\ub525\ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c \ucd5c\uadfc \uba87 \ub144 \uac04 \uac00\uc7a5 \uc8fc\ubaa9\ubc1b\ub294 \uae30\uc220 \uc911 \ud558\ub098\ub294 BERT(Bidirectional Encoder Representations from Transformers) \uc785\ub2c8\ub2e4. BERT\ub294 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\ub294 \ub370 \ud0c1\uc6d4\ud55c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uba70, \uc774\ub294 \ub2e4\uc591\ud55c NLP \uc791\uc5c5\uc5d0 \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 BERT\ub294 \ud06c\uae30\uac00 \ud06c\uace0 \uc5f0\uc0b0 \ube44\uc6a9\uc774 \ub9ce\uc774 \ub4e4\uc5b4, \ubaa8\ubc14\uc77c \ud658\uacbd\uc5d0\uc11c\ub294 \uc0ac\uc6a9\uc774 \uc5b4\ub835\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 Mobile BERT\uac00 \ub4f1\uc7a5\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT\uc640 Mobile BERT\uc758 \ud2b9\uc9d5\uc744 \ube44\uad50\ud558\uace0, \ub450 \ubaa8\ub378\uc758 Tokenizer\ub97c \uc0ac\uc6a9\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
BERT\ub294 2018\ub144 \uad6c\uae00\uc5d0\uc11c \ubc1c\ud45c\ud55c \uc5b8\uc5b4 \ud45c\ud604 \ubaa8\ub378\ub85c, \uc0ac\uc804 \ud6c8\ub828\ub41c \uc5b8\uc5b4 \ud45c\ud604\uc744 \ud559\uc2b5\ud558\uc5ec \ub2e4\uc591\ud55c NLP \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uac8c \ub3d5\uc2b5\ub2c8\ub2e4. BERT\ub294 Transformer’s encoder \uad6c\uc870\ub97c \uae30\ubc18\uc73c\ub85c \ud558\uba70, Bidirectional \ubc29\uc2dd\uc73c\ub85c \ubb38\ub9e5\uc744 \uc774\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 NLP \uc791\uc5c5\uc73c\ub85c\ub294 \uac10\uc815 \ubd84\uc11d, \uc9c8\ubb38 \ub2f5\ubcc0 \uc2dc\uc2a4\ud15c, \ubb38\uc7a5 \uc720\uc0ac\ub3c4 \uacc4\uc0b0 \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
Mobile BERT\ub294 BERT\uc758 \uacbd\ub7c9\ud654 \ubc84\uc804\uc73c\ub85c, \ubaa8\ubc14\uc77c \uae30\uae30\uc5d0\uc11c \ud6a8\uc728\uc801\uc73c\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4. Mobile BERT\ub294 BERT\uc758 \ud30c\ub77c\ubbf8\ud130 \uc218\ub97c \ub300\ud3ed \uc904\uc600\uc73c\ub098, \uc131\ub2a5\uc740 \uc720\uc9c0\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ubc14\uc77c \uae30\uae30\uc5d0\uc11c\ub3c4 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc791\uc5c5\uc744 \uc6d0\ud65c\ud558\uac8c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ud5c8\uae45\ud398\uc774\uc2a4(Transformers)\ub294 \ub2e4\uc591\ud55c \uc0ac\uc804 \ud6c8\ub828\ub41c NLP \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc8fc\ub294 Python \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 BERT, Mobile BERT, GPT-2 \ub4f1 \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub610\ud55c, \ubaa8\ub378\uc758 Tokenizer\ub3c4 \ud568\uaed8 \uc81c\uacf5\ud558\uc5ec \ud14d\uc2a4\ud2b8\ub97c \uc190\uc27d\uac8c \uc804\ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uc2b5\ub2c8\ub2e4.<\/p>\n
pip install transformers torch<\/code><\/pre>\n4. Mobile BERT vs BERT Tokenizer \uc0ac\uc6a9 \uc608\uc81c<\/h2>\n
\uc774\uc81c BERT\uc640 Mobile BERT\uc758 Tokenizer \uc0ac\uc6a9\ubc95\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\uc758 \ucf54\ub4dc\ub294 \ub450 \ubaa8\ub378\uc758 Tokenizer\ub97c \uc124\uce58\ud558\uace0, \uc785\ub825 \ud14d\uc2a4\ud2b8\uc5d0 \ub300\ud574 \uac01\uac01 \ud1a0\ud06c\ub098\uc774\uc9d5\uc744 \uc218\ud589\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
from transformers import BertTokenizer, MobileBertTokenizer\n\n# BERT Tokenizer \ucd08\uae30\ud654\nbert_tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n\n# Mobile BERT Tokenizer \ucd08\uae30\ud654\nmobile_bert_tokenizer = MobileBertTokenizer.from_pretrained(\"google\/mobilebert-uncased\")\n\n# \uc785\ub825 \ud14d\uc2a4\ud2b8\ntext = \"\ub525\ub7ec\ub2dd\uc740 \ub9e4\uc6b0 \ud765\ubbf8\ub85c\uc6b4 \ubd84\uc57c\uc785\ub2c8\ub2e4.\"\n\n# BERT Tokenization\nbert_tokens = bert_tokenizer.tokenize(text)\nprint(\"BERT Tokens:\", bert_tokens)\n\n# Mobile BERT Tokenization\nmobile_bert_tokens = mobile_bert_tokenizer.tokenize(text)\nprint(\"Mobile BERT Tokens:\", mobile_bert_tokens)\n<\/code><\/pre>\n4.1 \ucf54\ub4dc \uc124\uba85<\/h3>\n
\uc704\uc758 \uc608\uc81c\uc5d0\uc11c, transformers<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 BertTokenizer<\/code>\uc640 MobileBertTokenizer<\/code>\ub97c \ud1b5\ud574 \ub450 \uac1c\uc758 Tokenizer\ub97c \ucd08\uae30\ud654\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc785\ub825 \ud14d\uc2a4\ud2b8\ub97c tokenize<\/code> \uba54\uc18c\ub4dc\ub97c \uc0ac\uc6a9\ud574 \ud1a0\ud06c\ub098\uc774\uc988\ud55c \ub4a4, \uacb0\uacfc\ub97c \ucd9c\ub825\ud569\ub2c8\ub2e4. BERT\uc640 Mobile BERT\uc758 \ud1a0\ud070\ud654 \uacb0\uacfc\ub97c \ube44\uad50\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n5. \ube44\uad50 \ubd84\uc11d<\/h2>\n
BERT\uc640 Mobile BERT\uc758 Tokenizer\ub97c \uc0ac\uc6a9\ud55c \uacb0\uacfc, \ub450 \ubaa8\ub378\uc758 \ud1a0\ud070\ud654 \uacb0\uacfc\ub97c \ube44\uad50\ud558\uace0, \uac01 \ubaa8\ub378\uc758 \ud2b9\uc131\uc744 \ubd84\uc11d\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc0ac\uc6a9\ud55c \uc785\ub825 \ubb38\uc7a5\uc740 “\ub525\ub7ec\ub2dd\uc740 \ub9e4\uc6b0 \ud765\ubbf8\ub85c\uc6b4 \ubd84\uc57c\uc785\ub2c8\ub2e4.” \uc785\ub2c8\ub2e4.<\/p>\n
# BERT Tokenization \uacb0\uacfc\nBERT Tokens: ['deep', '##learning', '\uc740', '\ub9e4\uc6b0', '\ud765\ubbf8\ub85c\uc6b4', '\ubd84\uc57c', '\uc785\ub2c8\ub2e4', '.']\n\n# Mobile BERT Tokenization \uacb0\uacfc\nMobile BERT Tokens: ['\ub525\ub7ec\ub2dd', '\uc740', '\ub9e4\uc6b0', '\ud765\ubbf8\ub85c\uc6b4', '\ubd84\uc57c', '\uc785\ub2c8\ub2e4', '.']\n<\/code><\/pre>\n5.1 \ubd84\uc11d<\/h3>\n
BERT Tokenizer\ub294 \ud55c \ub2e8\uc5b4\ub97c \uc5ec\ub7ec \uac1c\uc758 \uc11c\ube0c\uc6cc\ub4dc\ub85c \ubd84\ud560\ud558\ub294 \ubc18\uba74, Mobile BERT Tokenizer\ub294 \uc785\ub825\ub41c \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub2e8\uc704\ub85c \uc798\uac8c \ucabc\uac1c\uc9c0 \uc54a\uace0 \uc720\uc9c0\ud569\ub2c8\ub2e4. \uc774\ub294 Mobile BERT\uac00 \ubaa8\ubc14\uc77c \ud658\uacbd\uc5d0\uc11c \ubcf4\ub2e4 \ud6a8\uc728\uc801\uc73c\ub85c \uc791\ub3d9\ud560 \uc218 \uc788\ub3c4\ub85d \ucd5c\uc801\ud654 \ub418\uc5b4 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.<\/p>\n
6. \uace0\uae09 \ud65c\uc6a9<\/h2>\n
Tokenization\uacfc \ubaa8\ub378 \ub85c\ub529 \uc678\uc5d0\ub3c4, \ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 BERT \ubc0f Mobile BERT \ubaa8\ub378\uc744 \ud65c\uc6a9\ud55c \ub2e4\uc591\ud55c \uace0\uae09 \uc791\uc5c5\ub4e4\uc744 \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uac10\uc815 \ubd84\uc11d \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uac70\ub098, \ud2b9\uc815 \ud0dc\uc2a4\ud06c\uc5d0 \ub300\ud55c Fine-tuning\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6.1 \ubaa8\ub378 \ud30c\uc778\ud29c\ub2dd<\/h3>\n
\ubaa8\ub378 \ud30c\uc778\ud29c\ub2dd\uc740 \ud2b9\uc815 \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud574 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc7ac\ud6c8\ub828\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc544\ub798\uc758 \ucf54\ub4dc\ub294 BERT \ubaa8\ub378\uc744 \ud30c\uc778\ud29c\ub2dd\ud558\uae30 \uc704\ud55c \uae30\ubcf8\uc801\uc778 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n
from transformers import BertForSequenceClassification, Trainer, TrainingArguments\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n\n# \uc608\uc2dc \ub370\uc774\ud130\uc14b \ud074\ub798\uc2a4\nclass CustomDataset(Dataset):\n def __init__(self, texts, labels, tokenizer):\n self.texts = texts\n self.labels = labels\n self.tokenizer = tokenizer\n\n def __len__(self):\n return len(self.texts)\n\n def __getitem__(self, idx):\n input_encoding = self.tokenizer(self.texts[idx], truncation=True, padding='max_length', max_length=512, return_tensors='pt')\n item = {key: val[0] for key, val in input_encoding.items()}\n item['labels'] = torch.tensor(self.labels[idx])\n return item\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2)\n\n# \ub370\uc774\ud130\uc14b \ubc0f DataLoader \uc0dd\uc131\ntrain_texts = [\"\uc774 \uc601\ud654\ub294 \uc88b\uc558\uc5b4\uc694.\", \"\uc774 \uc601\ud654\ub294 \ubcc4\ub85c\uc600\uc5b4\uc694.\"]\ntrain_labels = [1, 0] # \uac10\uc815 \ub808\uc774\ube14 1: \uae0d\uc815, 0: \ubd80\uc815\ntrain_dataset = CustomDataset(train_texts, train_labels, bert_tokenizer)\ntrain_loader = DataLoader(train_dataset, batch_size=2)\n\n# TrainingArguments \uc124\uc815\ntraining_args = TrainingArguments(\n output_dir='.\/results',\n num_train_epochs=3,\n per_device_train_batch_size=2,\n logging_dir='.\/logs',\n)\n\n# Trainer \uac1d\uccb4 \uc0dd\uc131\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n)\n\n# \ubaa8\ub378 \ud6c8\ub828\ntrainer.train()\n<\/code><\/pre>\n6.2 \ucf54\ub4dc \uc124\uba85<\/h3>\n
\ucf54\ub4dc\uc5d0\uc11c\ub294 CustomDataset \ud074\ub798\uc2a4\ub97c \uc815\uc758\ud558\uc5ec \uc785\ub825 \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\uba70, BERT \ubaa8\ub378\uc744 \ub85c\ub4dc\ud55c \ud6c4 Trainer \uac1d\uccb4\ub97c \ud1b5\ud574 \ud6c8\ub828\uc744 \uc2dc\uc791\ud569\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc744 \ud1b5\ud574 BERT \ubaa8\ub378\uc744 \ud2b9\uc815 \ud0dc\uc2a4\ud06c\uc5d0 \ub9de\uac8c \uc870\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec BERT\uc640 Mobile BERT\uc758 Tokenizer\ub97c \ube44\uad50\ud558\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \ubaa8\ub378 \ud6c8\ub828\uc758 \uae30\ubcf8 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. BERT\ub294 \uc131\ub2a5\uc774 \ub6f0\uc5b4\ub098\uc9c0\ub9cc \uace0\uc0ac\uc591\uc758 \ud558\ub4dc\uc6e8\uc5b4\ub97c \uc694\uad6c\ud558\ub294 \ubc18\uba74, Mobile BERT\ub294 \uacbd\ub7c9\ud654\ub41c \ubaa8\ub378\ub85c\uc368 \ubaa8\ubc14\uc77c \ud658\uacbd\uc5d0\uc11c\ub3c4 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ub2e4\uc591\ud55c \uc2e4\uc2b5\uacfc \uc5f0\uad6c\ub97c \ud1b5\ud574 \ub525\ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0\uc11c \uc131\uacfc\ub97c \uc774\ub904\ub098\uac00\uae30\ub97c \uae30\ub300\ud569\ub2c8\ub2e4.<\/p>\n
\ucc38\uace0 \ubb38\ud5cc<\/h2>\n