{"id":29552,"date":"2024-10-28T01:55:25","date_gmt":"2024-10-28T01:55:25","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29552"},"modified":"2024-11-26T06:52:17","modified_gmt":"2024-11-26T06:52:17","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%95%99%ec%83%81%eb%b8%94-%ed%95%99%ec%8a%b5-%ec%bb%a4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29552\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 – \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc138\ud2b8 \uc815\uc758"},"content":{"rendered":"

<\/p>\n

\uc11c\ub860<\/h2>\n

\n \ub525\ub7ec\ub2dd\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc744 \uc77c\uc73c\ucf1c\uc654\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc740 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud558\uba70, \ub2e4\uc591\ud55c NLP \uacfc\uc81c\uc5d0\uc11c \ucd5c\ucca8\ub2e8 \uc131\ub2a5\uc744 \uae30\ub85d\ud588\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc758 \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uad6c\ud604\ud558\uace0, \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc14b\uc744 \uc815\uc758\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

1. \ud5c8\uae45\ud398\uc774\uc2a4 Transformers \uc18c\uac1c<\/h2>\n

\n \ud5c8\uae45\ud398\uc774\uc2a4\ub294 NLP \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ub2e4\uc591\ud55c \uace0\uae09 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ub9cc\ub4ed\ub2c8\ub2e4. \ud2b9\ud788 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 BERT, GPT-2, T5 \ub4f1 \uc5ec\ub7ec \ucd5c\uc2e0 \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ud574\uc90d\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uba74 \ubcf5\uc7a1\ud55c \uc2e0\uacbd\ub9dd \uad6c\uc131 \uacfc\uc815\uc744 \ub2e8\uc21c\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

1.1 BERT\ub780 \ubb34\uc5c7\uc778\uac00?<\/h3>\n

\n BERT\ub294 \uc591\ubc29\ud5a5 Transformer \uc778\ucf54\ub354\ub85c\uc11c, \ubb38\uc7a5 \ub0b4\uc5d0\uc11c\uc758 \ub2e8\uc5b4 \uac04\uc758 \uad00\uacc4\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ud30c\uc545\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. BERT\ub294 \ub450 \uac00\uc9c0 \uc8fc\uc694 \ub2e8\uacc4\ub85c \ud559\uc2b5\ub429\ub2c8\ub2e4: \ub9c8\uc2a4\ud0b9 \ub41c \uc5b8\uc5b4 \ubaa8\ub378\ub9c1(MLM)\uacfc \ub2e4\uc74c \ubb38\uc7a5 \uc608\uce21(NSP)\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud559\uc2b5 \ubc29\uc2dd \ub355\ubd84\uc5d0 BERT\ub294 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\uace0 \ub2e4\uc591\ud55c NLP \uacfc\uc81c\uc5d0\uc11c \uc6b0\uc218\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4.\n <\/p>\n

2. \uc559\uc0c1\ube14 \ud559\uc2b5\uc758 \uac1c\ub150<\/h2>\n

\n \uc559\uc0c1\ube14 \ud559\uc2b5(Ensemble Learning)\uc740 \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \uacb0\ud569\ud574 \ub354 \ub098\uc740 \uc608\uce21 \uc131\ub2a5\uc744 \ub04c\uc5b4\ub0b4\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \ub2e8\uc77c \ubaa8\ub378\uc774 \uac00\uc9c0\ub294 \ud3b8\ud5a5\uc744 \uc904\uc774\uace0, \ubaa8\ub378\uc758 \ub2e4\uc591\uc131\uc744 \ud1b5\ud574 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \uc559\uc0c1\ube14 \uae30\ubc95\uc5d0\ub294 \ubc30\uae45(Bagging)\uacfc \ubd80\uc2a4\ud305(Boosting) \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4. BERT \ubaa8\ub378\uc758 \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \ud1b5\ud574 \ub2e4\uc591\ud55c \ubaa8\ub378\uc758 \uac15\uc810\uc744 \uacb0\ud569\ud574\ubcfc \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n

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

\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 Python\uacfc Hugging Face Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud558\uae30 \uc704\ud574 \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \ud130\ubbf8\ub110\uc5d0 \uc785\ub825\ud569\ub2c8\ub2e4.\n <\/p>\n

pip install transformers datasets torch<\/code><\/pre>\n
4. \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc14b \uc815\uc758\ud558\uae30<\/h2>\n
\n        NLP \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c\ub294 \uc801\uc808\ud55c \ud615\uc2dd\uc758 \ub370\uc774\ud130\uc14b\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ubcf8 \uc139\uc158\uc5d0\uc11c\ub294 \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc14b\uc744 \uc815\uc758\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
4.1 \ub370\uc774\ud130\uc14b \ud615\uc2dd<\/h3>\n
\n        \ub370\uc774\ud130\uc14b\uc740 \uc77c\ubc18\uc801\uc73c\ub85c \ud14d\uc2a4\ud2b8\uc640 \ud574\ub2f9 \ub808\uc774\ube14\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc6b0\ub9ac\uac00 \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\uc14b\uc740 CSV \ud615\uc2dd\uc73c\ub85c \uc900\ube44\ud560 \uac83\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub2e4\uc74c\uacfc \uac19\uc740 \ud615\uc2dd\uc774\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n    <\/p>\n
\n    text,label\n    \"\uc774 \uc601\ud654\ub294 \uc815\ub9d0 \uc7ac\ubbf8\uc788\uc5c8\uc2b5\ub2c8\ub2e4.\",1\n    \"\ubcc4\ub85c\uc600\ub2e4.\",0\n    <\/code><\/pre>\n
4.2 \ub370\uc774\ud130 \ub85c\ub4dc\ud558\uae30<\/h3>\n
\n        \uc774\uc81c \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\ub294 \ucf54\ub4dc\ub97c \uc791\uc131\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. Hugging Face\uc758 datasets<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ud3b8\ud558\uac8c \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\nimport pandas as pd\nfrom datasets import Dataset\n\n# CSV \ud30c\uc77c\uc5d0\uc11c \ub370\uc774\ud130 \ub85c\ub4dc\ud558\uae30\ndata = pd.read_csv('custom_dataset.csv')\ndataset = Dataset.from_pandas(data)\n    <\/code><\/pre>\n
5. BERT \ubaa8\ub378 \uad6c\uc131 \ubc0f \ud6c8\ub828<\/h2>\n
\n        \ub370\uc774\ud130\uc14b\uc774 \uc900\ube44\ub418\uc5c8\uc73c\ub2c8 \uc774\uc81c BERT \ubaa8\ub378\uc744 \uad6c\uc131\ud558\uace0 \ud6c8\ub828\uc2dc\ud0a4\ub294 \ub2e8\uacc4\ub85c \ub118\uc5b4\uac00\uaca0\uc2b5\ub2c8\ub2e4. Hugging Face\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc190\uc27d\uac8c BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
5.1 BERT \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30<\/h3>\n
\n        \ub2e4\uc74c \ucf54\ub4dc\ub294 BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800\ub97c \ubd88\ub7ec\uc624\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.\n    <\/p>\n
\nfrom transformers import BertTokenizer, BertForSequenceClassification\n\n# \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800 \ubd88\ub7ec\uc624\uae30\nmodel_name = 'bert-base-uncased'\ntokenizer = BertTokenizer.from_pretrained(model_name)\nmodel = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)\n    <\/code><\/pre>\n
5.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\n        BERT \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uae30 \uc804\uc5d0 \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub97c \uc218\ud589\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc785\ub825 \ud14d\uc2a4\ud2b8\ub97c \ud1a0\ud06c\ub098\uc774\uc988\ud558\uace0, \uc801\uc808\ud55c \ud615\ud0dc\ub85c \ud328\ub529 \ubc0f \ud2b8\ub801\ud06c\ub97c \ud558\uae30 \uc704\ud574 \uc544\ub798 \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\ndef preprocess_function(examples):\n    return tokenizer(examples['text'], padding='max_length', truncation=True)\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uc218\ud589\ntokenized_dataset = dataset.map(preprocess_function, batched=True)\n    <\/code><\/pre>\n
5.3 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
\n        \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uac00 \ub05d\ub0ac\uc73c\ub2c8 \uc774\uc81c \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0ac \uc900\ube44\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. trainer API\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud6c8\ub828 \ubc0f \ud3c9\uac00\ub97c \uc218\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\nfrom transformers import Trainer, TrainingArguments\n\n# \ud6c8\ub828 \uc778\uc790 \uc124\uc815\ntraining_args = TrainingArguments(\n    output_dir='.\/results',\n    evaluation_strategy='epoch',\n    learning_rate=2e-5,\n    per_device_train_batch_size=16,\n    num_train_epochs=3,\n)\n\n# Trainer \uac1d\uccb4 \uc0dd\uc131\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=tokenized_dataset,\n)\n\n# \ubaa8\ub378 \ud6c8\ub828\ntrainer.train()\n    <\/code><\/pre>\n
6. \uc559\uc0c1\ube14 \ubaa8\ub378 \uad6c\ud604<\/h2>\n
\n        \uc5ec\ub7ec \uac1c\uc758 BERT \ubaa8\ub378\uc744 \uc559\uc0c1\ube14\ud558\uc5ec \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uac01 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uacb0\ud569\ud558\uc5ec \ucd5c\uc885 \uc608\uce21\uc744 \ub3c4\ucd9c\ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub450 \uac1c \uc774\uc0c1\uc758 \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uace0, \uadf8 \uacb0\uacfc\ub97c \uacb0\ud569\ud574\ubd05\uc2dc\ub2e4.\n    <\/p>\n
6.1 \uc5ec\ub7ec \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
\n# BERT \ubaa8\ub378 \ub450 \uac1c \ud6c8\ub828\nmodel1 = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)\nmodel2 = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)\n\n# \uac01\uac01 \ud6c8\ub828 \uc218\ud589\ntrainer1 = Trainer(\n    model=model1,\n    args=training_args,\n    train_dataset=tokenized_dataset,\n)\n\ntrainer2 = Trainer(\n    model=model2,\n    args=training_args,\n    train_dataset=tokenized_dataset,\n)\n\ntrainer1.train()\ntrainer2.train()\n    <\/code><\/pre>\n
6.2 \uc559\uc0c1\ube14 \uc608\uce21 \uc218\ud589\ud558\uae30<\/h3>\n
\n        \ub450 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \ud3c9\uade0\ud558\uc5ec \uc559\uc0c1\ube14 \uc608\uce21 \uacb0\uacfc\ub97c \ub3c4\ucd9c\ud569\ub2c8\ub2e4.\n    <\/p>\n
\nimport numpy as np\n\n# \uc608\uce21 \uc218\ud589\npreds1 = trainer1.predict(tokenized_dataset)['logits']\npreds2 = trainer2.predict(tokenized_dataset)['logits']\n\n# \uc559\uc0c1\ube14 \uc608\uce21 \uc218\ud589\nfinal_preds = (preds1 + preds2) \/ 2\nfinal_predictions = np.argmax(final_preds, axis=1)\n    <\/code><\/pre>\n
7. \uacb0\uacfc \ud3c9\uac00<\/h2>\n
\n        \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \uac83\uc740 \uc911\uc694\ud558\uba70, \uc6b0\ub9ac\ub294 \uc815\ud655\ub3c4\uc640 F1 \uc810\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\nfrom sklearn.metrics import accuracy_score, f1_score\n\n# \ub77c\ubca8\uacfc \uc608\uce21 \uacb0\uacfc\ub97c \ube44\uad50\ud558\uc5ec \uc131\ub2a5 \ud3c9\uac00\ntrue_labels = tokenized_dataset['label']\naccuracy = accuracy_score(true_labels, final_predictions)\nf1 = f1_score(true_labels, final_predictions)\n\nprint(f'Accuracy: {accuracy}')\nprint(f'F1 Score: {f1}')\n    <\/code><\/pre>\n
\uacb0\ub860<\/h2>\n
\n        \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 \ubaa8\ub378\uc758 \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uc218\ud589\ud558\ub294 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc14b \uc815\uc758, \ubaa8\ub378 \uad6c\uc131 \ubc0f \ud6c8\ub828, \uc559\uc0c1\ube14 \uae30\ubc95\uc5d0 \ub300\ud574 \ubc30\uc6b0\uba70 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ubc29\ubc95\uc744 \uc775\ud614\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc744 \ud1b5\ud574 \ub3c5\uc790 \ubd84\ub4e4\uc774 BERT \ubaa8\ub378\uc758 \uc0ac\uc6a9\ubc95\uacfc \uc559\uc0c1\ube14 \ud559\uc2b5\uc758 \uac1c\ub150\uc744 \ubcf4\ub2e4 \uae4a\uac8c \uc774\ud574\ud560 \uc218 \uc788\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.\n    <\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc11c\ub860 \ub525\ub7ec\ub2dd\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc744 \uc77c\uc73c\ucf1c\uc654\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc740 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud558\uba70, \ub2e4\uc591\ud55c NLP \uacfc\uc81c\uc5d0\uc11c \ucd5c\ucca8\ub2e8 \uc131\ub2a5\uc744 \uae30\ub85d\ud588\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc758 \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uad6c\ud604\ud558\uace0, \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc14b\uc744 \uc815\uc758\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud5c8\uae45\ud398\uc774\uc2a4 Transformers \uc18c\uac1c \ud5c8\uae45\ud398\uc774\uc2a4\ub294 NLP \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 … \ub354 \ubcf4\uae30 “\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 – \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc138\ud2b8 \uc815\uc758”<\/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-29552","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 \uc559\uc0c1\ube14 \ud559\uc2b5 - \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc138\ud2b8 \uc815\uc758 - \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\/29552\/\" \/>\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 \uc559\uc0c1\ube14 \ud559\uc2b5 - \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc138\ud2b8 \uc815\uc758 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc11c\ub860 \ub525\ub7ec\ub2dd\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc744 \uc77c\uc73c\ucf1c\uc654\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc740 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud558\uba70, \ub2e4\uc591\ud55c NLP \uacfc\uc81c\uc5d0\uc11c \ucd5c\ucca8\ub2e8 \uc131\ub2a5\uc744 \uae30\ub85d\ud588\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc758 \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uad6c\ud604\ud558\uace0, \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc14b\uc744 \uc815\uc758\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud5c8\uae45\ud398\uc774\uc2a4 Transformers \uc18c\uac1c \ud5c8\uae45\ud398\uc774\uc2a4\ub294 NLP \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 … \ub354 \ubcf4\uae30 "\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 – \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc138\ud2b8 \uc815\uc758"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29552\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T01:55:25+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:52:17+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/29552\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29552\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 – \ucee4\uc2a4\ud140 \ub370\uc774\ud130\uc138\ud2b8 \uc815\uc758\",\"datePublished\":\"2024-10-28T01:55:25+00:00\",\"dateModified\":\"2024-11-26T06:52:17+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29552\/\"},\"wordCount\":55,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud65c\uc6a9\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/29552\/\",\"url\":\"https:\/\/atmokpo.com\/w\/29552\/\",\"name\":\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud559\uc2b5 - 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