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[ + "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (4.57.3)\n", + "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.12/dist-packages (1.2.3)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers) (3.20.0)\n", + "Collecting huggingface-hub\n", + " Downloading huggingface_hub-0.36.0-py3-none-any.whl.metadata (14 kB)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2.0.2)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (25.0)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers) (6.0.3)\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: requests in 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"output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "huggingface_hub" + ] + }, + "id": "65d8cfa3af8243a6951d00dcc2836d7b" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install --upgrade --quiet datasets fsspec huggingface_hub" + ], + "metadata": { + "id": "2eSXxqg4a9SF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a1582127-3415-4bf7-b468-c8d20a998530" + }, + "id": "2eSXxqg4a9SF", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/512.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m512.3/512.3 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[?25l 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This behaviour is the source of the following dependency conflicts.\n", + "transformers 4.57.3 requires huggingface-hub<1.0,>=0.34.0, but you have huggingface-hub 1.2.3 which is incompatible.\n", + "gcsfs 2025.3.0 requires fsspec==2025.3.0, but you have fsspec 2025.10.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ] + }, + { + "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": "f131cdef-c18a-427a-ad02-6eb4e16a6525" + }, + "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": 722, + "referenced_widgets": [ + "84ae3a4958464306bc59730cd1688683", + "10f96d56c56b4f138c509a40306aa3ce", + "70fd9e801daa490ea919ec971a3885cb", + "b7d0758189d046cbb4b9da4f70ec4b12", + "c25b52a5f9f94998bcbbac4ebd7a4f24", + 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"da5eb5154c6f4c8bbc21e67c5a6c2f6f", + "d21077696f6c4a4d8d6d64ecc9c86f1a", + "43e082f6854f45e990927ab9c98fe3b7", + "3f226a8efc664b18b88616312ac010b6", + "e0c8b64cd26945c789b989b116b6a49f", + "021cb68f12b84e139364eb43102ed3f6", + "cb1d9d2a699d49108d9591831fccbba7", + "e108cafa40414e62a676a8dac3c980af", + "f9272ceeaaaf4a3cbb1cc74dbde87ba7", + "eec43a75714f4a36a2475177e7d2fdbf", + "3f46936e4bb143619c231914fa172f68", + "243834476db74beb89dcc7ca26bc6c9a", + "c9b3ba15af1c496fa2376d2ccb004d71", + "4e662763c03e416eb2745b98aff36bd7", + "1ec2502a0a584e4581d0e398f21e2d67", + "5fbfba3bd4f8497f9e736adc3fde378d", + "41ecb12728274e6ab418f6a92c010e95", + "a7a97bc3400f4f829949d60151d31627", + "e0233bdcca09446ba1b1c796fb441f6a", + "a94e58987dc54e45bdafc5ab65ab40ff", + "91bf1bce5e25440694cd84de38566afa", + "727b4b36ff55442582bea7e792a26145", + "772764c292e845569ae15a147067d1a8", + "2e529cb5cdb646a88d7e47128b32bd2a", + "9cb0939e18cb4e1c818ccd2d7f4ec131", + "583e7e1537fa48e8a210d82dec18d94b", + "d196328dfd9d4415bd763469e5dcadf9", + "8c26ece4cbbf40f3855f291e6035005a", + "3fc1dce718244d7ba49d1d56369bf1f6", + "ef73c6b8768c4413beb9afbb9169f3b1", + "0e59aac685b24ad693dd85f35f2994a5", + "5852b1b2449f4f9c92e67b1f166cfbcb", + "d3ae996d6f90406e827f9641ea43a25f", + "5b1e6c3586ca41409268c59bffc16599", + "1935888514b84d909c65060bb50f54a6", + "aa9cc5f5d5c24924ba30495a64f942d8", + "1f33d4a5a9d6423383b0c4a8fd64b351", + "bdf431c6cecd47f09b766db8f2f4d31f", + "584c6a9fccb64766a6a273a614bfa4eb", + "e789bd8f2ca8475bb007e3237dfadffd", + "bbf6b75aa56541e6998ebb0e44f174eb", + "7fc7189f1e4e4e4aa889f15e87675333", + "ed48cd51d5724ef28528437dc014138c", + "56aab5711f914bf2b37860229647b084", + "56b205db6ed440019bad8a9c6c83ec70", + "9fd63efec0f8410bbfd2c249d29eca40" + ] + }, + "id": "QTVKkGiIflzk", + "outputId": "539f8a5a-eec4-4966-dc16-edb7af368483" + }, + "id": "QTVKkGiIflzk", + "execution_count": null, + "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": "84ae3a4958464306bc59730cd1688683" + } + }, + "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" + ] + } + ] + }, + { + "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)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sFTvc6ex6zWn", + "outputId": "46ffa49e-1e3f-402e-d3d4-8bf99a2217bb" + }, + "execution_count": 2, + "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, 75.5MB/s] \n", + "[nsmc] download ratings_test.txt: 4.90MB [00:00, 41.8MB/s] \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.16 BERT 입력 텐서 생성" + ], + "metadata": { + "id": "sECcG4zp3P7e" + } + }, + { + "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": "xRlVZYa_6zuY", + "outputId": "541b647c-ac5c-4476-8d20-4a98b590aa3d" + }, + "execution_count": 3, + "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_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", + "\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_dataloader(train_dataset, RandomSampler, batch_size)\n", + "\n", + "valid_dataset = make_dataset(valid, tokenizer, device)\n", + "valid_dataloader = get_dataloader(valid_dataset, SequentialSampler, batch_size)\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])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 655, + "referenced_widgets": [ + "a9cb4cd213194d72be3c49633b533311", + 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"4204d02215ca4424bf967b814326a747", + "9cdaa2305c0544e78b77d28716d5d2fc", + "592e26208b29495ca75b2577e5f7b23a", + "a1813c9c50de4b938c881ab553435cec", + "1039cc58741d45828ccd03368c031112", + "89740a0b07114d38ae44b0d01f58d135", + "4b07f5765e804a07a02a131052191bd9", + "138af002f4ea4e07a456d4cef3e8627a", + "44649e2d356140209f447569edc5b6a3", + "99716a65ee514fb0ae47493e93ced50c", + "0b1dd749dd114783ab6cdc44b2a81521", + "ad8c65d591dd4cbf9bf0a6681b200427", + "d8a150e6e4c44c89a79f773d2a12544e", + "bcd5d9f8c77344d08befd40a2f5650de", + "bea7bdc3051346bf8bc006986749127a", + "ab59e6b78269450b8665d4ff19b5db98", + "81b81fc072ee4279baf3a400d8846178" + ] + }, + "id": "KwUr18u67CaM", + "outputId": "eba26d46-6ce0-48d9-8b57-c1e3480319eb" + }, + "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": [ + "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=617c2e1ea02871d782ddf14fa9ec9c1780374562c06f3a9b42a031bdab859813\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_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", + "\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_df)}\")\n", + "print(f\"Testing Data Size : {len(test_df)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fv57MMjd7lEb", + "outputId": "bdac2bce-3b74-4572-d92d-8be316eb04ba" + }, + "execution_count": 21, + "outputs": [ + { + "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": "markdown", + "source": [ + "## 예제 7.19 BART 입력 텐서 생성" + ], + "metadata": { + "id": "1Xmv-pw63P7f" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import BartTokenizer\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", + "import torch.nn as nn\n", + "import numpy as np\n", + "import pandas as pd\n", + "from torch.nn.utils.rnn import pad_sequence\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", + " labels=[]\n", + " input_ids = tokenized[\"input_ids\"]\n", + " attention_mask = tokenized[\"attention_mask\"]\n", + "\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)\n", + " return TensorDataset(input_ids, attention_mask, labels)\n", + "\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 = 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_df, tokenizer, device)\n", + "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True)\n", + "\n", + "valid_dataset = make_dataset(valid_df, tokenizer, device)\n", + "valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True)\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": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9k4tiREA7qmX", + "outputId": "6d0bc6fd-7417-4fc0-b0e3-664c394b599a" + }, + "execution_count": 34, + "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": [ + "print(type((train_df)))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lvamlyHvK1Ih", + "outputId": "720dd3cb-b715-40d1-bece-0464148601ba" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.20 BART 모델 선언" + ], + "metadata": { + "id": "47hwau8t3P7f" + } + }, + { + "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", + ")\n", + "optimizer = optim.AdamW(model.parameters(), lr=5e-5, eps=1e-8)" + ], + "metadata": { + "id": "BPg5N3A27uUC" + }, + "execution_count": 35, + "outputs": [] + }, + { + "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": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-DVcnCCq7wzy", + "outputId": "378932b2-664a-44f7-d96d-cf37f7fe7c86" + }, + "execution_count": 36, + "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": "markdown", + "source": [ + "## 예제 7.21 BART 모델 학습 및 평가" + ], + "metadata": { + "id": "25_LnKOO3P7f" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "os.environ[\"CUDA_LAUNCH_BLOCKING\"] = \"1\"\n" + ], + "metadata": { + "id": "vbPfmwfKIyZ2" + }, + "execution_count": 37, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import evaluate\n", + "\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.to(device,non_blocking = True),\n", + " attention_mask=attention_mask.to(device,non_blocking = True),\n", + " labels=labels.to(device, non_blocking = True)\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.to(device),\n", + " attention_mask=attention_mask.to(device),\n", + " labels=labels.to(device)\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", + "\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", + " torch.save(model.state_dict(), \"/content/drive/MyDrive/Euron_9thDL/pytorch_transformer/models/BartForConditionalGeneration.pt\")\n", + " print(\"Saved the model weights\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 532 + }, + "id": "6bRcNs9172nT", + "outputId": "cf5991c6-2585-484b-b3f9-dfd1e9b05d06" + }, + "execution_count": 38, + "outputs": [ + { + "output_type": "error", + "ename": "AcceleratorError", + "evalue": "CUDA error: device-side assert triggered\nSearch for `cudaErrorAssert' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAcceleratorError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipython-input-1559395447.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mbest_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0mtrain_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 69\u001b[0m \u001b[0mval_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Epoch {epoch + 1}: Train Loss: {train_loss:.4f} Val Loss: {val_loss:.4f} Val Rouge {val_accuracy:.4f}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tmp/ipython-input-1559395447.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, optimizer, dataloader)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mtrain_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdataloader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m outputs = model(\n\u001b[1;32m 24\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnon_blocking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 730\u001b[0m \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 731\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 732\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 733\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 734\u001b[0m if (\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 788\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 789\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 790\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 791\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 792\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/_utils/pin_memory.py\u001b[0m in \u001b[0;36mpin_memory\u001b[0;34m(data, device)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0mclone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m \u001b[0mclone\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 94\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mclone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msample\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/_utils/pin_memory.py\u001b[0m in \u001b[0;36mpin_memory\u001b[0;34m(data, device)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 58\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAcceleratorError\u001b[0m: CUDA error: device-side assert triggered\nSearch for `cudaErrorAssert' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.22 BART 모델 평가" + ], + "metadata": { + "id": "oDL5nymo3P7g" + } + }, + { + "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(\"/content/drive/MyDrive/Euron_9thDL/pytorch_transformer/models/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": "wJflYAAp77KK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.23 문장 요약문 비교" + ], + "metadata": { + "id": "ce3vaMq03P7g" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import pipeline\n", + "\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_summarization}\\n\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "id": "t9GZzt6b75b8", + "outputId": "02e63309-d7ad-45de-9510-e3a5be534377" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "name 'model' is not defined", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipython-input-1167495881.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m summarizer = pipeline(\n\u001b[1;32m 5\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"summarization\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m54\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.24 네이버 영화 리뷰 데이터셋 전처리" + ], + "metadata": { + "id": "1sRTyoDh3P7g" + } + }, + { + "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_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", + "\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_df, tokenizer, device)\n", + "train_dataloader = get_dataloader(train_dataset, RandomSampler, batch_size)\n", + "\n", + "valid_dataset = make_dataset(valid_df, tokenizer, device)\n", + "valid_dataloader = get_dataloader(valid_dataset, SequentialSampler, batch_size)\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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "id": "1btqz9Th8jjj", + "outputId": "1d652d30-574b-4f72-a51e-03d957a18a2a" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "name 'train_df' is not defined", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipython-input-999088080.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 32\u001b[0m )\n\u001b[1;32m 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0mtrain_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 35\u001b[0m \u001b[0mtrain_dataloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_dataloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRandomSampler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'train_df' is not defined" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.25 KoELECTRA 모델 선언" + ], + "metadata": { + "id": "y0i5RDP_3P7g" + } + }, + { + "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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139, + "referenced_widgets": [ + "227d933406344c6fab8047455b014053", + "070e32cee5ab4da089bffb176f39b250", + "61f1f22ec3164cbe89f5e6cd699bdf09", + "efbb982e2d9f40968cb05a95a5539126", + "5578005322644467b6203ebc7e21695b", + "4d9cda4d855c428b82be678128e8760a", + "67c9b4a6034f453bb96236b446c6e16f", + "0e01350368894018aa946739151089a9", + "9594d8cb27bb4b769288ad71429d29ba", + "d8adc2e441014dc4b6a6bf11324fc7e7", + "0a2f6862de714c2aa916a9cbc0149b79", + "d8601892c7034085a3657858ed71a9da", + "2b332d23e82a47ff8ae6bf84578d70df", + "132b21eec89c4b708cbbd6fa4e7002b1", + "c09d98ca58bb4c1d93c353a72717c84e", + "8136c43b184d4c078d0ffbe5385d3666", + "f6a1d641b16d45ccbe2c5941fcd52217", + "b06a0112dc9047c8b219275b1d332bd0", + "d01f223a32714aa2af6aff4845028bd0", + "313eb555218e4376ab401696ab429664", + "0ebed6aafe7f4b858166299fe97ba58e", + "1948424d047940f99da5f7165a1b6646" + ] + }, + "id": "3VfdoMeq8pw7", + "outputId": "cf1b951e-8ef0-4987-b18f-1a35174b63e9" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "pytorch_model.bin: 0%| | 0.00/452M [00:00\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mbest_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrain_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mval_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Epoch {epoch + 1}: Train Loss: {train_loss:.4f} Val Loss: {val_loss:.4f} Val Accuracy {val_accuracy:.4f}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: 'DataFrame' object is not callable" + ] + } + ] + }, + { + "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(\"/content/drive/MyDrive/Euron_9thDL/pytorch_transformer/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": "FoMA2SJ88790" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.26 뉴스 요약 데이터셋 불러오기" + ], + "metadata": { + "id": "wbD7upNJrDdz" + } + }, + { + "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": "g66IQvQ78_LE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.27 뉴스 요약 데이터셋 전처리" + ], + "metadata": { + "id": "zNBnM1hBrDgo" + } + }, + { + "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))\n" + ], + "metadata": { + "id": "aCcUu9TL9JLH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "ㄴ 이 예제는 출력창이 무한로딩되더니 삭제 안하면 코랩 탭이 멈춰버려서 그냥 출력창을 지웠습니다..." + ], + "metadata": { + "id": "mB8buCddCK0W" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.28 T5 모델 선언" + ], + "metadata": { + "id": "SakG3-owrDjx" + } + }, + { + "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": "0BrcmYFb9MRH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.29 T5 모델 학습 및 평가" + ], + "metadata": { + "id": "6JZjK5uJrDmy" + } + }, + { + "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(), \"/content/drive/MyDrive/Euron_9thDL/pytorch_transformer/models/T5ForConditionalGeneration.pt\")\n", + " print(\"Saved the model weights\")\n" + ], + "metadata": { + "id": "WFrb3-Ft9VBo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 예제 7.30 T5 생성 모델 테스트" + ], + "metadata": { + "id": "SELgZP4UrDpv" + } + }, + { + "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": "zQX1yIge9b5_" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git "a/a0272f00-b931-405f-acde-c18a677c036b_Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\354\236\245\354\204\234\354\227\260.pdf" "b/a0272f00-b931-405f-acde-c18a677c036b_Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\354\236\245\354\204\234\354\227\260.pdf" new file mode 100644 index 0000000..a6c2c6c Binary files /dev/null and "b/a0272f00-b931-405f-acde-c18a677c036b_Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\354\236\245\354\204\234\354\227\260.pdf" differ