{"id":29702,"date":"2024-10-28T01:56:09","date_gmt":"2024-10-28T01:56:09","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29702"},"modified":"2024-11-26T06:51:40","modified_gmt":"2024-11-26T06:51:40","slug":"%ed%97%88%ea%b9%85%ed%8e%98%ec%9d%b4%ec%8a%a4-%ed%8a%b8%eb%a0%8c%ec%8a%a4%ed%8f%ac%eb%a8%b8-%ed%99%9c%ec%9a%a9%ea%b0%95%ec%a2%8c-%ed%8c%8c%ec%9d%b8%ed%8a%9c%eb%8b%9d-bert-%eb%aa%a8%eb%8d%b8-%ed%95%99","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29702\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ud30c\uc778\ud29c\ub2dd BERT \ubaa8\ub378 \ud559\uc2b5 \uacfc\uc815 \uc2dc\uac01\ud654"},"content":{"rendered":"

<\/p>\n

\n \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ub9ce\uc740 \ud601\uc2e0\uc774 \uc774\ub8e8\uc5b4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc740 \uadf8 \uc131\ub2a5\uacfc \ud6a8\uc728\uc131 \ub355\ubd84\uc5d0 \ub9ce\uc740 \uc778\uae30\ub97c \ub04c\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 BERT \ubaa8\ub378\uc744 \ud30c\uc778\ud29c\ub2dd\ud558\uace0 \uadf8 \uacfc\uc815\uacfc \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc744 \uc0c1\uc138\ud788 \ub2e4\ub8e8\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

1. BERT \ubaa8\ub378 \uac1c\uc694<\/h2>\n

\n BERT\ub294 Google\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc0ac\uc804 \ud559\uc2b5\ub41c \ud14d\uc2a4\ud2b8 \ud45c\ud604 \ubaa8\ub378\ub85c, Bidirectional Attention Mechanism\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc5b4\uc758 \ubb38\ub9e5\uc744 \uc591\ubc29\ud5a5\uc5d0\uc11c \uc774\ud574\ud569\ub2c8\ub2e4. BERT\ub294 \ub450 \uac00\uc9c0 \uc8fc\uc694 \uc791\uc5c5, \uc989 \ub9c8\uc2a4\ud0b9\ub41c \uc5b8\uc5b4 \ubaa8\ub378\ub9c1(Masked Language Modeling)\uacfc \ub2e4\uc74c \ubb38\uc7a5 \uc608\uce21(Next Sentence Prediction)\uc744 \ud1b5\ud574 \uc0ac\uc804 \ud559\uc2b5\ub429\ub2c8\ub2e4. \uc774\ub7f0 \uacfc\uc815\uc744 \ud1b5\ud574 BERT\ub294 \uc790\uc5f0\uc5b4 \uc774\ud574 \ubc0f \uc0dd\uc131 \uc791\uc5c5\uc5d0 \ub9e4\uc6b0 \ub192\uc740 \uc131\ub2a5\uc744 \ubcf4\uc774\uac8c \ub429\ub2c8\ub2e4.\n <\/p>\n

2. \ud658\uacbd \uc124\uc815<\/h2>\n

\n BERT \ubaa8\ub378\uc744 \ud30c\uc778\ud29c\ub2dd\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \ucf54\ub4dc\ub97c \ud1b5\ud574 Hugging Face\uc758 Transformers\uacfc \uae30\ud0c0 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.\n <\/p>\n

!pip install transformers torch datasets matplotlib seaborn<\/code><\/pre>\n
3. \ub370\uc774\ud130\uc14b \uc900\ube44<\/h2>\n
\n        \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 IMDB \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc601\ud654 \ub9ac\ubdf0\uac00 \uae0d\uc815\uc801\uc778\uc9c0 \ubd80\uc815\uc801\uc778\uc9c0\ub97c \ubd84\ub958\ud558\ub294 \uc774\uc9c4 \ubd84\ub958 \ubb38\uc81c\ub97c \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4. Hugging Face\uc758 datasets \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc190\uc27d\uac8c \ub370\uc774\ud130\ub97c \ubd88\ub7ec\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
from datasets import load_dataset\n\ndataset = load_dataset('imdb')\ntrain_dataset = dataset['train']\ntest_dataset = dataset['test']<\/code><\/pre>\n
4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\n        BERT \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud14d\uc2a4\ud2b8 \ud1a0\ud070\ud654\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. Hugging Face\uc758 Tokenizer\ub97c \uc0ac\uc6a9\ud574 \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4. BERT\ub294 \ucd5c\ub300 \uc785\ub825 \uae38\uc774\uac00 512\ub85c \uc81c\ud55c\ub418\ubbc0\ub85c \uc774 \uc810\uc744 \uc720\uc758\ud574\uc57c \ud569\ub2c8\ub2e4.\n    <\/p>\n
from transformers import BertTokenizer\n\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n\ndef tokenize_function(examples):\n    return tokenizer(examples['text'], padding='max_length', truncation=True)\n\ntokenized_train = train_dataset.map(tokenize_function, batched=True)\ntokenized_test = test_dataset.map(tokenize_function, batched=True)<\/code><\/pre>\n
5. \ub370\uc774\ud130 \ub85c\ub354 \uc124\uc815<\/h2>\n
\n        \ub370\uc774\ud130\ub97c \ub85c\ub529\ud558\uace0 \ubc30\uce58\ub85c \ub098\ub204\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc758 DataLoader\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud6c8\ub828 \ubc0f \uac80\uc99d\uc5d0 \ud544\uc694\ud55c \ubc30\uce58\ub97c \uad6c\uc131\ud569\ub2c8\ub2e4.\n    <\/p>\n
import torch\n\ntrain_loader = torch.utils.data.DataLoader(tokenized_train, batch_size=16, shuffle=True)\ntest_loader = torch.utils.data.DataLoader(tokenized_test, batch_size=16)<\/code><\/pre>\n
6. \ubaa8\ub378 \uc124\uc815<\/h2>\n
\n        \uc774\uc81c BERT \ubaa8\ub378\uc744 \uc124\uc815\ud558\uace0 \uc774\ub97c \ud30c\uc778\ud29c\ub2dd\ud560 \uc900\ube44\ub97c \ud569\ub2c8\ub2e4. Hugging Face\uc5d0\uc11c\ub294 \uac04\ub2e8\ud558\uac8c BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
from transformers import BertForSequenceClassification\n\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)<\/code><\/pre>\n
7. \ud559\uc2b5 \uc900\ube44<\/h2>\n
\n        \ud30c\uc778\ud29c\ub2dd\uc744 \uc704\ud55c \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc124\uc815\ud569\ub2c8\ub2e4. BERT\ub294 \uae30\ubcf8\uc801\uc73c\ub85c CrossEntropyLoss\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud569\ub2c8\ub2e4.\n    <\/p>\n
from transformers import AdamW\n\noptimizer = AdamW(model.parameters(), lr=5e-5)<\/code><\/pre>\n
8. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\n        \uc774\uc81c \ubaa8\ub378 \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \uac01 \uc5d0\ud3ec\ud06c\uc5d0\uc11c \ud6c8\ub828 \ub370\uc774\ud130\uc640 \uac80\uc99d \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc5d0\ud3ec\ud06c \uc218, \uc190\uc2e4 \uac12, \uc815\ud655\ub3c4 \ub4f1\uc744 \uae30\ub85d\ud558\uc5ec \ubaa8\ub2c8\ud130\ub9c1\ud569\ub2c8\ub2e4.\n    <\/p>\n
from tqdm import tqdm\n\nmodel.train()\nfor epoch in range(3):\n    for batch in tqdm(train_loader):\n        optimizer.zero_grad()\n        input_ids = batch['input_ids'].to(model.device)\n        attention_mask = batch['attention_mask'].to(model.device)\n        labels = batch['label'].to(model.device)\n\n        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n        loss = outputs.loss\n        loss.backward()\n        optimizer.step()\n        print(f\"Epoch {epoch}, Loss: {loss.item()}\")<\/code><\/pre>\n
9. \uc131\ub2a5 \ud3c9\uac00<\/h2>\n
\n        \ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ud6c4, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc815\ud655\ub3c4, \uc815\ubc00\ub3c4, \uc7ac\ud604\uc728 \ub4f1\uc758 \uba54\ud2b8\ub9ad\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.\n    <\/p>\n
from sklearn.metrics import accuracy_score\n\nmodel.eval()\npredictions, true_labels = [], []\nwith torch.no_grad():\n    for batch in test_loader:\n        input_ids = batch['input_ids'].to(model.device)\n        attention_mask = batch['attention_mask'].to(model.device)\n        \n        outputs = model(input_ids, attention_mask=attention_mask)\n        preds = torch.argmax(outputs.logits, dim=-1)\n        \n        predictions.extend(preds.cpu().numpy())\n        true_labels.extend(batch['label'].numpy())\n\naccuracy = accuracy_score(true_labels, predictions)\nprint(f'Accuracy: {accuracy}') <\/code><\/pre>\n
10. \ud559\uc2b5 \uacfc\uc815 \uc2dc\uac01\ud654<\/h2>\n
\n        \ubaa8\ub378\uc758 \ud559\uc2b5 \uacfc\uc815\uacfc \uc131\ub2a5\uc744 \uc2dc\uac01\ud654\ud558\ub294 \uac83\uc740 \ubaa8\ub378\uc744 \uc774\ud574\ud558\uace0 \ud29c\ub2dd\ud558\ub294 \ub370\uc5d0 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \ud1a0\uce58\ube44\uc804(TorchVision)\uacfc \ub9e4\ud2b8\ud50c\ub86f\ub9bd(Matplotlib)\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5 \uc190\uc2e4\uc744 \uadf8\ub798\ud504\ub85c \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4.\n    <\/p>\n
import matplotlib.pyplot as plt\n\ndef plot_loss(losses):\n    plt.plot(losses, label='Training Loss')\n    plt.xlabel('Epochs')\n    plt.ylabel('Loss')\n    plt.title('Training Loss Over Epochs')\n    plt.legend()\n    plt.show()\n\nlosses = [...]  # \uac01 \uc5d0\ud3ec\ud06c\uc758 \uc190\uc2e4\uc744 \uae30\ub85d\nplot_loss(losses)<\/code><\/pre>\n
\uacb0\ub860<\/h2>\n
\n        \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \ud30c\uc778\ud29c\ub2dd\ud558\ub294 \uc804 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b \uc900\ube44, \ubaa8\ub378 \uc124\uc815, \ud559\uc2b5 \uacfc\uc815, \uc131\ub2a5 \ud3c9\uac00 \uadf8\ub9ac\uace0 \uc2dc\uac01\ud654\uae4c\uc9c0 \ub2e4\uc591\ud55c \uacfc\uc815\uc744 \ud1b5\ud574 BERT \ubaa8\ub378\uc758 \ud30c\uc778\ud29c\ub2dd\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \uc9c4\ud589\ud560 \uc218 \uc788\uc74c\uc744 \ubcf4\uc5ec\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\n        \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\uacf5\uc801\uc778 \uad6c\ud604\uc5d0\ub294 \uc801\uc808\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd, \uadf8\ub9ac\uace0 \uacb0\uacfc \ubd84\uc11d\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. BERT\uc640 \uac19\uc740 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ud1b5\ud574 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \ud574\uacb0\ud560 \uc218 \uc788\uc74c\uc744 \uc78a\uc9c0 \ub9c8\uc138\uc694.\n    <\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ub9ce\uc740 \ud601\uc2e0\uc774 \uc774\ub8e8\uc5b4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc740 \uadf8 \uc131\ub2a5\uacfc \ud6a8\uc728\uc131 \ub355\ubd84\uc5d0 \ub9ce\uc740 \uc778\uae30\ub97c \ub04c\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 BERT \ubaa8\ub378\uc744 \ud30c\uc778\ud29c\ub2dd\ud558\uace0 \uadf8 \uacfc\uc815\uacfc \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc744 \uc0c1\uc138\ud788 \ub2e4\ub8e8\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. BERT \ubaa8\ub378 \uac1c\uc694 BERT\ub294 Google\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc0ac\uc804 \ud559\uc2b5\ub41c \ud14d\uc2a4\ud2b8 \ud45c\ud604 \ubaa8\ub378\ub85c, Bidirectional … \ub354 \ubcf4\uae30 “\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ud30c\uc778\ud29c\ub2dd BERT \ubaa8\ub378 \ud559\uc2b5 \uacfc\uc815 \uc2dc\uac01\ud654”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[30],"tags":[],"class_list":["post-29702","post","type-post","status-publish","format-standard","hentry","category-30"],"yoast_head":"\n\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ud30c\uc778\ud29c\ub2dd BERT \ubaa8\ub378 \ud559\uc2b5 \uacfc\uc815 \uc2dc\uac01\ud654 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/29702\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ud30c\uc778\ud29c\ub2dd BERT \ubaa8\ub378 \ud559\uc2b5 \uacfc\uc815 \uc2dc\uac01\ud654 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ub9ce\uc740 \ud601\uc2e0\uc774 \uc774\ub8e8\uc5b4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc740 \uadf8 \uc131\ub2a5\uacfc \ud6a8\uc728\uc131 \ub355\ubd84\uc5d0 \ub9ce\uc740 \uc778\uae30\ub97c \ub04c\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 BERT \ubaa8\ub378\uc744 \ud30c\uc778\ud29c\ub2dd\ud558\uace0 \uadf8 \uacfc\uc815\uacfc \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc744 \uc0c1\uc138\ud788 \ub2e4\ub8e8\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. 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