{"id":29540,"date":"2024-10-28T01:55:21","date_gmt":"2024-10-28T01:55:21","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29540"},"modified":"2024-11-26T06:52:21","modified_gmt":"2024-11-26T06:52:21","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%82%ac%ec%a0%84%ed%95%99%ec%8a%b5-%eb%aa%a8%eb%8d%b8%ec%9d%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29540\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378\uc744 \ud30c\uc774\ud504\ub77c\uc778\uc5d0 \ubd88\ub7ec\uc624\uae30"},"content":{"rendered":"

<\/p>\n

1. \uc11c\ub860<\/h2>\n

\ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5 \ubc0f \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c\ub294 BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc774 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. BERT\ub294 \uad6c\uae00\uc5d0\uc11c \uac1c\ubc1c\ud55c \ud2b8\ub79c\uc2a4\ud3ec\uba38 \uae30\ubc18\uc758 \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378\ub85c, \ub2e4\uc591\ud55c NLP \uacfc\uc81c\uc5d0\uc11c \ucd5c\ucca8\ub2e8 \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uacfc \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud574 BERT \ubaa8\ub378\uc744 \uc27d\uac8c \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

2. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h2>\n

\uba3c\uc800, \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \ud30c\uc774\uc36c \ud328\ud0a4\uc9c0 \uad00\ub9ac\uc790\uc778 pip\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

pip install transformers<\/code><\/pre>\n
3. BERT \ubaa8\ub378 \uc774\ud574\ud558\uae30<\/h2>\n
BERT\ub294 \uc785\ub825 \ubb38\uc7a5\uc758 \uc591 \ubc29\ud5a5 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\uc5ec \ub354 \ub098\uc740 \uc790\uc5f0\uc5b4 \uc774\ud574\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \uc989, \ubb38\uc7a5\uc744 \uc67c\ucabd\uc5d0\uc11c \uc624\ub978\ucabd\uc73c\ub85c, \ub610\ub294 \uc624\ub978\ucabd\uc5d0\uc11c \uc67c\ucabd\uc73c\ub85c \uc77d\uc744 \uc218 \uc788\uc5b4 \ubb38\ub9e5 \uc815\ubcf4\ub97c \ub354 \ud48d\ubd80\ud558\uac8c \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4. BERT \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30<\/h2>\n
\ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc624\ub294 \uacfc\uc815\uc740 \ub9e4\uc6b0 \uac04\ub2e8\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc740 BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc624\uae30 \uc704\ud55c \uae30\ubcf8 \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n
\nfrom transformers import BertTokenizer, BertForMaskedLM\nimport torch\n\n# BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800 \ubd88\ub7ec\uc624\uae30\nmodel_name = 'bert-base-uncased'\ntokenizer = BertTokenizer.from_pretrained(model_name)\nmodel = BertForMaskedLM.from_pretrained(model_name)\n\n# \uc608\uc81c \ubb38\uc7a5\ntext = \"Hello, my name is [MASK].\"\ninputs = tokenizer(text, return_tensors=\"pt\")\n\n# \uc608\uce21\nwith torch.no_grad():\n    outputs = model(**inputs)\n    predictions = outputs.logits\n    predicted_index = torch.argmax(predictions, dim=-1)\n    predicted_token = tokenizer.decode(predicted_index[0, inputs['input_ids'].tolist().index(tokenizer.mask_token_id)])\n\nprint(f\"\uc608\uce21\ub41c \ub2e8\uc5b4: {predicted_token}\")\n    <\/code><\/pre>\n
4.1 \ucf54\ub4dc \uc124\uba85<\/h3>\n
\uc704 \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uacfc\uc815\uc73c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n