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pip in /usr/local/lib/python3.12/dist-packages (24.1.2)\n", + "Collecting pip\n", + " Downloading pip-25.3-py3-none-any.whl.metadata (4.7 kB)\n", + "Downloading pip-25.3-py3-none-any.whl (1.8 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m40.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: pip\n", + " Attempting uninstall: pip\n", + " Found existing installation: pip 24.1.2\n", + " Uninstalling pip-24.1.2:\n", + " Successfully uninstalled pip-24.1.2\n", + "Successfully installed pip-25.3\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install transformers -U" + ], + "metadata": { + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 597 + }, + "id": "z9rRXZuI4_uj", + "outputId": "b9cf2590-94ec-41bc-bf8e-b58df86f3ab6" + }, + "id": "z9rRXZuI4_uj", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (4.57.3)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers) (3.20.0)\n", + "Collecting huggingface-hub<1.0,>=0.34.0 (from transformers)\n", + " Using cached 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 /usr/local/lib/python3.12/dist-packages (from transformers) (2.32.4)\n", + "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.1)\n", + "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.7.0)\n", + "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers) (4.67.1)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (2025.10.0)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (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,>=0.34.0->transformers) (1.2.0)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (3.4.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (3.11)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (2.5.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (2025.11.12)\n", + "Using cached huggingface_hub-0.36.0-py3-none-any.whl (566 kB)\n", + "Installing 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": "e3ba384d4fa14c36b9f600a45284bc01" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install --upgrade --quiet datasets fsspec huggingface_hub" + ], + "metadata": { + "id": "2eSXxqg4a9SF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1406dcd9-cfaa-4ffc-ca83-b658a25e7c15" + }, + "id": "2eSXxqg4a9SF", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\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.\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": "d380b73e-7f65-4e55-83f1-de97b2b430bf" + }, + "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": 628, + "referenced_widgets": [ + "c192caed77174832a676da759c250cf1", + "ce9c83c00a7341a0a881d80f9a619cca", + "f36682d4c79841088e3827473a2c3395", + "6372d66246b744088a0444bd36d3c354", + "3e78d6dc69604c9394be5737075c5bfb", + "4e5e620d61d54ed38a449f73b7f48eb4", + "08305e89e6df4a8fb0731914b36528df", + "9e595622cfdf4d0d97220687aba6619f", + "5513bdf7ac3e4f51a0e737aa5d52cf53", + "869a4ed59e4c4bc28502b85cda69f731", + "f77165f72e2241c1a492d6537122d8be", + "99d0d28ec2594a3a842a1170868711ab", + "a010a039842a47b7bd86a10117213328", + "e1153bf6c98e49a9a38111a6b3591d65", + "d75cf6c523844a55ad72854eaf331b3c", + "72a65c294a8749478c9af9daff88004a", + "0ff39534730243f5971a8a82fcabe5f5", + "03a5d703ca2d439b815018f158c9c930", + "944495f6209c417bae879be78c3f7545", + "3e445e4532bc4e39b4e7ca09d91a5ee2", + "6b5b972d10834e3893ef26018cc98e78", + "db91463796fb42c39ea6dad2bfc868b9", + "d39b41fd60a749119c9caacd60f9fee6", + "102f4ed7bcf843a2a9cb1eb375fcc84c", + "4be439588de24138a5450b78251667a6", + "cbfb2ab6f9734876813f93630645bc73", + "99f7f28112c64eaca678f8067a1e1cf9", + "b643ee403e124c768a6186b0a55460ad", + "dc7ef1d912bd45ff88a87003976ee833", + "8004fdd37a344d53ab2108b125b427ff", + "048a35c769724755b8f7cbc36a9b4d9c", + "5ca7a6f0859840d6a913b44041d62d53", + "8f06c977525b460c9c9207d8f06ff7c9", + "6bf958c882934c43a133de37c8adf7b3", + "7f138c48b914451d89fddb4c0ddaa19a", + "fc164341c01744d9b1db4ea897855130", + "6ee261a7c9ae4ceba82bf77eddcddb41", + "246a8343d50648afa9f102dd3d1bb64a", + "929cce1647b74a2b8cfd2cd62173f19b", + "958e842e3e7a472f8b59fb93f3f2732f", + "dc9554e940c0460080d66385e8e5833f", + "a0d29c81328143698f2c45a7278d9d0c", + "e116443dca8d4248a321ae074c17edf0", + "434a82cac05c4d8cb56253a1b0ff0772", + "19f6a5825f8c4923b303f8ee090d8c8a", + "69aa2a3e27bf4979875f9d2dd670213f", + "ecac183bc53b40568f305416a95b9c88", + "eb66b1db61ed449daa04be0f6db5a66c", + "5467806d10524d2a928a31fb89b69022", + "07d5ebe3b3d24655bf681757c2e45ccd", + "c30c822e59484570a8b462db6d84a45f", + "376ba25444b14743ad16fad931d9421f", + "8a4203b000d847d88f2777a1936b5022", + "04a8db1e1b4b4f96badde7ed5e2c07c2", + "9704e74592d043ed909b10f906c2ec8e", + "90a87b83a9694d7f9367b09e82bcf554", + "d16f917a1d2441929ce1563ded554534", + "148dce076878406781c749e125670229", + "88ed25f20f0741a79213f35240d18163", + "4ab071db5bf344f39da1221e98240b79", + "c7a8d3780d514d28b24585424e4a41b9", + "e0aaa1bd9d3f4ebda75674e51438817d", + "ac1d91fa030d46168768ee4c17421f30", + "bcc32e9545c34e4ca8df5e54d5f2007e", + "e29bdbc853064ea4a4fff62f9c5c06f9", + "17e35a816c44499fa9b0c4f3a5c27f2e", + "fd5e4c23cfa9468ca95a89b0c94ebf31", + "2cb0e4202c4b428fb21e6cc90d0ec220", + "3f900f3cb0de475888e19e65cf872be5", + "4e9bb852ea9b497eaf91f3cb9a9a8327", + "9e21233d072142439dfcc4a1041af41a", + "0cbe76282ac943cd9e40ac47c93c5450", + "23549b9931da46eea1b6ec9dbcd0001c", + "8379dcaebf674e899a3433a554641bb7", + "5659b3d20b2543888d83f373be6b2a06", + "a8130209821744e591527323ef82d7a7", + "1d0d2481f7314e8089bc515a94d80df9" + ] + }, + "id": "QTVKkGiIflzk", + "outputId": "0b5faf3f-7f60-461c-d971-91fbc1948fad" + }, + "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": "c192caed77174832a676da759c250cf1" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "data/train-00000-of-00001.parquet: 0%| | 0.00/3.11M [00:00 ELECTRA 모델이 더 적합하다.\n", + "- 최종 검증 정확도만 놓고 봤을 때 ELECTRA가 BERT보다 약 3.8%p 더 높은 성능을 보임.\n", + "- 두 모델 모두 걸린 시간은 약 24분으로 같은 시간이 걸리므로, 동일 훈련 시간 대비 성능이 더 좋은 ELECTRA 모델이 더 학습 효율성이 높으며 언어의 문맥적 의미를 파악하는 능력이 우수하다고 판단할 수 있다.\n", + "- 또한 학습 과정에서는 BERT의 Train Loss가 ELECTRA보다 근소하게 더 낮았음에도 실제 검증 정확도는 ELECTRA가 더 높다. 이는 BERT의 사전 학습 방식에서 기인한 한계로 해석할 수 있다.\n", + " - BERT는 사전 학습 때만 [MASK]라는 가짜 토큰을 보고, 지금처럼 실제 과제를 수행할 때는 이 토큰이 없어 사전 학습과 실제 추론 단계 간의 '불일치' 문제가 존재한다.\n", + " - 하지만 ELECTRA는 실제 토큰이 올바른지 여부를 판별하는 방식으로 학습되기 때문에, 사전 학습과 실제 태스크 간의 입력 분포 차이가 상대적으로 작다. 이런 특성으로 인해 ELECTRA가 실제 검증 단계에서 더 안정적이고 높은 성능을 보인 것으로 판단된다." + ], + "metadata": { + "id": "ZBW7DEtjsdXJ" + }, + "id": "ZBW7DEtjsdXJ" + } + ] +} \ No newline at end of file diff --git "a/Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\353\257\274\354\206\214\354\227\260.ipynb" "b/Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\353\257\274\354\206\214\354\227\260.ipynb" new file mode 100644 index 0000000..2447c1c --- /dev/null +++ "b/Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\353\257\274\354\206\214\354\227\260.ipynb" @@ -0,0 +1,1559 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3a416915-0e25-49b0-9b71-8990e2f3609b", + "metadata": { + "id": "3a416915-0e25-49b0-9b71-8990e2f3609b" + }, + "source": [ + "## BERT 모델" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install Korpora" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bBNyijBiRnDL", + "outputId": "4363d1ca-66b9-4d3f-a7b0-c3c8e5bb6f6c" + }, + "id": "bBNyijBiRnDL", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: Korpora in /usr/local/lib/python3.12/dist-packages (0.2.0)\n", + "Requirement already satisfied: dataclasses>=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", + "execution_count": 2, + "id": "c72d40aa-0286-4f2d-90f5-b0e03e1ef763", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c72d40aa-0286-4f2d-90f5-b0e03e1ef763", + "outputId": "b5fab8ad-0a07-4d97-8917-6f389bb4b034" + }, + "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", + "| | 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" + ] + } + ], + "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)\n", + "\n", + "train, valid, test = np.split(\n", + "\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)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "90d8f0b2-a6d5-4e24-a65f-d0f9b2b00b4d", + "metadata": { + "id": "90d8f0b2-a6d5-4e24-a65f-d0f9b2b00b4d" + }, + "source": [ + "### BERT 입력 텐서 생성" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dca9b019-a212-4b6c-8df8-adb55d0ad872", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dca9b019-a212-4b6c-8df8-adb55d0ad872", + "outputId": "4503954b-08f4-46e7-f771-67c0a143930e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "디바이스 설정: cuda\n", + "(tensor([ 101, 58466, 9812, 118956, 119122, 59095, 10892, 9434, 118888,\n", + " 117, 9992, 40032, 30005, 117, 9612, 37824, 9410, 12030,\n", + " 42337, 10739, 83491, 12508, 106, 106, 102, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0],\n", + " device='cuda:0'), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0], device='cuda:0'), tensor(1, device='cuda:0'))\n" + ] + } + ], + "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", + "if torch.cuda.is_available():\n", + " device = torch.device(\"cuda\")\n", + "else:\n", + " device = torch.device(\"cpu\")\n", + " print(\"디바이스 설정: cpu (CUDA를 사용할 수 없습니다)\")\n", + "print(\"디바이스 설정:\", device)\n", + "\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])" + ] + }, + { + "cell_type": "markdown", + "id": "80cc247f-dc12-4ff7-ab90-da9cfbbca4c1", + "metadata": { + "id": "80cc247f-dc12-4ff7-ab90-da9cfbbca4c1" + }, + "source": [ + "### BERT 모델 선언" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9ff7c3a2-2873-4f9b-b9a9-e0ba933b67fb", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9ff7c3a2-2873-4f9b-b9a9-e0ba933b67fb", + "outputId": "1a2ca2a0-d212-4408-b703-1f957639d8dc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + } + ], + "source": [ + "from torch import optim\n", + "from transformers import BertForSequenceClassification\n", + "\n", + "model = BertForSequenceClassification.from_pretrained(\n", + " pretrained_model_name_or_path=\"bert-base-multilingual-cased\",\n", + " num_labels=2\n", + ").to(device)\n", + "optimizer = optim.AdamW(model.parameters(), lr=1e-5, eps=1e-8)\n", + "# 활성화함수: AdamW, 최적화 함수에 가중치 감쇠 추가한 변형된 경사 하강법 알고리즘" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 모델 구조 출력" + ], + "metadata": { + "id": "FplZolRfgYx8" + }, + "id": "FplZolRfgYx8" + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6a01ff60-add0-44fe-b841-bfa59022e480", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6a01ff60-add0-44fe-b841-bfa59022e480", + "outputId": "ee126ae6-214a-44f8-dba7-e61c0f2e4876" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "bert\n", + "└ embeddings\n", + "│ └ word_embeddings\n", + "│ └ position_embeddings\n", + "│ └ token_type_embeddings\n", + "│ └ LayerNorm\n", + "│ └ dropout\n", + "└ encoder\n", + "│ └ layer\n", + "│ │ └ 0\n", + "│ │ └ 1\n", + "│ │ └ 2\n", + "│ │ └ 3\n", + "│ │ └ 4\n", + "│ │ └ 5\n", + "│ │ └ 6\n", + "│ │ └ 7\n", + "│ │ └ 8\n", + "│ │ └ 9\n", + "│ │ └ 10\n", + "│ │ └ 11\n", + "└ pooler\n", + "│ └ dense\n", + "│ └ activation\n", + "dropout\n", + "classifier\n" + ] + } + ], + "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)" + ] + }, + { + "cell_type": "markdown", + "id": "12ff156c-abb1-4ba7-a0e5-d1543cb28f69", + "metadata": { + "id": "12ff156c-abb1-4ba7-a0e5-d1543cb28f69" + }, + "source": [ + "### 모델 학습 및 평가" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "941caa66-bf44-4444-b4d2-2d6c181aeb20", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "941caa66-bf44-4444-b4d2-2d6c181aeb20", + "outputId": "d8826f7a-04d8-4764-ccbc-392af79bfd99" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1: Train Loss: 0.5435 Val Loss: 0.4491 Val Accuracy 0.7873\n", + "Saved the model weights\n", + "Epoch 2: Train Loss: 0.4106 Val Loss: 0.4161 Val Accuracy 0.8035\n", + "Saved the model weights\n", + "Epoch 3: Train Loss: 0.3294 Val Loss: 0.4249 Val Accuracy 0.8165\n" + ] + } + ], + "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", + "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(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\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", + " 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(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\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.item()\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", + "\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(), \"BertForSequenceClassification.pt\")\n", + " print(\"Saved the model weights\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07daeefa-2dec-4e70-bb4e-d2e705772ab4", + "metadata": { + "id": "07daeefa-2dec-4e70-bb4e-d2e705772ab4", + "outputId": "aa237848-b108-4dc6-9e6a-a9a02d1355b8" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/torch310/lib/python3.10/site-packages/huggingface_hub/file_download.py:943: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n", + "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Loss : 0.4341\n", + "Test Accuracy : 0.8197\n" + ] + } + ], + "source": [ + "model=BertForSequenceClassification.from_pretrained(\n", + " pretrained_model_name_or_path=\"bert-base-multilingual-cased\",\n", + " num_labels=2\n", + ").to(device)\n", + "model.load_state_dict(torch.load(\"BertForSequenceClassification.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}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5f7dd02b-c068-4fd5-9166-b75354241bb9", + "metadata": { + "id": "5f7dd02b-c068-4fd5-9166-b75354241bb9" + }, + "source": [ + "## BART" + ] + }, + { + "cell_type": "markdown", + "id": "4ed0b9b5-89ca-4be6-9aa4-9e401edeb691", + "metadata": { + "id": "4ed0b9b5-89ca-4be6-9aa4-9e401edeb691" + }, + "source": [ + "### 뉴스 요약 데이터세트 불러오기" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64d071de-0471-4155-8a4c-13d7bbd16506", + "metadata": { + "colab": { + "referenced_widgets": [ + "a09be095b71b4cdaa76ceb2cd1bd1557", + "cf72d08fdca04143a0706536ec682a2e", + "1d2f0fe0a6944e509e8362f0d60db5aa", + "630262c174864768b84ed280e2a0142a", + "0c1d4ff2b82643dfb8b002e2e814f5df" + ] + }, + "id": "64d071de-0471-4155-8a4c-13d7bbd16506", + "outputId": "4e977e4c-a37b-4034-9aaf-927f9cc821fe" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a09be095b71b4cdaa76ceb2cd1bd1557", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading readme: 0%| | 0.00/2.02k [00:00