\ucd5c\uadfc \uba87 \ub144 \uac04 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\ub294 \uc5c4\uccad\ub09c \ubc1c\uc804\uc744 \uc774\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 \uad6c\uae00\uc758 BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc740 \ud2b9\ud788 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. BERT\ub294 \uc8fc\uc5b4\uc9c4 \ubb38\ub9e5\uc5d0\uc11c \ub2e8\uc5b4\uc758 \uc758\ubbf8\ub97c \uc774\ud574\ud558\ub294 \ub370 \uc788\uc5b4 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc778 \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 BERT\uc758 \uc8fc\uc694 \uac1c\ub150\uacfc \ub9c8\uc2a4\ud06c\ub4dc \uc5b8\uc5b4 \ubaa8\ub378(Masked Language Model, MLM)\uc758 \uc6d0\ub9ac\ub97c \uc124\uba85\ud558\uace0, \uc2e4\uc2b5\uc744 \ud1b5\ud574 BERT\ub97c \ud65c\uc6a9\ud558\uc5ec NLP \ud0dc\uc2a4\ud06c\uc5d0 \uc801\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc18c\uac1c\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub525 \ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud558\ub294 \uae30\uacc4 \ud559\uc2b5\uc758 \ud55c \uc7a5\ub974\ub85c, \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ud328\ud134\uacfc \uaddc\uce59\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \ucef4\ud4e8\ud130\uac00 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\uc744 \uc9c0\uce6d\ud569\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \ub525 \ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uc740 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0 \ud601\uc2e0\uc801\uc778 \ubcc0\ud654\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub300\ub7c9\uc758 \ub370\uc774\ud130\uc640 \uac15\ub825\ud55c \ucef4\ud4e8\ud305 \ud30c\uc6cc\uc758 \uacb0\ud569\uc740 NLP \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ube44\uc57d\uc801\uc73c\ub85c \ud5a5\uc0c1\uc2dc\ucf30\uc2b5\ub2c8\ub2e4.<\/p>\n
BERT\ub294 \uad6c\uae00\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc0ac\uc804 \ud6c8\ub828(pre-trained) \uc5b8\uc5b4 \ubaa8\ub378\ub85c, Transformer \uc544\ud0a4\ud14d\ucc98\ub97c \uae30\ubc18\uc73c\ub85c \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. BERT\uc758 \uac00\uc7a5 \ud070 \ud2b9\uc9d5\uc740 \ubb38\ub9e5\uc744 \uc591\ubc29\ud5a5\uc73c\ub85c \uc774\ud574\ud560 \uc218 \uc788\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4. \uc774\ub294 \ub2e8\uc5b4\uac00 \ubb38\uc7a5\uc5d0\uc11c \uac00\uc9c0\ub294 \uc758\ubbf8\ub97c \uc2e4\uc81c \ubb38\ub9e5\uc5d0 \ub530\ub77c \ub2ec\ub77c\uc9c8 \uc218 \uc788\uc74c\uc744 \uc778\uc2dd\ud560 \uc218 \uc788\uac8c \ud574\uc90d\ub2c8\ub2e4. BERT\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ub450 \uac00\uc9c0 \uc8fc\uc694 \uacfc\uc81c\ub97c \ud1b5\ud574 \ud559\uc2b5\ub429\ub2c8\ub2e4:<\/p>\n
MLM\uc758 \uc544\uc774\ub514\uc5b4\ub294 \uc8fc\uc5b4\uc9c4 \ubb38\uc7a5\uc5d0\uc11c \uc77c\ubd80 \ub2e8\uc5b4\ub97c \uac00\ub9ac\uace0, \ubaa8\ub378\uc774 \uadf8 \ub2e8\uc5b4\ub97c \uc608\uce21\ud558\ub3c4\ub85d \ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, “\ub098\ub294 \uc0ac\uacfc\ub97c \uc88b\uc544\ud55c\ub2e4”\ub77c\ub294 \ubb38\uc7a5\uc5d0\uc11c “\uc0ac\uacfc”\ub77c\ub294 \ub2e8\uc5b4\ub97c \ub9c8\uc2a4\ud06c\ud558\uba74 “\ub098\ub294 [MASK]\ub97c \uc88b\uc544\ud55c\ub2e4”\uc640 \uac19\uc740 \ud615\ud0dc\uac00 \ub429\ub2c8\ub2e4. \ubaa8\ub378\uc740 \uc8fc\uc5b4\uc9c4 \ubb38\ub9e5\uc744 \uae30\ubc18\uc73c\ub85c “[MASK]”\uc758 \uac12\uc744 \uc608\uce21\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc29\uc2dd\uc73c\ub85c \ubaa8\ub378\uc740 \ud48d\ubd80\ud55c \ubb38\ub9e5 \uc815\ubcf4\ub97c \ud559\uc2b5\ud558\uace0 \ub2e8\uc5b4 \uac04\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n
NSP \ud0dc\uc2a4\ud06c\ub294 \ubaa8\ub378\uc5d0\uac8c \ub450 \uac1c\uc758 \ubb38\uc7a5\uc774 \uc8fc\uc5b4\uc9c0\uba74 \uc774\ub97c \ud1b5\ud574 \ub450 \ubb38\uc7a5\uc774 \uc2e4\uc81c\ub85c \uc774\uc5b4\uc9c0\ub294\uc9c0 \uc5ec\ubd80\ub97c \ud310\ub2e8\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, “\ub098\ub294 \uc0ac\uacfc\ub97c \uc88b\uc544\ud55c\ub2e4”\ub77c\ub294 \ubb38\uc7a5\uacfc “\uadf8\ub140\ub294 \ub098\uc5d0\uac8c \uc0ac\uacfc\ub97c \uc8fc\uc5c8\ub2e4”\ub77c\ub294 \ubb38\uc7a5\uc744 \ud1b5\ud574 \ub450 \ubb38\uc7a5\uc740 \uc790\uc5f0\uc2a4\ub7fd\uac8c \uc774\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc18\uba74, “\ub098\ub294 \uc0ac\uacfc\ub97c \uc88b\uc544\ud55c\ub2e4”\uc640 “\ud654\ucc3d\ud55c \ub0a0\uc528\uac00 \uc88b\ub2e4”\ub77c\ub294 \ubb38\uc7a5\uc740 \uc11c\ub85c\uc758 \uc5f0\uc18d\uc131\uc744 \uac00\uc9c0\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uc774 \ud0dc\uc2a4\ud06c\ub294 \ubaa8\ub378\uc774 \ubb38\uc7a5 \uac04\uc758 \uad00\uacc4\ub97c \ud3ec\ucc29\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4.<\/p>\n
BERT\ub294 \ub300\ub7c9\uc758 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud574 \uc0ac\uc804 \ud6c8\ub828\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc740 \ub2e4\uc591\ud55c NLP \uc791\uc5c5\uc5d0 Fine-tuning\uc744 \ud1b5\ud574 \uc27d\uac8c \uc801\uc751\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. BERT\uc758 \ud559\uc2b5\uc740 \ub450 \uac00\uc9c0 \uc8fc\uc694 \uc870\uac74\uc744 \ucda9\uc871\ud568\uc73c\ub85c\uc368 \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4:<\/p>\n
\uc774\uc81c BERT\uc758 \uae30\ubcf8 \uac1c\ub150\uc744 \uc774\ud574\ud588\uc73c\ubbc0\ub85c, \uc2e4\uc81c\ub85c BERT\ub97c \uc0ac\uc6a9\ud558\uc5ec NLP \ud0dc\uc2a4\ud06c\ub97c \uc218\ud589\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 Hugging Face\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 BERT\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc0ac\uc804 \ud559\uc2b5 \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ub9cc\ub4e4\uc5b4\uc84c\uc2b5\ub2c8\ub2e4.<\/p>\n
!pip install transformers torch<\/code><\/pre>\n4.2 BERT \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30<\/h3>\nfrom transformers import BertTokenizer, BertForMaskedLM\nimport torch\n\n# BERT \ud1a0\ud06c\ub098\uc774\uc800 \ubc0f \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertForMaskedLM.from_pretrained('bert-base-uncased')\n<\/code><\/pre>\n4.3 \ubb38\uc7a5\uc758 \ub9c8\uc2a4\ud0b9 \ubc0f \uc608\uce21<\/h3>\n
\uc774\uc81c \ubb38\uc7a5\uc744 \ub9c8\uc2a4\ud0b9\ud558\uace0 \ubaa8\ub378\uc744 \ud1b5\ud574 \uc608\uce21\uc744 \uc218\ud589\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
# \uc785\ub825 \ubb38\uc7a5\ninput_text = \"I love [MASK] and [MASK] is my favorite fruit.\"\n\n# \ubb38\uc7a5\uc744 \ud1a0\ud070\ud654\ninput_ids = tokenizer.encode(input_text, return_tensors='pt')\n\n# \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uc5ec \uc608\uce21\nwith torch.no_grad():\n outputs = model(input_ids)\n predictions = outputs[0]\n\n# \uc608\uce21\ub41c \ub9c8\uc2a4\ud06c\uc758 \uc778\ub371\uc2a4\nmasked_index = input_ids[0].tolist().index(tokenizer.mask_token_id)\n\n# \uc608\uce21\ub41c \ub2e8\uc5b4\uc758 \ud1a0\ud070\uc744 \uacc4\uc0b0\npredicted_index = torch.argmax(predictions[0, masked_index]).item()\npredicted_token = tokenizer.decode(predicted_index)\n\nprint(f'\uc608\uce21\ub41c \ub2e8\uc5b4: {predicted_token}')\n<\/code><\/pre>\n\uc704 \ucf54\ub4dc\uc5d0\uc11c\ub294 \uc785\ub825 \ubb38\uc7a5\uc5d0\uc11c \ub450 \uac1c\uc758 \ub2e8\uc5b4\uac00 \ub9c8\uc2a4\ud0b9\ub41c \uc0c1\ud0dc\uc785\ub2c8\ub2e4. \ubaa8\ub378\uc740 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\uace0 \ub9c8\uc2a4\ud0b9\ub41c \ubd80\ubd84\uc5d0 \ub300\ud574 \uc608\uce21\uc744 \uc2dc\ub3c4\ud569\ub2c8\ub2e4.<\/p>\n
4.4 \ub2e4\uc591\ud55c NLP \ud0dc\uc2a4\ud06c\uc5d0 \uc801\uc6a9\ud558\uae30<\/h3>\n
BERT\ub294 \ud14d\uc2a4\ud2b8 \ubd84\ub958, \ubb38\uc11c \uc720\uc0ac\ub3c4 \uacc4\uc0b0, \uac1c\uccb4\uba85 \uc778\uc2dd \ub4f1 \ub2e4\uc591\ud55c NLP \ud0dc\uc2a4\ud06c\uc5d0 \uc801\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uac10\uc815 \ubd84\uc11d\uc744 \uc704\ud574 BERT\ub97c Fine-tuning\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
from transformers import BertForSequenceClassification, Trainer, TrainingArguments\n\n# Fine-tuning\uc744 \uc704\ud55c BERT \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n\n# \ud2b8\ub808\uc774\ub2dd \ub370\uc774\ud130 \uc124\uc815\ntrain_dataset = ... # Your training dataset\ntest_dataset = ... # Your test dataset\n\n# \ud2b8\ub808\uc774\ub2dd \ud30c\ub77c\ubbf8\ud130 \uc124\uc815\ntraining_args = TrainingArguments(\n output_dir='.\/results',\n num_train_epochs=3,\n per_device_train_batch_size=16,\n per_device_eval_batch_size=16,\n warmup_steps=500,\n weight_decay=0.01,\n logging_dir='.\/logs',\n)\n\n# Trainer \uc778\uc2a4\ud134\uc2a4 \uc0dd\uc131\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n eval_dataset=test_dataset,\n)\n\n# \ud2b8\ub808\uc774\ub2dd \uc218\ud589\ntrainer.train()\n<\/code><\/pre>\n5. \uacb0\ub860<\/h2>\n
BERT \ubaa8\ub378\uc740 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \ubc1c\uc804\uc744 \ubcf4\uc5ec\uc8fc\uc5c8\uc73c\uba70, \ub9c8\uc2a4\ud06c\ub4dc \uc5b8\uc5b4 \ubaa8\ub378(Masked Language Model) \uae30\ubc95\uc744 \ud1b5\ud574 \uc8fc\uc5b4\uc9c4 \ubb38\ub9e5\uc5d0\uc11c \ub2e8\uc5b4\uc758 \uc758\ubbf8\ub97c \ub354\uc6b1 \uae4a\uc774 \uc774\ud574\ud558\ub294 \ub370 \uae30\uc5ec\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 BERT\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud559\uc2b5 \ubc29\uc2dd\uc744 \uc124\uba85\ud558\uace0, \uc2e4\uc9c8\uc801\uc778 \uc0ac\ub840\ub97c \ud1b5\ud574 BERT \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 BERT\uc640 \uac19\uc740 \ud601\uc2e0\uc801\uc778 \ubaa8\ub378\ub4e4\uc774 NLP \ubd84\uc57c\uc5d0\uc11c\uc758 \uac00\ub2a5\uc131\uc744 \ub354\uc6b1 \ud655\uc7a5\uc2dc\ud0ac \uac83\uc73c\ub85c \uae30\ub300\ub429\ub2c8\ub2e4.<\/p>\n
6. \ucc38\uace0\ubb38\ud5cc<\/h2>\n\n- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.<\/li>\n
- Hugging Face. (n.d.). Transformers. Retrieved from https:\/\/huggingface.co\/transformers\/<\/a><\/li>\n<\/ul>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"