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이번 복습과제에서는 SST-2 데이터셋을 기반으로 BERT와 ELECTRA 모델을 학습시켜보고 성능과 구조의 차이를 알아보겠습니다.\n", + "- 코드 실행시간이 매우 길 수 있습니다.\n", + " - 최대한 끝까지 실행해보시되, 시간 부족으로 인해 중간에 중지하신 실행 결과를 제출하셔도 괜찮습니다.\n", + " - 제출 이후에는 꼭 끝까지 실행시켜 비교해보시기 바랍니다!" + ], + "metadata": { + "id": "4644be45" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install --upgrade --quiet datasets fsspec huggingface_hub" + ], + "metadata": { + "id": "2eSXxqg4a9SF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f7005b3b-8053-4166-c476-9022fc070f52" + }, + "id": "2eSXxqg4a9SF", + "execution_count": 1, + "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[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m512.0/512.3 kB\u001b[0m \u001b[31m24.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m512.3/512.3 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/201.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.0/201.0 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.0/521.0 kB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.7/47.7 MB\u001b[0m \u001b[31m18.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\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", + "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": 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": "408e9944-68bb-470d-e467-1b6490eabaf5" + }, + "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": 588, + "referenced_widgets": [ + "2a84cc8fe6094284be967e2a38a66241", + "9c00cbcb035f41d38b39928ecf1daabb", + "b79e1dfd20744ad29aea1ed891833869", + "1165d66b57cd421c9cec068439f37ea0", + "2a354d8025b4432b811fb9c2b1b8dd51", + "82ebda6905e24b99b6e9346255472d54", + "0018defb3e604d45bb72f28d449dc499", + "f1d2ca3cc29f475ebdd72163073cc94b", + "5615348624d64be8b2f70fcc0aa67a60", + "abc1899a5ba2439bbfc4d6a0a413bbdf", + "c05ff08a4310448c8a892b3d290ad980", + "cf561c1b68f54fa9a3c12ee283c4ad14", + "5673949cc4da4b50af754c02db291be2", + "49ff4bd942a045738e53c9df85394ba2", + "848cee1025494f0cbae81b8f3a4cd21c", + "81ca9acbd5434f14a9bde5064ba1fed6", + "74471c81da044454ba3bfeda6a273115", + "d0fe7160bbf6493dabd2616f5dd3f508", + "edd5c259d16b4199b2ca6af5473900f7", + "663bcf009faa4af0933f4642a65eb789", + "ddd53604d63240acb5709ef93bbadf86", + "b88838aa8bd54441ba0a7a9777ebf7f4", + "2ff9973b3ae34cb29fec0f48f7798f90", + "261a7854a7b2467b8e56573090615dcd", + "16598d5fce9f4b13a2c9c3078f9590d5", + "75366ab7a1fd44119f535edd5ac2961a", + "3cbee8a6be754eae9cc8efcf28780c47", + "1e19739b21204f499ba6915b19d2a07b", + "84a32c360fd8463e944a8b4796ac6842", + "bca5182653f2405496535039cdf32ed2", + "774fa86f874d41aca7744ab5e44f0300", + "c8345ee0cab3436ba9ecb0771b64bfb1", + "d737fd063023401e98f480dc47e12f98", + "59a6af7ed18a4410af44546a81c575e7", + "58d8fc12453045348b8db2e2a223e0cd", + "b80f78cc1a2c4a30865d6c11323dc1fd", + "78206cc971904cecbf2276e40adb003a", + "1d4ca206c5f64974b200a78fcdd7cd1e", + "31ed4d62bbe344fc85ce5026d7268c97", + "3216477ddc6c4314809effe6b3e3c231", + "34953047448f4b0a80df89ab2f364332", + "40dbb80afb784de89b5f005d497df5c9", + "2623b1831d3c49bea4f833062c7c7361", + "1167c5666df74d949404c6dbbc2e0c4b", + "f6a19968fcc14c3fb59305d4fd8d9318", + "85c14bfb972143fd8d7521845fe8d4a3", + "490ad7850dc04e1b9b17798168af7191", + "105e09e1bfaa45b88631ec3a9f00d9b4", + "10b29111d7ab4eb686005b48be1912cb", + "f92bae1bca3a4d58949b1cb88f68d7ce", + "659a49758fdd4ad6b6ad9861f3eaef4b", + "dfd71b51c3704330b637a3f1e8dadcde", + "3b4a5a2cc0354b5ea7bcc2356829cad6", + "5e52d7668b5e424cb852b50b0ea738e3", + "c9bd1245139643b8b798208e3be7f28c", + "7ede68417940478e89401e78d1c5f5ca", + "9354200bf3a44015b16bb77ceab2b914", + "1339d755ef944791b370dc2ee5eda438", + "03dc71ad6a414ba6966106d3eea23b03", + "6290ca87258c48c89770cf2d92fc2882", + "2e7f3432757e4777877fc8cdd5559ec3", + "d29a6aa3a40542d3a00c9e0e8ae290c7", + "b5910ceb651c4840a386274b0f048adc", + "a95353ef5e9a47f99776543427455d66", + "fbd13d51d6a84e1f95a46b03a505a224", + "8648d38eab9143059a302d7e80aaa38c", + "2135ef76405749ec9d4b67ac8b85aab0", + "00be7195e4cc472c8907ad397c57150f", + "71399cdec49444dd96bc8eb8e9312dd4", + "8be139c171424874be4cc966b388e560", + "d5b0896013b8407fb9eaf371208bda91", + "763976f7b8be4128bd1a1e56b6477dbc", + "18e20adf575d493880911bcee7488b53", + "c669015e518d435e83d67132049aadce", + "42b5069df9bd42a4a5797a6c6233e13b", + "dcbe0a76f477496a85b8222d17987aad", + "447b667f397e415bb560e236735775b8" + ] + }, + "id": "QTVKkGiIflzk", + "outputId": "63fc793e-6601-438c-e58b-7ddac9782a0a" + }, + "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": "2a84cc8fe6094284be967e2a38a66241" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "data/train-00000-of-00001.parquet: 0%| | 0.00/3.11M [00:00=0.6 (from Korpora)\n", + " 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"colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "09qfJ-lFgjrV", + "outputId": "2ebf537d-98b6-4d57-a9e3-4d21e33597a1" + }, + "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 [00:01, 11.7MB/s] \n", + "[nsmc] download ratings_test.txt: 4.90MB [00:00, 154MB/s]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from Korpora import Korpora\n", + "\n", + "\n", + "corpus = Korpora.load(\"nsmc\")\n", + "df = pd.DataFrame(corpus.test).sample(20000, random_state=42)" + ] + }, + { + "cell_type": "code", + "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": "_NPH5VQthWqi", + "outputId": "d2ad579b-a9e5-41e6-d1d0-c3c2ea51852f" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "| | text | label |\n", + "|------:|:---------------------------------------------------------|--------:|\n", + "| 26891 | 역시 코믹액션은 성룡, 홍금보, 원표 삼인방이 최고지!! | 1 |\n", + "| 25024 | 점수 후하게 줘야것네 별 반개~ | 0 |\n", + "| 11666 | 오랜만에 느낄수 있는 [감독] 구타욕구. | 0 |\n", + "| 40303 | 본지는 좀 됬지만 극장서 돈주고 본게 아직까지 아까운 영화 | 0 |\n", + "| 18010 | 징키스칸이란 소재를 가지고 이것밖에 못만드냐 | 0 |\n", + "Training Data Size : 12000\n", + "Validation Data Size : 4000\n", + "Testing Data Size : 4000\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + " return bound(*args, **kwds)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from transformers import BertTokenizer\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "from torch.utils.data import RandomSampler, SequentialSampler\n", + "\n", + "\n", + "def make_dataset(data, tokenizer, device):\n", + " tokenized = tokenizer(\n", + " text=data.text.tolist(),\n", + " padding=\"longest\",\n", + " truncation=True,\n", + " return_tensors=\"pt\"\n", + " )\n", + " input_ids = tokenized[\"input_ids\"].to(device)\n", + " attention_mask = tokenized[\"attention_mask\"].to(device)\n", + " labels = torch.tensor(data.label.values, dtype=torch.long).to(device)\n", + " return TensorDataset(input_ids, attention_mask, labels)\n", + "\n", + "\n", + "def get_datalodader(dataset, sampler, batch_size):\n", + " data_sampler = sampler(dataset)\n", + " dataloader = DataLoader(dataset, sampler=data_sampler, batch_size=batch_size)\n", + " return dataloader\n", + "\n", + "\n", + "epochs = 5\n", + "batch_size = 32\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "tokenizer = BertTokenizer.from_pretrained(\n", + " pretrained_model_name_or_path=\"bert-base-multilingual-cased\",\n", + " do_lower_case=False\n", + ")\n", + "\n", + "train_dataset = make_dataset(train, tokenizer, device)\n", + "train_dataloader = get_datalodader(train_dataset, RandomSampler, batch_size)\n", + "\n", + "valid_dataset = make_dataset(valid, tokenizer, device)\n", + "valid_dataloader = get_datalodader(valid_dataset, SequentialSampler, batch_size)\n", + "\n", + "test_dataset = make_dataset(test, tokenizer, device)\n", + "test_dataloader = get_datalodader(test_dataset, SequentialSampler, batch_size)\n", + "\n", + "print(train_dataset[0])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 596, + "referenced_widgets": [ + "196ec0079ed1489b84e26c1ee86ab28a", + 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"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/49.0 [00:00=2.0.0 in /usr/local/lib/python3.12/dist-packages (from evaluate) (4.0.0)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from evaluate) (2.0.2)\n", + "Requirement already satisfied: dill in /usr/local/lib/python3.12/dist-packages (from evaluate) (0.3.8)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (from evaluate) (2.2.2)\n", + "Requirement already satisfied: requests>=2.19.0 in 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sha256=5e6bdc3ae32fadb77c7b9f99cf5309eee8ba61caf2dae43daaa5faca252cf7c0\n", + " Stored in directory: /root/.cache/pip/wheels/85/9d/af/01feefbe7d55ef5468796f0c68225b6788e85d9d0a281e7a70\n", + "Successfully built rouge_score\n", + "Installing collected packages: rouge_score, evaluate\n", + "Successfully installed evaluate-0.4.6 rouge_score-0.1.2\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from datasets import load_dataset\n", + "\n", + "\n", + "news = load_dataset(\"argilla/news-summary\", split=\"test\")\n", + "df = news.to_pandas().sample(5000, random_state=42)[[\"text\", \"prediction\"]]\n", + "df[\"prediction\"] = df[\"prediction\"].map(lambda x: x[0][\"text\"])\n", + "train, valid, test = np.split(\n", + " df.sample(frac=1, random_state=42), [int(0.6 * len(df)), int(0.8 * len(df))]\n", + ")\n", + "\n", + "print(f\"Source News : {train.text.iloc[0][:200]}\")\n", + "print(f\"Summarization : {train.prediction.iloc[0][:50]}\")\n", + "print(f\"Training Data Size : {len(train)}\")\n", + "print(f\"Validation Data Size : {len(valid)}\")\n", + "print(f\"Testing Data Size : {len(test)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 318, + "referenced_widgets": [ + "95123cc8bdbd42b5987b7af1023ef822", + "0376e477d260408fbbc50f6c19a74187", + "6bbbccc064324194b9a4f1eb72018ec2", + "c53ca509a4bf4c5a923520b271744db4", + "f8bf5cbe261a4da0999fc00a48e97f31", + "48f3de5c8d504c83bd56df17a200a0e8", + "8344c2e84abf44d589542c430d3d8616", + "d7232a76cdae42138c83d978958c3786", + "84d37e799bd34af88bf2f7ec602a8e9c", + "5e687a5a6eda44aca9b1c4db66a4e25a", + "22c1abe494ce45ba9df893d1c580d124", + "4f36e6f6028d4dd8984740c74083e2bf", + "ecfd340e413042e6bf7f1c498dca8672", + "3d6c1f2297bc4840bf684fd82b33de3b", + "c64635e99fec4875a3a9c81b4ab0c701", + "7be050adba2e43f389bb25ba2d1df545", + "4b856081d0364f9d979a8f493ae6f7a4", + "6a6750539a8c4b7aa84a88ac27f357e4", + "859d5deaaf97469e8a49164906919117", + 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