{"id":29532,"date":"2024-10-28T01:55:19","date_gmt":"2024-10-28T01:55:19","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29532"},"modified":"2024-11-26T06:52:23","modified_gmt":"2024-11-26T06:52:23","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-%eb%8f%84%ed%81%90%eb%a8%bc%ed%8a%b8-%eb%b2%a1%ed%84%b0-%ed%91%9c","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29532\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604 \ucd94\ucd9c"},"content":{"rendered":"

<\/p>\n

\uc548\ub155\ud558\uc138\uc694! \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face)\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud558\uc5ec BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc744 \ud65c\uc6a9\ud574 \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604\uc744 \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. BERT\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c \uc5ec\ub7ec \uc791\uc5c5\uc5d0 \uc804\ubc29\uc704\uc801\uc73c\ub85c \ud65c\uc6a9\ub418\uace0 \uc788\ub294 \uac15\ub825\ud55c \uc5b8\uc5b4 \ubaa8\ub378\uc785\ub2c8\ub2e4.<\/p>\n

1. BERT \uc18c\uac1c<\/h2>\n

BERT\ub294 2018\ub144 \uad6c\uae00\uc774 \ubc1c\ud45c\ud55c \ubaa8\ub378\ub85c, \uc790\uc5f0\uc5b4 \uc774\ud574(Task)\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4. BERT\ub294 \uc591\ubc29\ud5a5\uc801\uc73c\ub85c \ubb38\ub9e5\uc744 \uace0\ub824\ud558\uc5ec \ub2e8\uc5b4\ub97c \uc774\ud574\ud560 \uc218 \uc788\ub3c4\ub85d \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \ud14d\uc2a4\ud2b8\uc758 \uc758\ubbf8\ub97c \uc880 \ub354 \uae4a\uc774 \uc774\ud574\ud558\uae30 \uc704\ud574 ‘Masked Language Model’\uacfc ‘Next Sentence Prediction’\uc774\ub77c\ub294 \ub450 \uac00\uc9c0 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\ub429\ub2c8\ub2e4.<\/p>\n

1.1 BERT\uc758 \ub3d9\uc791 \uc6d0\ub9ac<\/h3>\n

BERT\ub294 \uc785\ub825 \ubb38\uc7a5\uc5d0\uc11c \ubb34\uc791\uc704\ub85c \uba87 \uac1c\uc758 \ub2e8\uc5b4\ub97c \uac00\ub824(\ub9c8\uc2a4\ud0b9) \uc774\ub97c \ub9de\ucd94\ub294 \ubc29\uc2dd\uc73c\ub85c \ud559\uc2b5\ub429\ub2c8\ub2e4. \uadf8\ub7f0 \ub2e4\uc74c, \ubb38\uc7a5\uc758 \ub2e4\uc74c \ubb38\uc7a5\uc774 \uc8fc\uc5b4\uc84c\uc744 \ub54c, \ud604\uc7ac \ubb38\uc7a5\uc774 \uadf8 \ub2e4\uc74c \ubb38\uc7a5\uacfc \uc5b4\ub5a4 \uad00\uacc4\uc778\uc9c0 \uc608\uce21\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uacfc\uc815\uc744 \ud1b5\ud574 \ubb38\ub9e5\uc758 \uc758\ubbf8\ub97c \ub354 \uc798 \uc774\ud574\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n

2. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc18c\uac1c<\/h2>\n

\ud5c8\uae45\ud398\uc774\uc2a4\ub294 AI \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790\ub4e4\uc774 \uc27d\uac8c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ub2e4\uc591\ud55c API\uc640 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. transformers<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uba74 BERT\ub97c \ud3ec\ud568\ud55c \ub2e4\uc591\ud55c \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 \uc7a5\uc810\uc740 \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \uc81c\uacf5\ud55c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \ub530\ub77c\uc11c, \ubaa8\ub378\uc744 \ucc98\uc74c\ubd80\ud130 \ud559\uc2b5\ud560 \ud544\uc694 \uc5c6\uc774 \ub2e4\uc591\ud55c \uc791\uc5c5\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

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

\uba3c\uc800, \ud30c\uc774\uc36c \ud658\uacbd\uc5d0 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \uc785\ub825\ud558\uc5ec \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

pip install transformers torch<\/code><\/pre>\n
4. BERT \ubaa8\ub378 \ub85c\ub4dc\ud558\uae30<\/h2>\n
\uc774\uc81c BERT \ubaa8\ub378\uc744 \ub85c\ub4dc\ud574\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800\ub97c \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc2e4\ud589\ud574 \ubcf4\uc138\uc694:<\/p>\n
from transformers import BertModel, BertTokenizer\n\n# \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800 \ub85c\ub4dc\nmodel_name = 'bert-base-uncased'\ntokenizer = BertTokenizer.from_pretrained(model_name)\nmodel = BertModel.from_pretrained(model_name)<\/code><\/pre>\n
5. \ub3c4\ucfe0\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604 \ucd94\ucd9c\ud558\uae30<\/h2>\n
\uc774\uc81c \uc2e4\uc81c\ub85c \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604\uc744 \ucd94\ucd9c\ud574\ubd05\uc2dc\ub2e4. BERT\uc758 \uc785\ub825\uc740 \ud1a0\ud070\ud654\ub41c \ud615\ud0dc\ub85c \uc8fc\uc5b4\uc838\uc57c \ud558\uba70, \ud1a0\ud06c\ub098\uc774\uc800\ub97c \uc774\uc6a9\ud574 \ubb38\uc7a5\uc744 \ud1a0\ud070\ud654\ud55c \ud6c4 \ud150\uc11c \ud615\ud0dc\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798\ub294 \uc8fc\uc5b4\uc9c4 \ubb38\uc7a5\uc744 BERT\ub97c \ud1b5\ud574 \ubca1\ud130 \ud45c\ud604\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n
# \uc608\uc81c \ubb38\uc7a5\ndocument = \"\ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4.\"\n\n# \ubb38\uc7a5 \ud1a0\ud070\ud654\ninputs = tokenizer(document, return_tensors='pt')\n\n# \ubaa8\ub378\uc744 \ud1b5\ud574 \ubca1\ud130 \ud45c\ud604 \ucd94\ucd9c\nwith torch.no_grad():\n    outputs = model(**inputs)\n\n# \ub9c8\uc9c0\ub9c9 \uc740\ub2c9 \uc0c1\ud0dc(hidden state) \uac00\uc838\uc624\uae30\nlast_hidden_states = outputs.last_hidden_state\n\n# [CLS] \ud1a0\ud070\uc758 \ubca1\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604\ndocument_vector = last_hidden_states[0][0]\nprint(document_vector.shape)<\/code><\/pre>\n
5.1 \ubb38\uc11c \ubca1\ud130\uc758 \uc758\ubbf8<\/h3>\n
\uc704 \ucf54\ub4dc\ub294 \uc785\ub825 \ubb38\uc7a5\uc758 \ub9c8\uc9c0\ub9c9 \uc740\ub2c9 \uc0c1\ud0dc(hidden state)\ub97c \ucd9c\ub825\ud569\ub2c8\ub2e4. BERT \ubaa8\ub378\uc5d0\uc11c [CLS] \ud1a0\ud070\uc740 \ubb38\uc11c \uc804\uccb4\ub97c \ub098\ud0c0\ub0b4\ub294 \ubca1\ud130\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774 \ubca1\ud130\ub294 \ubb38\uc11c\uc758 \uc758\ubbf8\ub97c \ud3ec\uad04\uc801\uc73c\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6. \uc5ec\ub7ec \ubb38\uc11c\uc5d0\uc11c \ubca1\ud130 \ud45c\ud604 \ucd94\ucd9c\ud558\uae30<\/h2>\n
\ub2e4\uc591\ud55c \ubb38\uc11c\ub4e4\uc758 \ubca1\ud130 \ud45c\ud604\uc744 \ucd94\ucd9c\ud558\ub824\uba74, \uc608\uc81c \ubb38\uc7a5\uc744 \ub9ac\uc2a4\ud2b8\ub85c \uc0dd\uc131\ud558\uace0 \ubc18\ubcf5\ubb38\uc744 \ud1b5\ud574 \uac01\uac01\uc758 \ubb38\uc7a5\uc5d0 \ub300\ud574 \ubca1\ud130\ub97c \ucd94\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
# \uc5ec\ub7ec \ubb38\uc11c\uc758 \uc608\uc2dc\ndocuments = [\n    \"\ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4.\",\n    \"BERT\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \ud601\uc2e0\uc801\uc778 \ubaa8\ub378\uc785\ub2c8\ub2e4.\",\n    \"\ub525\ub7ec\ub2dd\uc740 \uc5ec\ub7ec \ubd84\uc57c\uc5d0 \uc801\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\"\n]\n\ndocument_vectors = []\n\nfor doc in documents:\n    inputs = tokenizer(doc, return_tensors='pt')\n    with torch.no_grad():\n        outputs = model(**inputs)\n    document_vector = outputs.last_hidden_state[0][0]\n    document_vectors.append(document_vector)\n\n# Document \ubca1\ud130 \ucd9c\ub825\nfor i, vec in enumerate(document_vectors):\n    print(f\"Document {i+1} Vector: {vec.shape}\")<\/code><\/pre>\n
7. \ubb38\uc11c \ubca1\ud130 \ud65c\uc6a9\ud558\uae30<\/h2>\n
\ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604\uc740 \uc5ec\ub7ec \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc791\uc5c5\uc5d0\uc11c \uc720\uc6a9\ud558\uac8c \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ubb38\uc11c \uc720\uc0ac\ub3c4 \uce21\uc815, \ud074\ub7ec\uc2a4\ud130\ub9c1, \ubd84\ub958 \ub4f1 \ub2e4\uc591\ud55c \uc791\uc5c5\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubca1\ud130\uc758 \ub0b4\uc801(inner product)\uc73c\ub85c \ub450 \ubca1\ud130 \uac04 \uc720\uc0ac\ub3c4\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7.1 \ubb38\uc11c \uc720\uc0ac\ub3c4 \uce21\uc815 \uc608\uc81c<\/h3>\n
from sklearn.metrics.pairwise import cosine_similarity\nimport numpy as np\n\n# Numpy \ubc30\uc5f4\ub85c \ubcc0\ud658\ndocument_vectors_np = np.array([vec.numpy() for vec in document_vectors])\n\n# \ucf54\uc0ac\uc778 \uc720\uc0ac\ub3c4 \uacc4\uc0b0\nsimilarity_matrix = cosine_similarity(document_vectors_np)\n\nprint(\"\ubb38\uc11c \uc720\uc0ac\ub3c4 \ud589\ub82c:\")\nprint(similarity_matrix)<\/code><\/pre>\n
8. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uae00\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\ub85c \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604\uc744 \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. BERT \ubaa8\ub378\uc744 \ud1b5\ud574 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uc5ec\ub7ec \uc791\uc5c5\uc5d0\uc11c \ud6a8\uacfc\uc801\uc73c\ub85c \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \ubca1\ud130\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc74c\uc744 \ubcf4\uc5ec\ub4dc\ub838\uc2b5\ub2c8\ub2e4. \uc774 \ubca1\ud130\ub294 \ub2e4\uc591\ud55c NLP \uc5b4\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0\uc11c \uc720\uc6a9\ud558\uac8c \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub525\ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \ubc1c\uc804\uc5d0 \ub530\ub77c \uc55e\uc73c\ub85c\ub3c4 \ub9ce\uc740 \uc5f0\uad6c\uc640 \uad00\uc2ec\uc774 \ud544\uc694\ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. BERT\uc640 \uac19\uc740 \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354 \ub9ce\uc740 \ud765\ubbf8\ub85c\uc6b4 \ud504\ub85c\uc81d\ud2b8\uc640 \uc751\uc6a9\uc744 \uc2dc\ub3c4\ud574\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4. \uac10\uc0ac\ud569\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc548\ub155\ud558\uc138\uc694! \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face)\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud558\uc5ec BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc744 \ud65c\uc6a9\ud574 \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604\uc744 \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. BERT\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c \uc5ec\ub7ec \uc791\uc5c5\uc5d0 \uc804\ubc29\uc704\uc801\uc73c\ub85c \ud65c\uc6a9\ub418\uace0 \uc788\ub294 \uac15\ub825\ud55c \uc5b8\uc5b4 \ubaa8\ub378\uc785\ub2c8\ub2e4. 1. BERT \uc18c\uac1c BERT\ub294 2018\ub144 \uad6c\uae00\uc774 \ubc1c\ud45c\ud55c \ubaa8\ub378\ub85c, \uc790\uc5f0\uc5b4 \uc774\ud574(Task)\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4. BERT\ub294 \uc591\ubc29\ud5a5\uc801\uc73c\ub85c \ubb38\ub9e5\uc744 \uace0\ub824\ud558\uc5ec \ub2e8\uc5b4\ub97c \uc774\ud574\ud560 \uc218 … \ub354 \ubcf4\uae30 “\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604 \ucd94\ucd9c”<\/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-29532","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 \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604 \ucd94\ucd9c - \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\/29532\/\" \/>\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 \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604 \ucd94\ucd9c - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc548\ub155\ud558\uc138\uc694! \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face)\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud558\uc5ec BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc744 \ud65c\uc6a9\ud574 \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604\uc744 \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 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