{"id":25443,"date":"2024-10-26T09:55:11","date_gmt":"2024-10-26T09:55:11","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=25443"},"modified":"2024-11-26T08:01:44","modified_gmt":"2024-11-26T08:01:44","slug":"%eb%94%a5-%eb%9f%ac%eb%8b%9d%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c-%ec%9e%90%ec%97%b0%ec%96%b4-%ec%b2%98%eb%a6%ac-%ec%8b%a4%ec%a0%84-bert-%ec%8b%a4%ec%8a%b5%ed%95%98%ea%b8%b0","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/25443\/","title":{"rendered":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc2e4\uc804! BERT \uc2e4\uc2b5\ud558\uae30"},"content":{"rendered":"<p>\uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP)\ub294 \uc778\uac04 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud558\uae30 \uc704\ud574 \uae30\uacc4 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uacfc \ud1b5\uacc4 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \uae30\uc220\uc785\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\uc744 \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, BERT(Bidirectional Encoder Representations from Transformers)\ub294 NLP \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ub370 \uc788\uc5b4 \ub9e4\uc6b0 \uac15\ub825\ud55c \ubaa8\ub378\ub85c \uc790\ub9ac \uc7a1\uc558\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 BERT\uc758 \uad6c\uc870\uc640 \uc791\ub3d9 \ubc29\uc2dd, \uadf8\ub9ac\uace0 \uc2e4\uc2b5\uc744 \ud1b5\ud574 \uc774\ub97c \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. BERT\ub780 \ubb34\uc5c7\uc778\uac00?<\/h2>\n<p>BERT\ub294 \uad6c\uae00\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc0ac\uc804 \ud6c8\ub828\ub41c \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 \uc591\ubc29\ud5a5 \ucc98\ub9ac(Bidirectional Processing)\uc785\ub2c8\ub2e4. \uc774\ub294 \ubb38\uc7a5\uc758 \uc55e\ub4a4 \uc815\ubcf4\ub97c \ubaa8\ub450 \ud65c\uc6a9\ud558\uc5ec \ub2e8\uc5b4\uc758 \uc758\ubbf8\ub97c \uc774\ud574\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 NLP \ubaa8\ub378\ub4e4\uc740 \uc77c\ubc18\uc801\uc73c\ub85c \ud55c \ubc29\ud5a5\uc73c\ub85c\ub9cc \uc815\ubcf4\ub97c \ucc98\ub9ac\ud588\uc9c0\ub9cc, BERT\ub294 \uc774\ub97c \ud601\uc2e0\uc801\uc73c\ub85c \uac1c\uc120\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>1.1 BERT\uc758 \uad6c\uc870<\/h3>\n<p>BERT\ub294 \uc5ec\ub7ec \uce35\uc758 Transformer \ube14\ub85d\uc73c\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc73c\uba70, \uac01 \ube14\ub85d\uc740 \ub450 \uac00\uc9c0 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\uc778 \uba40\ud2f0\ud5e4\ub4dc \uc5b4\ud150\uc158(Multi-Head Attention)\uacfc \ud53c\ub4dc\ud3ec\uc6cc\ub4dc \uc2e0\uacbd\ub9dd(Feedforward Neural Network)\uc73c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uad6c\uc870 \ub355\ubd84\uc5d0 BERT\ub294 \ub300\ub7c9\uc758 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud560 \uc218 \uc788\uc73c\uba70, \ub2e4\uc591\ud55c NLP \uc791\uc5c5\uc5d0 \uc801\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>1.2 BERT\uc758 \ud6c8\ub828 \ubc29\uc2dd<\/h3>\n<p>BERT\ub294 \ub450 \uac00\uc9c0 \uc8fc\uc694 \ud6c8\ub828 \uc791\uc5c5\uc744 \ud1b5\ud574 \uc0ac\uc804 \ud6c8\ub828\ub429\ub2c8\ub2e4. \uccab \ubc88\uc9f8 \uc791\uc5c5\uc740 &#8216;\ub9c8\uc2a4\ud06c\ub41c \uc5b8\uc5b4 \ubaa8\ub378\ub9c1(Masked Language Model, MLM)&#8217;\uc774\uba70, \ud14d\uc2a4\ud2b8 \ub0b4\uc758 \uc77c\ubd80 \ub2e8\uc5b4\ub97c \ub9c8\uc2a4\ud06c\ud558\uace0 \ubaa8\ub378\uc774 \uc774\ub97c \uc608\uce21\ud558\ub3c4\ub85d \ud6c8\ub828\ud569\ub2c8\ub2e4. \ub450 \ubc88\uc9f8 \uc791\uc5c5\uc740 &#8216;\ub2e4\uc74c \ubb38\uc7a5 \uc608\uce21(Next Sentence Prediction, NSP)&#8217;\uc73c\ub85c, \uc8fc\uc5b4\uc9c4 \ub450 \ubb38\uc7a5\uc774 \uc5f0\uc18d\uc801\uc778\uc9c0 \uc544\ub2cc\uc9c0\ub97c \ud310\ub2e8\ud558\ub3c4\ub85d \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub450 \uac00\uc9c0 \uc791\uc5c5\uc740 BERT\uac00 \ubb38\ub9e5\uc744 \uc798 \uc774\ud574\ud558\ub3c4\ub85d \ub3d5\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>2. BERT\ub97c \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc2e4\uc2b5<\/h2>\n<p>\uc774\ubc88 \uc139\uc158\uc5d0\uc11c\ub294 Python\uc744 \uc774\uc6a9\ud558\uc5ec BERT\ub97c \uc2e4\uc81c\ub85c \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uba3c\uc800, \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4.<\/p>\n<h3>2.1 \ud658\uacbd \uc124\uc815<\/h3>\n<pre><code>\n# \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58\n!pip install transformers\n!pip install torch\n!pip install pandas\n!pip install scikit-learn\n<\/code><\/pre>\n<h3>2.2 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n<p>\uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c\ub294 \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uac00 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \ubcf8 \uc608\uc81c\uc5d0\uc11c\ub294 \ub370\uc774\ud130\uc14b\uc73c\ub85c IMDB \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uae0d\uc815\/\ubd80\uc815 \uac10\uc815\uc744 \ubd84\ub958\ud558\ub294 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uba3c\uc800, \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud558\uace0 \uae30\ubcf8\uc801\uc778 \uc804\ucc98\ub9ac\ub97c \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>\nimport pandas as pd\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ndf = pd.read_csv('https:\/\/datasets.imdbws.com\/imdb.csv', usecols=['review', 'label'])\ndf.columns = ['text', 'label']\ndf['label'] = df['label'].map({'positive': 1, 'negative': 0})\n\n# \ub370\uc774\ud130 \ud655\uc778\nprint(df.head())\n<\/code><\/pre>\n<h3>2.3 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n<p>\ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud55c \ud6c4, \uc6b0\ub9ac\ub294 \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub97c \ud1b5\ud574 BERT \ubaa8\ub378\uc774 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud560 \uac83\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\ub294 \uc8fc\ub85c \ud14d\uc2a4\ud2b8\ub97c \ud1a0\ud070\ud654(tokenization)\ud558\ub294 \uacfc\uc815\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n<pre><code>\nfrom transformers import BertTokenizer\n\n# BERT Tokenizer \ucd08\uae30\ud654\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n\n# \ud1a0\ud070\ud654 \ud568\uc218 \uc815\uc758\ndef tokenize_and_encode(data):\n    return tokenizer(data.tolist(), padding=True, truncation=True, return_tensors='pt')\n\n# \ub370\uc774\ud130 \ud1a0\ud070\ud654\ninputs = tokenize_and_encode(df['text'])\n<\/code><\/pre>\n<h3>2.4 \ubaa8\ub378 \ub85c\ub4dc \ubc0f \ud6c8\ub828<\/h3>\n<p>\uc774\uc81c, BERT \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uc5ec \ud6c8\ub828\uc744 \uc9c4\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4. Hugging Face\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>\nfrom transformers import BertForSequenceClassification, Trainer, TrainingArguments\nimport torch\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n\n# \ud6c8\ub828 \uc778\uc218 \uc815\uc758\ntraining_args = TrainingArguments(\n    output_dir='.\/results',\n    num_train_epochs=3,\n    per_device_train_batch_size=16,\n    logging_dir='.\/logs',\n)\n\n# Trainer \ucd08\uae30\ud654\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=inputs,\n    eval_dataset=None,\n)\n\n# \ubaa8\ub378 \ud6c8\ub828\ntrainer.train()\n<\/code><\/pre>\n<h3>2.5 \uc608\uce21<\/h3>\n<p>\ud6c8\ub828\uc774 \uc644\ub8cc\ub418\uba74, \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ud14d\uc2a4\ud2b8\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac04\ub2e8\ud55c \uc608\uce21 \ud568\uc218\ub97c \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>\ndef predict(text):\n    tokens = tokenizer(text, return_tensors='pt')\n    output = model(**tokens)\n    predicted_label = torch.argmax(output.logits, dim=1).item()\n    return 'positive' if predicted_label == 1 else 'negative'\n\n# \uc0c8\ub85c\uc6b4 \ub9ac\ubdf0 \uc608\uce21\nnew_review = \"This movie was fantastic! I really enjoyed it.\"\nprint(predict(new_review))\n<\/code><\/pre>\n<h2>3. BERT \ubaa8\ub378 \ud29c\ub2dd \ubc0f \uac1c\uc120<\/h2>\n<p>BERT \ubaa8\ub378\uc740 \uae30\ubcf8\uc801\uc73c\ub85c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uc9c0\ub9cc, \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ub354 \ub098\uc740 \uacb0\uacfc\ub97c \uc5bb\uae30 \uc704\ud558\uc5ec \ubaa8\ub378\uc744 \ud29c\ub2dd\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc139\uc158\uc5d0\uc11c\ub294 BERT \ubaa8\ub378\uc744 \ud29c\ub2dd\ud558\ub294 \uba87 \uac00\uc9c0 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3.1 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815<\/h3>\n<p>\ud6c8\ub828 \uc2dc \uc124\uc815\ud558\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud559\uc2b5\ub960(learning rate), \ubc30\uce58 \ud06c\uae30(batch size), \uc5d0\ud3ed(epoch) \uc218 \ub4f1\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc870\uc815\ud558\uc5ec \ucd5c\uc801\uc758 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Grid Search \ub610\ub294 Random Search\uc640 \uac19\uc740 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \ucc3e\ub294 \uac83\ub3c4 \uc88b\uc740 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n<h3>3.2 \ub370\uc774\ud130 \uc99d\uac15<\/h3>\n<p>\ub370\uc774\ud130 \uc99d\uac15(data augmentation)\uc740 \ud559\uc2b5 \ub370\uc774\ud130\uc758 \uc591\uc744 \ub298\ub824 \ubaa8\ub378\uc758 \uc77c\ubc18\ud654\ub97c \ub3c4\ubaa8\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ud2b9\ud788 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c\ub294 \ubb38\uc7a5\uc758 \ub2e8\uc5b4\ub97c \uad50\uccb4(replace)\ud558\uac70\ub098 \uc870\ud569(combine)\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \ub370\uc774\ud130\ub97c \uc99d\uac15\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3.3 \uc804\uc774 \ud559\uc2b5(Fine-tuning)<\/h3>\n<p>\uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ud2b9\uc815 \ub370\uc774\ud130\uc14b\uc5d0 \ub9de\uac8c Fine-tuning \ud568\uc73c\ub85c\uc368 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \ub808\uc774\uc5b4\ub97c \ub3d9\uacb0(freeze)\ud558\uac70\ub098 \ubcc0\ud654\ub97c \uc8fc\uc5b4 \ud2b9\uc815 \uc791\uc5c5\uc758 \ud559\uc2b5\uc744 \ub354\uc6b1 \ud6a8\uacfc\uc801\uc73c\ub85c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>4. \uacb0\ub860<\/h2>\n<p>\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 BERT\ub97c \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ucd08\ubd80\ud130 \uc2e4\uc804\uc801\uc778 \ucf54\ub4dc \uc608\uc81c\uae4c\uc9c0 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. BERT\ub294 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \uc790\ub791\ud558\ub294 \ubaa8\ub378\uc774\uba70, \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc791\uc5c5\uc5d0 \uc751\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ucd94\uac00\uc801\uc73c\ub85c, \ud544\uc694\uc5d0 \ub530\ub77c \ubaa8\ub378\uc744 \ud29c\ub2dd\ud558\uace0 \uac1c\uc120\ud558\ub294 \uacfc\uc815\ub3c4 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\ub4e4\uc774 BERT\ub97c \ud65c\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c NLP \uc791\uc5c5\uc744 \uc218\ud589\ud558\uae38 \uae30\ub300\ud569\ub2c8\ub2e4!<\/p>\n<h2>5. \ucc38\uace0 \uc790\ub8cc<\/h2>\n<ul>\n<li>Devlin, J., Chang, M. W., Lee, K., &amp; Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.<\/li>\n<li>Hugging Face, Transformers Documentation: <a href=\"https:\/\/huggingface.co\/transformers\/\">https:\/\/huggingface.co\/transformers\/<\/a><\/li>\n<li>IMDB Dataset: <a href=\"https:\/\/ai.stanford.edu\/~amaas\/data\/sentiment\/\">https:\/\/ai.stanford.edu\/~amaas\/data\/sentiment\/<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP)\ub294 \uc778\uac04 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud558\uae30 \uc704\ud574 \uae30\uacc4 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uacfc \ud1b5\uacc4 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \uae30\uc220\uc785\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\uc744 \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, BERT(Bidirectional Encoder Representations from Transformers)\ub294 NLP \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ub370 \uc788\uc5b4 \ub9e4\uc6b0 \uac15\ub825\ud55c \ubaa8\ub378\ub85c \uc790\ub9ac \uc7a1\uc558\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 BERT\uc758 \uad6c\uc870\uc640 \uc791\ub3d9 \ubc29\uc2dd, \uadf8\ub9ac\uace0 \uc2e4\uc2b5\uc744 \ud1b5\ud574 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/25443\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc2e4\uc804! BERT \uc2e4\uc2b5\ud558\uae30&#8221;<\/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":[16],"tags":[],"class_list":["post-25443","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc2e4\uc804! BERT \uc2e4\uc2b5\ud558\uae30 - \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\/25443\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc2e4\uc804! 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