\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\uac00 \uc81c\uacf5\ud558\ub294 BERT \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \uc559\uc0c1\ube14 \ud559\uc2b5<\/strong>\uc744 \uc218\ud589\ud558\uace0, \uc774\ub97c \ud1b5\ud574 \uc608\uce21 \uc131\ub2a5\uc744 \uac1c\uc120\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc559\uc0c1\ube14 \ud559\uc2b5\uc740 \uc5ec\ub7ec \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uacb0\ud569\ud558\uc5ec \ub354 \uc88b\uc740 \uc131\ub2a5\uc744 \uc5bb\uace0\uc790 \ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 BERT \ubaa8\ub378\uc744 \ub2e4\uc591\ud558\uac8c \uc870\ud569\ud558\uc5ec \uc559\uc0c1\ube14\ud654\ub97c \uad6c\ud604\ud558\ub294 \uacfc\uc815\uc744 \uc0c1\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \uc559\uc0c1\ube14 \ud559\uc2b5\uc740 \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \uc870\ud569\ud558\uc5ec \ucd5c\uc885 \uc608\uce21 \uacb0\uacfc\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\uc2dd\uc740 \uac01\uac01\uc758 \ubaa8\ub378\uc774 \uac00\uc9c0\ub294 \uac15\uc810\uc744 \uc0b4\ub824, \uc804\uccb4 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ud65c\uc6a9\ub418\ub294 \uc559\uc0c1\ube14 \ubc29\ubc95\uc73c\ub85c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ubc95\ub4e4\uc774 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n BERT(Bidirectional Encoder Representations from Transformers)\ub294 Transformer \uae30\ubc18\uc758 \uc0ac\uc804 \ud6c8\ub828\ub41c \uc5b8\uc5b4 \ubaa8\ub378\ub85c, \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \uc791\uc5c5\uc5d0 \ud0c1\uc6d4\ud55c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. BERT\uc758 \ud2b9\uc9d5 \uc911 \ub450 \uac00\uc9c0\ub294:<\/p>\n \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uc704\ud574 \uc900\ube44\ud560 \ub370\uc774\ud130\uc14b\uc740 \uac04\ub2e8\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\ub97c \ub2e4\ub8e8\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uae0d\uc815\/\ubd80\uc815 \uac10\uc131\uc744 \ubd84\ub958\ud558\ub294 \uc791\uc5c5\uc744 \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \uba3c\uc800, \ud5c8\uae45\ud398\uc774\uc2a4<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud569\ub2c8\ub2e4:<\/p>\n \ub2e4\uc74c\uc73c\ub85c Hugging Face\uc758 \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 BERT<\/strong> \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \ud558\ub294 \ub450 \uac1c\uc758 \ubcc0\ud615 \ubaa8\ub378\uc744 \ub9cc\ub4ed\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc559\uc0c1\ube14 \ud6a8\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4. \uba3c\uc800, BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc624\ub294 \ud568\uc218\ub97c \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n \ubaa8\ub378\uc758 \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud558\ub294 \uacfc\uc815\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n \uc774\uc81c \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \uc2dc\uac04\uc785\ub2c8\ub2e4. \uc55e\uc11c \ubd88\ub7ec\uc628 \ubaa8\ub378\uacfc \uc804\ucc98\ub9ac\ud55c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud6c8\ub828\ud569\ub2c8\ub2e4:<\/p>\n \uc774\uc81c \ub450 \ubc88\uc9f8 \ubaa8\ub378\uc744 \ucd94\uac00\ud558\uc5ec \uc559\uc0c1\ube14\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. BERT \uad6c\uc870\ub294 \ub3d9\uc77c\ud558\uc9c0\ub9cc, \ub2e4\ub978 \ucd08\uae30\ud654 \ub610\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n \ubaa8\ub378 \uc608\uce21 \uacb0\uacfc\ub97c \uc559\uc0c1\ube14\ud558\uc5ec \ucd5c\uc885 \uacb0\uacfc\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \ub450 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \ud3c9\uade0 \ub0b4\uc5b4 \ucd5c\uc885 \uc608\uce21\uc744 \uad6c\ud569\ub2c8\ub2e4:<\/p>\n \uc774\uc81c \uc559\uc0c1\ube14 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc815\ud655\ub3c4 \ub610\ub294 F1 \uc810\uc218\uc640 \uac19\uc740 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n \uc774\ubc88 \uac15\uc88c\ub97c \ud1b5\ud574 BERT \ubaa8\ub378\uc744 \ud65c\uc6a9\ud55c \uc559\uc0c1\ube14 \ud559\uc2b5 \ubc29\ubc95\uc5d0 \ub300\ud574 \ubc30\uc6cc \ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec \ubaa8\ub378\uc758 \uc608\uce21\uc744 \uacb0\ud569\ud568\uc73c\ub85c\uc368 \uc131\ub2a5\uc744 \uac1c\uc120\ud558\ub294 \ubc29\ubc95\uc744 \uc775\ud614\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 \uc791\uc5c5\uc73c\ub85c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uac83\ub4e4\uc774 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n \ub525\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c \uc559\uc0c1\ube14 \ud559\uc2b5\uc740 \uacc4\uc18d\ud574\uc11c \uc720\ub9dd\ud55c \ubc29\ubc95\uc73c\ub85c \uc790\ub9ac\uc7a1\uace0 \uc788\uc73c\uba70, \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \ud63c\ud569\ud568\uc73c\ub85c\uc368 \ub354 \ub192\uc740 \uc815\ud655\ub3c4\ub97c \ub2ec\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc55e\uc11c \uc5b8\uae09\ud55c \uac83\ucc98\ub7fc, \uc5ec\ub7ec BERT \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \uc131\ub2a5\uc744 \ub04c\uc5b4\uc62c\ub9ac\ub294 \ub2e4\uc591\ud55c \uc2e4\ud5d8\uc744 \uc2dc\ub3c4\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n1. \uc559\uc0c1\ube14 \ud559\uc2b5\uc758 \uae30\ucd08<\/h2>\n
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2. BERT \ubaa8\ub378 \uc18c\uac1c<\/h2>\n
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3. \ub370\uc774\ud130 \uc900\ube44<\/h2>\n
!pip install transformers datasets torch scikit-learn<\/code><\/pre>\n\ub370\uc774\ud130\uc14b \ub85c\ub4dc<\/h3>\n
datasets<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\nfrom datasets import load_dataset\n\ndataset = load_dataset('imdb')\ntrain_data = dataset['train']\ntest_data = dataset['test']<\/code><\/pre>\n4. \ubaa8\ub378 \uc124\uc815<\/h2>\n
from transformers import BertTokenizer, BertForSequenceClassification\nfrom transformers import Trainer, TrainingArguments\n\n# BERT \ubaa8\ub378 \ubc0f \ud1a0\ud06c\ub098\uc774\uc800 \ubd88\ub7ec\uc624\uae30\ndef load_model_and_tokenizer(model_name='bert-base-uncased'):\n tokenizer = BertTokenizer.from_pretrained(model_name)\n model = BertForSequenceClassification.from_pretrained(model_name)\n return tokenizer, model<\/code><\/pre>\n5. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
def preprocess_data(dataset, tokenizer, max_len=128):\n inputs = tokenizer(dataset['text'], padding=True, truncation=True, max_length=max_len, return_tensors='pt')\n inputs['labels'] = torch.tensor(dataset['label'])\n return inputs\n\ntrain_inputs = preprocess_data(train_data, tokenizer)\ntest_inputs = preprocess_data(test_data, tokenizer)<\/code><\/pre>\n6. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
def train_model(model, train_inputs):\n training_args = TrainingArguments(\n output_dir='.\/results',\n num_train_epochs=3,\n per_device_train_batch_size=16,\n per_device_eval_batch_size=64,\n evaluation_strategy='epoch',\n logging_dir='.\/logs',\n )\n \n trainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_inputs,\n )\n trainer.train()\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel1 = load_model_and_tokenizer()[1]\ntrain_model(model1, train_inputs)<\/code><\/pre>\n7. \uc559\uc0c1\ube14 \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
model2 = load_model_and_tokenizer(model_name='bert-large-uncased')[1]\ntrain_model(model2, train_inputs)<\/code><\/pre>\n8. \uc559\uc0c1\ube14 \uc608\uce21<\/h2>\n
import numpy as np\n\ndef ensemble_predict(models, inputs):\n preds = []\n for model in models:\n model.eval()\n with torch.no_grad():\n outputs = model(**inputs)\n preds.append(outputs.logits)\n \n ensemble_preds = np.mean(preds, axis=0)\n return ensemble_preds\n\nmodels = [model1, model2]\npredictions = ensemble_predict(models, test_inputs)<\/code><\/pre>\n9. \uc131\ub2a5 \ud3c9\uac00<\/h2>\n
from sklearn.metrics import accuracy_score, f1_score\n\n# \uc815\ub2f5 \ub808\uc774\ube14\uc744 \uac00\uc838\uc635\ub2c8\ub2e4\nlabels = test_data['label']\n\n# \uc559\uc0c1\ube14 \uc608\uce21 \uacb0\uacfc\uc640 \ub808\uc774\ube14\uc744 \uae30\ubc18\uc73c\ub85c \uc9c0\ud45c \uacc4\uc0b0\npredicted_labels = np.argmax(predictions, axis=1)\n\naccuracy = accuracy_score(labels, predicted_labels)\nf1 = f1_score(labels, predicted_labels)\n\nprint(f'Accuracy: {accuracy:.4f}, F1 Score: {f1:.4f}')<\/code><\/pre>\n10. \uacb0\ub860 \ubc0f \ubbf8\ub798 \uc791\uc5c5<\/h2>\n
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\ucc38\uace0 \ubb38\ud5cc<\/h2>\n