dbc" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "model\n", + "L shared\n", + "L encoder\n", + "| L embed_tokens\n", + "| L embed_positions\n", + "| L layers\n", + "| | L 0\n", + "| | L 1\n", + "| | L 2\n", + "| | L 3\n", + "| | L 4\n", + "| | L 5\n", + "| L layernorm_embedding\n", + "L decoder\n", + "| L embed_tokens\n", + "| L embed_positions\n", + "| L layers\n", + "| | L 0\n", + "| | L 1\n", + "| | L 2\n", + "| | L 3\n", + "| | L 4\n", + "| | L 5\n", + "| L layernorm_embedding\n", + "lm_head\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "pip install evaluate rouge_score absl-py" + ], + "metadata": { + "id": "sqU96eBJ3mON", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c557da4f-94c3-4203-f2d4-52cd65b2938f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: evaluate in /usr/local/lib/python3.11/dist-packages (0.4.3)\n", + "Requirement already satisfied: rouge_score in /usr/local/lib/python3.11/dist-packages (0.1.2)\n", + "Requirement already satisfied: absl-py in /usr/local/lib/python3.11/dist-packages (1.4.0)\n", + "Requirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from evaluate) (3.6.0)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from evaluate) (2.0.2)\n", + "Requirement already satisfied: dill in /usr/local/lib/python3.11/dist-packages (from evaluate) 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/usr/local/lib/python3.11/dist-packages (from pandas->evaluate) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->evaluate) (2025.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->evaluate) (2025.2)\n", + "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=2021.05.0->evaluate) (2.6.1)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=2021.05.0->evaluate) (1.3.2)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=2021.05.0->evaluate) (25.3.0)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from 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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", + "\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", + " loss = outputs.loss\n", + " train_loss += loss.item()\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\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\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", + "\n", + "best_loss = 10000\n", + "epochs=1\n", + "for epoch in range(epochs):\n", + " train_loss = train(model, optimizer, train_dataloader)\n", + " val_loss, val_rouge = evaluation(model, valid_dataloader)\n", + " print(f\"Epoch {epoch+1}: Train Loss: {train_loss:.4f} Val Loss: {val_loss:.4f} Val Rouge {val_rouge:.4f}\")" + ], + "metadata": { + "id": "UEzbqkx3LZBE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# 7.22 BART 모델 평가\n", + "model = BartForConditionalGeneration.from_pretrained(\n", + " pretrained_model_name_or_path = \"facebook/bart-base\"\n", + ").to(device)\n", + "model.load_state_dict(torch.load(\"../models/BartsForConditionalGeneration.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": "PZ17pVPw9pYt" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# 7.23 문장 요약문 비교\n", + "\n", + "from transformers import pipeline\n", + "\n", + "summarizer = pipeline(\n", + " task = \"summarization\",\n", + " model = model,\n", + " tokenizer = tokenizer,\n", + " max_length=54,\n", + " device=\"cpu\"\n", + ")\n", + "\n", + "for index in range(5):\n", + " news_text = test.text.iloc[index]\n", + " summarization = test.prediction.iloc[index]\n", + " predicted_summarization = summarizer(news_text)[0][\"summary_text\"]\n", + " print(f\"정답 요약문 : {summarization}\")\n", + " print(f\"모델 요약문 : {predicted_summarizaition}\\n\")" + ], + "metadata": { + "id": "0giE1DGb_aR_" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "ELECTRA" + ], + "metadata": { + "id": "7R0L4LX9_30y" + } + }, + { + "cell_type": "code", + "source": [ + "# 7.24 ELECTRA 입력 텐서 생성\n", + "\n", + "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", + "# 토크나이저를 텐서 데이터세트로 반환\n", + "def make_dataset(data, tokenizer, device, max_length: int = 128):\n", + " tokenized = tokenizer(\n", + " text = data.text.tolist(),\n", + " padding = \"max_length\",\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", + "# 샘플러 클래스를 활용해 데이터를 목적에 따라 샘플링\n", + "def get_dataloader(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", + "epochs = 3\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_df, tokenizer, device)\n", + "train_dataloader = get_dataloader(train_dataset, RandomSampler, batch_size)\n", + "# RandomSampler: 데이터를 무작위로 샘플링 -> 학습에 적용\n", + "\n", + "valid_dataset = make_dataset(valid_df, tokenizer, device)\n", + "valid_dataloader = get_dataloader(valid_dataset, SequentialSampler, batch_size)\n", + "# SequentialSampler: 데이터를 고정된 순서로 반환 -> 검증 & 평가 배치에 적용\n", + "\n", + "test_dataset = make_dataset(test_df, tokenizer, device)\n", + "test_dataloader = get_dataloader(test_dataset, SequentialSampler, batch_size)\n", + "\n", + "print(train_dataset[0])" + ], + "metadata": { + "id": "to5--ARl_5WW", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "622884d6-73bc-4936-944c-a5b07543902d", + "collapsed": true + }, + "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": "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", + " 4200, 5, 5, 3, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0]), tensor(1))\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 7.25 KoELECTRA 모델 선언\n", + "from torch import optim\n", + "from transformers import ElectraForSequenceClassification\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)\n", + "\n", + "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(\"L\",sub_name)\n", + " for ssub_name, ssub_module in sub_module.named_children():\n", + " print(\"| L\", ssub_name)\n", + " for sssub_name, sssub_module in ssub_module.named_children():\n", + " print(\"| | L\", sssub_name)" + ], + "metadata": { + "id": "oh-V32hw8GXH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6d222167-63a9-4f27-ef83-15ecf854ede6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at monologg/koelectra-base-v3-discriminator and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "electra\n", + "L embeddings\n", + "| L word_embeddings\n", + "| L position_embeddings\n", + "| L token_type_embeddings\n", + "| L LayerNorm\n", + "| L dropout\n", + "L encoder\n", + "| L layer\n", + "| | L 0\n", + "| | L 1\n", + "| | L 2\n", + "| | L 3\n", + "| | L 4\n", + "| | L 5\n", + "| | L 6\n", + "| | L 7\n", + "| | L 8\n", + "| | L 9\n", + "| | L 10\n", + "| | L 11\n", + "classifier\n", + "L dense\n", + "L activation\n", + "L dropout\n", + "L out_proj\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 모델 학습 및 검증\n", + "import numpy as np\n", + "from torch import nn\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(\n", + " input_ids=input_ids,\n", + " attention_mask = attention_mask,\n", + " labels = labels\n", + " )\n", + " loss = outputs.loss\n", + " train_loss += loss.item()\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\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(\n", + " input_ids = input_ids,\n", + " attention_mask = attention_mask,\n", + " labels = labels\n", + " )\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\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", + "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", + " torch.save(model.state_dict(),\"../models/ELECTRAForSequenceClassification.pt\")\n", + " print(\"Saved the model weights\")" + ], + "metadata": { + "id": "E28F2zxqLNpI" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# 모델 평가\n", + "model = BertForSequenceClassification.from_pretrained(\n", + " pretrained_model_name_or_path=\"monologg/koelectra-base-v3-discriminator\",\n", + " num_labels=2\n", + ").to(device)\n", + "\n", + "model.load_state_dict(torch.load(\"../models/ELECTRAForSequenceClassification.pt\"))\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": "_uoSPP8a8wKk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "T5" + ], + "metadata": { + "id": "w3MHE7gZ8xAZ" + } + }, + { + "cell_type": "code", + "source": [ + "# 7.26\n", + "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[\"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", + "print(f\"Source News: {train.text.iloc[0]['text']}\")\n", + "print(f\"Summarization: {train.prediction.iloc[0][:50]}\")" + ], + "metadata": { + "id": "S1o6K0PJLRJv" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# 7.27 뉴스 요약 데이터세트 전처리\n", + "\n", + "import torch\n", + "from transformers import T5Tokenizer\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "from torch.utils.data import RandomSampler, SequentialSampler\n", + "\n", + "# 토크나이저를 텐서 데이터세트로 반환\n", + "def make_dataset(data, tokenizer, device):\n", + " tokenized = 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", + " 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", + " # 요약 작업은 입/출력값 문장 길이가 다르기 때문에 padding을 사용\n", + " # CrossEntropy와 같은 손실함수에서 패딩된 토큰을 무시하게 하기 위해 -100값 사용\n", + "\n", + "# 샘플러 클래스를 활용해 데이터를 목적에 따라 샘플링\n", + "def get_dataloader(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", + "epochs = 3\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_dataloader(train_dataset, RandomSampler, batch_size)\n", + "# RandomSampler: 데이터를 무작위로 샘플링 -> 학습에 적용\n", + "\n", + "valid_dataset = make_dataset(valid, tokenizer, device)\n", + "valid_dataloader = get_dataloader(valid_dataset, SequentialSampler, batch_size)\n", + "# SequentialSampler: 데이터를 고정된 순서로 반환 -> 검증 & 평가 배치에 적용\n", + "\n", + "test_dataset = make_dataset(test, tokenizer, device)\n", + "test_dataloader = get_dataloader(test_dataset, SequentialSampler, batch_size)\n", + "\n", + "print(train_dataset[0])" + ], + "metadata": { + "id": "w7P4jjGg-a1E" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# 7.28 모델 선언\n", + "from torch import optim\n", + "from transformers import T5ForConditionalGeneration\n", + "\n", + "model = T5ForConditionalGeneration.from_pretrained(\n", + " pretrained_model_name_or_path = \"t5-small\",\n", + ").to(device)\n", + "optimizer = optim.AdamW(model.parameters(), lre-5, eps=1e-8)" + ], + "metadata": { + "id": "3piJbjeLFD7M" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# 7.29 모델 학습 및 평가\n", + "import numpy as np\n", + "from torch import nn\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_inoput_ids = target_ids[:,1:].clone().detach()\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", + "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\n", + "\n", + " val_loss = val_loss/len(dataloader)\n", + " return val_loss\n", + "\n", + "best_loss=10000\n", + "for epcoh in (epochs):\n", + " train_loss = train(model, optimizer, train_dataloader)\n", + " val_loss = evalutaion(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": "zXACmShoFfdR" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git 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