{"id":29554,"date":"2024-10-28T01:55:25","date_gmt":"2024-10-28T01:55:25","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29554"},"modified":"2024-11-26T06:52:18","modified_gmt":"2024-11-26T06:52:18","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-bert-%ec%95%99%ec%83%81%eb%b8%94-%ed%95%99%ec%8a%b5-%eb%9d%bc%ec%9d%b4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29554\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uc815"},"content":{"rendered":"

<\/p>\n

\ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uac00 \uc911\uc694\ud55c \ub3c4\uc804 \uacfc\uc81c\uac00 \ub418\uc5c8\uc73c\uba70, BERT(Bidirectional Encoder Representations from Transformers)\uc640 \uac19\uc740 \ubaa8\ub378\uc774 \uc774 \ubd84\uc57c\uc758 \ud601\uc2e0\uc744 \uc774\ub04c\uc5b4\uc654\uc2b5\ub2c8\ub2e4. BERT \ubaa8\ub378\uc740 \ub2e8\uc5b4\uc758 \ub9e5\ub77d\uc744 \uc591\ubc29\ud5a5\uc73c\ub85c \uc774\ud574\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uba70, \uc774\ub294 \ub354\uc6b1 \uc815\uad50\ud55c \ubc29\ubc95\uc73c\ub85c \uc790\uc5f0\uc5b4 \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\uac8c \ud569\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uc124\uc815\ud558\uace0, \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. BERT \uc559\uc0c1\ube14 \ud559\uc2b5\uc774\ub780?<\/h2>\n

\uc559\uc0c1\ube14 \ud559\uc2b5\uc740 \uc5ec\ub7ec \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uacb0\ud569\ud558\uc5ec \ucd5c\uc885 \uc608\uce21\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\ub860\uc785\ub2c8\ub2e4. \uc5ec\ub7ec \ubaa8\ub378\uc758 \uc608\uce21\uce58\ub97c \ud3c9\uade0\ud558\uac70\ub098, \ub2e4\uc218\uacb0\uc758 \uc6d0\uce59(Get majority vote)\uc73c\ub85c \uacb0\uc815\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c, \uc774\ub294 \ub2e8\uc77c \ubaa8\ub378\uc758 \ud3b8\ud5a5\uc744 \uc904\uc774\uace0, \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4. BERT\uc640 \uac19\uc740 \uac15\ub825\ud55c \uc5b8\uc5b4 \ubaa8\ub378\uc744 \uc559\uc0c1\ube14\ud558\uc5ec \ud65c\uc6a9\ud558\ub294 \uac83\uc740 \ubaa8\ub378\uc758 \ud559\uc2b5 \uc131\ub2a5\uacfc \uc608\uce21 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\ub294 \ud6a8\uacfc\ub97c \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

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

\ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \uad00\ub828 \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

pip install transformers torch<\/code><\/pre>\n
\uc774\uc678\uc5d0 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c\ub294 \ub370\uc774\ud130 \ucc98\ub9ac\ub97c \uc704\ud55c pandas<\/code>\uc640 \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00\ub97c \uc704\ud55c scikit-learn<\/code>\uc744 \uc0ac\uc6a9\ud560 \uc608\uc815\uc785\ub2c8\ub2e4.<\/p>\n
pip install pandas scikit-learn<\/code><\/pre>\n
3. \ub370\uc774\ud130 \uc900\ube44<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uc601\ud654 \ub9ac\ubdf0 \uac10\uc131 \ubd84\uc11d \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\uc14b\uc740 \ub9ac\ubdf0\uc640 \uac10\uc131 \ub808\uc774\ube14\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc73c\uba70, \uae0d\uc815\uc801 \ub610\ub294 \ubd80\uc815\uc801\uc778 \ub9ac\ubdf0\ub85c \uad6c\ubd84\ub429\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc740 pandas\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubd88\ub7ec\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import pandas as pd\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ndata = pd.read_csv('movie_reviews.csv')\nprint(data.head())<\/code><\/pre>\n
4. BERT \ubaa8\ub378 \uc124\uc815<\/h2>\n
\ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uc124\uc815\ud569\ub2c8\ub2e4. BERT\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0, \ud1a0\ud06c\ub098\uc774\uc800\ub97c \uc124\uc815\ud558\uc5ec \uc785\ub825 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
from transformers import BertTokenizer, BertForSequenceClassification\nimport torch\n\n# BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800 \ub85c\ub4dc\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n\n# \uc785\ub825 \ub370\uc774\ud130 \ud1a0\ud06c\ub098\uc774\uc9d5\ndef tokenize_data(sentences):\n    return tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')\n\n# \uc608\uc2dc\ub85c \uccab \ubc88\uc9f8 \ubb38\uc7a5\uc744 \ud1a0\ud06c\ub098\uc774\uc9d5\ntokens = tokenize_data(data['review'].tolist())\nprint(tokens)  # \ud1a0\ud06c\ub098\uc774\uc988\ub41c \ub370\uc774\ud130 \ud655\uc778<\/code><\/pre>\n
5. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\ubaa8\ub378 \ud559\uc2b5\uc744 \uc704\ud558\uc5ec \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uac01 \ub9ac\ubdf0\ub294 \ud1a0\ud070\ud654\ub418\uba70, \uc774\ub97c \ubaa8\ub378\uc774 \uc778\uc2dd\ud560 \uc218 \uc788\ub294 \ud3ec\ub9f7\uc73c\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4. \ub610\ud55c, \ubc30\uce58 \uc0ac\uc774\uc988\ub97c \uc124\uc815\ud558\uc5ec GPU\uc5d0\uc11c \ud559\uc2b5 \uc18d\ub3c4\ub97c \ud5a5\uc0c1\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n
from torch.utils.data import DataLoader, TensorDataset\n\n# \uc785\ub825 \ub370\uc774\ud130\uc640 \ub808\uc774\ube14 \uc124\uc815\ninputs = tokens['input_ids']\nattn_masks = tokens['attention_mask']\nlabels = torch.tensor(data['label'].tolist())\n\n# \ud150\uc11c \ub370\uc774\ud130\uc14b \uc0dd\uc131\ndataset = TensorDataset(inputs, attn_masks, labels)\n\n# \ub370\uc774\ud130 \ub85c\ub354 \uc124\uc815\nbatch_size = 16\ndataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)<\/code><\/pre>\n
6. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
BERT \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc635\ud2f0\ub9c8\uc774\uc800\uc640 \uc190\uc2e4 \ud568\uc218\ub97c \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 AdamW \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ubaa8\ub378 \ud559\uc2b5\uc744 \uc704\ud55c \uc190\uc2e4 \ud568\uc218\ub294 CrossEntropyLoss\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
from transformers import AdamW\nfrom torch import nn\n\n# \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815\noptimizer = AdamW(model.parameters(), lr=5e-5)\n\n# \uc190\uc2e4 \ud568\uc218 \uc124\uc815\nloss_fn = nn.CrossEntropyLoss()\n\n# \ubaa8\ub378 \ud6c8\ub828\uc744 \uc704\ud55c \ud568\uc218 \uc791\uc131\ndef train_model(dataloader, model, optimizer, loss_fn, epochs=3):\n    model.train()\n    for epoch in range(epochs):\n        total_loss = 0\n        for batch in dataloader:\n            input_ids, attention_masks, labels = batch\n            \n            # \ubaa8\ub378\uc5d0 \ub370\uc774\ud130 \uc804\uc1a1\n            input_ids = input_ids.to('cuda')\n            attention_masks = attention_masks.to('cuda')\n            labels = labels.to('cuda')\n            \n            # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n            optimizer.zero_grad()\n            \n            # \ubaa8\ub378 \uc608\uce21\n            outputs = model(input_ids, token_type_ids=None, attention_mask=attention_masks)\n            loss = loss_fn(outputs.logits, labels)\n            \n            # \uc190\uc2e4 \uacc4\uc0b0 \ubc0f \uc5ed\uc804\ud30c\n            total_loss += loss.item()\n            loss.backward()\n            optimizer.step()\n        print(f'Epoch: {epoch+1}, Loss: {total_loss\/len(dataloader)}')\n\n# \ubaa8\ub378 \ud559\uc2b5 \ud638\ucd9c\ntrain_model(dataloader, model.to('cuda'), optimizer, loss_fn)<\/code><\/pre>\n
7. \uc559\uc0c1\ube14 \ubaa8\ub378 \uc124\uc815<\/h2>\n
\uc774\uc81c \uae30\ubcf8\uc801\uc778 BERT \ubaa8\ub378\uc744 \uc124\uc815\ud588\uc73c\ub2c8, \uc5ec\ub7ec \uac1c\uc758 BERT \ubaa8\ub378\uc744 \uc559\uc0c1\ube14\ud558\uc5ec \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uaca0\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \ub450 \uac1c\uc758 BERT \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uace0, \uc608\uce21 \uacb0\uacfc\ub97c \ud3c9\uade0\ud558\uc5ec \ucd5c\uc885 \uc608\uce21\uc744 \ub9cc\ub4e4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
def create_ensemble_model(num_models=2):\n    models = []\n    for _ in range(num_models):\n        model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2).to('cuda')\n        models.append(model)\n    return models\n\n# \uc559\uc0c1\ube14 \ubaa8\ub378 \uc0dd\uc131\nensemble_models = create_ensemble_model()<\/code><\/pre>\n
8. \uc559\uc0c1\ube14 \ud559\uc2b5 \ubc0f \uc608\uce21<\/h2>\n
\uc559\uc0c1\ube14\ub41c \ubaa8\ub378\ub4e4\uc744 \ud559\uc2b5\ud558\uace0, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \uc608\uce21\uc744 \uc218\ud589\ud55c \ud6c4, \uacb0\uacfc\ub97c \ud3c9\uade0\ud558\uc5ec \ucd5c\uc885 \uc608\uce21\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
def train_ensemble(models, dataloader, optimizer, loss_fn, epochs=3):\n    for model in models:\n        train_model(dataloader, model, optimizer, loss_fn, epochs)\n\ndef ensemble_predict(models, input_ids, attention_masks):\n    preds = []\n    for model in models:\n        model.eval()\n        with torch.no_grad():\n            outputs = model(input_ids, attention_mask=attention_masks)\n            preds.append(outputs.logits)\n    return sum(preds) \/ len(preds)\n\n# \uc559\uc0c1\ube14 \ubaa8\ub378 \ud559\uc2b5\ntrain_ensemble(ensemble_models, dataloader, optimizer, loss_fn)\n\n# \uc608\uc2dc\ub85c \uccab \ubc88\uc9f8 \ubb38\uc7a5 \uc608\uce21\ninputs = tokenize_data([data['review'].iloc[0]])\naverage_logits = ensemble_predict(ensemble_models, inputs['input_ids'].to('cuda'), inputs['attention_mask'].to('cuda'))\npredictions = torch.argmax(average_logits, dim=1)\nprint(f'Predicted label: {predictions}')  # \uc608\uce21 \uacb0\uacfc \ud655\uc778<\/code><\/pre>\n
9. \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00<\/h2>\n
\ub9c8\uc9c0\ub9c9\uc73c\ub85c, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc815\ud655\ub3c4, \uc815\ubc00\ub3c4, \uc7ac\ud604\uc728 \ub4f1\uc744 \uce21\uc815\ud558\uc5ec \uc131\ub2a5\uc744 \uac80\ud1a0\ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.metrics import accuracy_score, classification_report\n\n# \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \ub85c\ub4dc\ntest_data = pd.read_csv('movie_reviews_test.csv')\ntest_tokens = tokenize_data(test_data['review'].tolist())\ntest_inputs = test_tokens['input_ids'].to('cuda')\ntest_masks = test_tokens['attention_mask'].to('cuda')\n\n# \uc559\uc0c1\ube14 \uc608\uce21\ntest_logits = ensemble_predict(ensemble_models, test_inputs, test_masks)\ntest_predictions = torch.argmax(test_logits, axis=1)\n\n# \uc815\ud655\ub3c4 \ubc0f \ud3c9\uac00 \uc9c0\ud45c \ucd9c\ub825\naccuracy = accuracy_score(test_data['label'].tolist(), test_predictions.cpu())\nreport = classification_report(test_data['label'].tolist(), test_predictions.cpu())\n\nprint(f'Accuracy: {accuracy}\\n')\nprint(report)<\/code><\/pre>\n
\uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 BERT \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ubc29\ubc95\uacfc, \uc5ec\ub7ec \ubaa8\ub378\uc744 \uc559\uc0c1\ube14\ud558\uc5ec \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 BERT \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \uc801\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \uc0ac\uc6a9\uc790 \ub9de\ucda4\ud615 \uc559\uc0c1\ube14 \ubaa8\ub378\ub9c1\uc744 \ud1b5\ud574 \ub354\uc6b1 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \uae30\ub300\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\uc5d0 \ub300\ud574 \uc774\uc640 \uac19\uc740 \uae30\uc220\uc744 \ud65c\uc6a9\ud574 \ub098\uac00\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uac00 \uc911\uc694\ud55c \ub3c4\uc804 \uacfc\uc81c\uac00 \ub418\uc5c8\uc73c\uba70, BERT(Bidirectional Encoder Representations from Transformers)\uc640 \uac19\uc740 \ubaa8\ub378\uc774 \uc774 \ubd84\uc57c\uc758 \ud601\uc2e0\uc744 \uc774\ub04c\uc5b4\uc654\uc2b5\ub2c8\ub2e4. BERT \ubaa8\ub378\uc740 \ub2e8\uc5b4\uc758 \ub9e5\ub77d\uc744 \uc591\ubc29\ud5a5\uc73c\ub85c \uc774\ud574\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uba70, \uc774\ub294 \ub354\uc6b1 \uc815\uad50\ud55c \ubc29\ubc95\uc73c\ub85c \uc790\uc5f0\uc5b4 \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\uac8c \ud569\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uc124\uc815\ud558\uace0, \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 … \ub354 \ubcf4\uae30 “\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uc815”<\/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-29554","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, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uc815 - \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\/29554\/\" \/>\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, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uc815 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uac00 \uc911\uc694\ud55c \ub3c4\uc804 \uacfc\uc81c\uac00 \ub418\uc5c8\uc73c\uba70, BERT(Bidirectional Encoder Representations from Transformers)\uc640 \uac19\uc740 \ubaa8\ub378\uc774 \uc774 \ubd84\uc57c\uc758 \ud601\uc2e0\uc744 \uc774\ub04c\uc5b4\uc654\uc2b5\ub2c8\ub2e4. BERT \ubaa8\ub378\uc740 \ub2e8\uc5b4\uc758 \ub9e5\ub77d\uc744 \uc591\ubc29\ud5a5\uc73c\ub85c \uc774\ud574\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uba70, \uc774\ub294 \ub354\uc6b1 \uc815\uad50\ud55c \ubc29\ubc95\uc73c\ub85c \uc790\uc5f0\uc5b4 \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\uac8c \ud569\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uc124\uc815\ud558\uace0, \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 … \ub354 \ubcf4\uae30 "\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uc815"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29554\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T01:55:25+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:52:18+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"2\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/29554\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29554\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uc815\",\"datePublished\":\"2024-10-28T01:55:25+00:00\",\"dateModified\":\"2024-11-26T06:52:18+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29554\/\"},\"wordCount\":28,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud65c\uc6a9\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/29554\/\",\"url\":\"https:\/\/atmokpo.com\/w\/29554\/\",\"name\":\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uc815 - 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