{"id":30052,"date":"2024-10-28T03:19:21","date_gmt":"2024-10-28T03:19:21","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30052"},"modified":"2024-11-26T06:50:15","modified_gmt":"2024-11-26T06:50:15","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-%eb%b2%84%ed%8a%b8bert","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30052\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubc84\ud2b8(BERT)"},"content":{"rendered":"<p><body><\/p>\n<p>\ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \ubc1c\uc804\uc740 \ucd5c\uadfc NLP(\uc790\uc5f0\uc5b4 \ucc98\ub9ac) \ubd84\uc57c\uc5d0\uc11c \ud2b9\ud788 \ub450\ub4dc\ub7ec\uc9c4 \uc131\uacfc\ub97c \ub0b4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 <strong>BERT<\/strong>(Bidirectional Encoder Representations from Transformers)\ub294 Google\uc5d0\uc11c \uac1c\ubc1c\ud55c \ud601\uc2e0\uc801\uc778 \ubaa8\ub378\ub85c, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \uc0c8\ub85c\uc6b4 \uae30\uc900\uc744 \uc81c\uc2dc\ud588\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 BERT\uc758 \uac1c\ub150, \uc791\ub3d9 \uc6d0\ub9ac, \uadf8\ub9ac\uace0 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud55c \uc2e4\uc2b5 \uc608\uc81c\ub97c \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. BERT\ub780 \ubb34\uc5c7\uc778\uac00?<\/h2>\n<p>BERT\ub294 \uad6c\uc5ed Transformers \uc544\ud0a4\ud14d\ucc98\ub97c \uae30\ubc18\uc73c\ub85c \ud558\uba70, \ubb38\uc7a5\uc5d0\uc11c \ub2e8\uc5b4\uc758 \uc758\ubbf8\ub97c \uc591\ubc29\ud5a5\uc73c\ub85c \uc774\ud574\ud560 \uc218 \uc788\ub3c4\ub85d \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4. BERT\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc8fc\uc694 \ud2b9\uc9d5\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\uc591\ubc29\ud5a5\uc131(Bidirectional):<\/strong> BERT\ub294 \ub2e8\uc5b4\uc758 \ub9e5\ub77d\uc744 \uc774\ud574\ud558\uae30 \uc704\ud574 \uc88c\uc6b0 \ubb38\ub9e5 \ubaa8\ub450\ub97c \uace0\ub824\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc0ac\uc804\ud559\uc2b5(Pre-training):<\/strong> \ub300\uaddc\ubaa8\uc758 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c \uc0ac\uc804 \ud559\uc2b5\uc744 \uc218\ud589\ud558\uc5ec \ub2e4\uc591\ud55c NLP \uc791\uc5c5\uc5d0\uc11c \uc88b\uc740 \uc131\ub2a5\uc744 \ubc1c\ud718\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc804\uc774 \ud559\uc2b5(Transfer Learning):<\/strong> \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ub9de\uac8c fine-tuning\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>2. BERT\uc758 \uae30\ubcf8 \uc6d0\ub9ac<\/h2>\n<p>BERT\ub294 Transformer \uc544\ud0a4\ud14d\ucc98\uc758 Encoder \ubd80\ubd84\ub9cc\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc740 BERT\uc758 \ud575\uc2ec \uad6c\uc131 \uc694\uc18c\uc785\ub2c8\ub2e4:<\/p>\n<h3>2.1 Tokenization<\/h3>\n<p>\uc785\ub825 \ubb38\uc7a5\uc740 \uba3c\uc800 \ud1a0\ud070\ud654 \uacfc\uc815\uc744 \uac70\uccd0 \ub2e8\uc5b4 \ub610\ub294 \uc11c\ube0c\uc6cc\ub4dc\ub85c \ub098\ub204\uc5b4\uc9d1\ub2c8\ub2e4. BERT\uc5d0\uc11c\ub294 WordPiece\ub77c\ub294 \ud1a0\ud06c\ub098\uc774\uc800\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, &#8216;playing&#8217;\uc740 [&#8216;play&#8217;, &#8216;##ing&#8217;]\uc73c\ub85c \ub098\ub204\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.2 Masked Language Model (MLM)<\/h3>\n<p>BERT\ub294 \uc785\ub825\ub418\ub294 \ubb38\uc7a5\uc5d0\uc11c \uc784\uc758\uc758 \ub2e8\uc5b4\ub97c [MASK] \ud1a0\ud070\uc73c\ub85c \ub300\uccb4\ud558\uc5ec \ubaa8\ub378\uc774 \ud574\ub2f9 \ub2e8\uc5b4\ub97c \uc608\uce21\ud558\ub3c4\ub85d \ud6c8\ub828\ub429\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \ubaa8\ub378\uc774 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\ub294\ub370 \ud070 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4.<\/p>\n<h3>2.3 Next Sentence Prediction (NSP)<\/h3>\n<p>BERT\ub294 \uc8fc\uc5b4\uc9c4 \ub450 \ubb38\uc7a5\uc774 \uc5f0\uc18d\ub41c \ubb38\uc7a5\uc778\uc9c0 \uc608\uce21\ud558\ub294 \uacfc\uc81c\ub97c \ud1b5\ud574 \ubb38\uc7a5 \uac04\uc758 \uad00\uacc4\ub97c \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n<h2>3. BERT \ubaa8\ub378 \uc544\ud0a4\ud14d\ucc98<\/h2>\n<p>BERT \ubaa8\ub378\uc740 \uc5ec\ub7ec \uce35\uc758 Transformer Encoder\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uac01\uac01\uc758 Encoder\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc5ed\ud560\uc744 \uc218\ud589\ud569\ub2c8\ub2e4:<\/p>\n<ul>\n<li>Self-attention: \uac01 \ub2e8\uc5b4\uac00 \ub2e4\ub978 \ub2e8\uc5b4\uc640\uc758 \uad00\uacc4\ub97c \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<li>Feed Forward Neural Network: \uac01 \ub2e8\uc5b4\uc758 \ud45c\ud604\uc744 \ub354\uc6b1 \ud48d\ubd80\ud558\uac8c \ub9cc\ub4ed\ub2c8\ub2e4.<\/li>\n<li>Layer Normalization: \uac01 \uce35\uc758 \ucd9c\ub825\uc744 \uc815\uaddc\ud654\ud558\uc5ec \uc548\uc815\uc131\uc744 \ub192\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>4. BERT\ub97c \ud30c\uc774\ud1a0\uce58\ub85c \uad6c\ud604\ud558\uae30<\/h2>\n<p>\uc774\uc81c BERT \ubaa8\ub378\uc744 \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 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 \ubaa8\ub378\uc744 \ud3ec\ud568\ud55c \ub2e4\uc591\ud55c NLP \ubaa8\ub378\uc758 \uc0ac\uc804 \ud6c8\ub828\ub41c \uac00\uc911\uce58\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n<h3>4.1 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n<p>\uc544\ub798 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>pip install transformers torch<\/code><\/pre>\n<h3>4.2 \ubaa8\ub378 \ub85c\ub4dc\ud558\uae30<\/h3>\n<p>BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc624\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<pre><code>from transformers import BertTokenizer, BertModel\n\n# \ud1a0\ud06c\ub098\uc774\uc800\uc640 \ubaa8\ub378 \ub85c\ub4dc\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertModel.from_pretrained('bert-base-uncased')\n<\/code><\/pre>\n<h3>4.3 \uc785\ub825 \ubb38\uc7a5 \uc900\ube44\ud558\uae30<\/h3>\n<p>\ubb38\uc7a5\uc744 \uc785\ub825\ud558\uc5ec \ud1a0\ud070\ud654\ud558\uace0 \ud150\uc11c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>text = \"Hello, how are you?\"\ninputs = tokenizer(text, return_tensors=\"pt\")\n\n# \ud14d\uc2a4\ud2b8 \uc815\ubcf4 \ud655\uc778\nprint(inputs)\n<\/code><\/pre>\n<h3>4.4 \ubaa8\ub378 \uc608\uce21\ud558\uae30<\/h3>\n<p>\uc785\ub825\ub41c \ubb38\uc7a5\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>outputs = model(**inputs)\n\n# \ucd9c\ub825 \ud655\uc778\nlast_hidden_states = outputs.last_hidden_state\nprint(last_hidden_states.shape)  # (\ubc30\uce58 \ud06c\uae30, \uc2dc\ud000\uc2a4 \uae38\uc774, \uc228\uaca8\uc9c4 \ud06c\uae30)\n<\/code><\/pre>\n<h2>5. Fine-tuning BERT<\/h2>\n<p>BERT \ubaa8\ub378\uc744 \ud2b9\uc815 NLP \uc791\uc5c5\uc5d0 fine-tune\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\uc11c\ub294 \uc608\ub97c \ub4e4\uc5b4 \uac10\uc815 \ubd84\uc11d\uc744 \uc704\ud55c fine-tuning\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>5.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n<p>\uac10\uc815 \ubd84\uc11d\uc744 \uc704\ud55c \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4. \uac04\ub2e8\ud55c \uc608\ub85c \uae0d\uc815\uc801 \ubc0f \ubd80\uc815\uc801 \ub9ac\ubdf0\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>5.2 \ubaa8\ub378 \uc815\uc758<\/h3>\n<pre><code>from torch import nn\n\nclass BERTClassifier(nn.Module):\n    def __init__(self, n_classes):\n        super(BERTClassifier, self).__init__()\n        self.bert = BertModel.from_pretrained('bert-base-uncased')\n        self.dropout = nn.Dropout(0.3)\n        self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\n\n    def forward(self, input_ids, attention_mask):\n        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n        pooled_output = outputs[1]\n        output = self.dropout(pooled_output)\n        return self.out(output)\n<\/code><\/pre>\n<h3>5.3 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n<p>\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<pre><code>from transformers import AdamW\n\n# \uc190\uc2e4\ud568\uc218 \ubc0f \ucd5c\uc801\ud654\uae30 \uc815\uc758\nloss_fn = nn.CrossEntropyLoss()\noptimizer = AdamW(model.parameters(), lr=2e-5)\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.train()\nfor epoch in range(epochs):\n    for batch in train_loader:\n        optimizer.zero_grad()\n        input_ids, attention_mask, labels = batch\n        outputs = model(input_ids, attention_mask)\n        loss = loss_fn(outputs, labels)\n        loss.backward()\n        optimizer.step()\n<\/code><\/pre>\n<h2>6. \uacb0\ub860<\/h2>\n<p>BERT\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \ub9ce\uc740 \ubb38\uc81c\ub4e4\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ud574\uacb0\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uc774\ub7ec\ud55c BERT \ubaa8\ub378\uc744 \uc27d\uace0 \ud6a8\uc728\uc801\uc73c\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\ub97c \ud1b5\ud574 BERT\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c\uc758 \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc774\ud574\ud560 \uc218 \uc788\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uacc4\uc18d\ud574\uc11c \ub354 \ub2e4\uc591\ud55c NLP \uc791\uc5c5\uc744 \uc2e4\ud5d8\ud574 \ubcf4\uc138\uc694!<\/p>\n<h2>\ucc38\uace0 \ubb38\ud5cc<\/h2>\n<ul>\n<li>Devlin, J., Chang, M. W., Lee, K., &amp; Toutanova, K. (2018). <a href=\"https:\/\/arxiv.org\/abs\/1810.04805\">BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding<\/a>.<\/li>\n<li>Hugging Face. <a href=\"https:\/\/huggingface.co\/transformers\/\">Transformers<\/a> \ub77c\uc774\ube0c\ub7ec\ub9ac \uacf5\uc2dd \ubb38\uc11c.<\/li>\n<\/ul>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \ubc1c\uc804\uc740 \ucd5c\uadfc NLP(\uc790\uc5f0\uc5b4 \ucc98\ub9ac) \ubd84\uc57c\uc5d0\uc11c \ud2b9\ud788 \ub450\ub4dc\ub7ec\uc9c4 \uc131\uacfc\ub97c \ub0b4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 BERT(Bidirectional Encoder Representations from Transformers)\ub294 Google\uc5d0\uc11c \uac1c\ubc1c\ud55c \ud601\uc2e0\uc801\uc778 \ubaa8\ub378\ub85c, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \uc0c8\ub85c\uc6b4 \uae30\uc900\uc744 \uc81c\uc2dc\ud588\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 BERT\uc758 \uac1c\ub150, \uc791\ub3d9 \uc6d0\ub9ac, \uadf8\ub9ac\uace0 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud55c \uc2e4\uc2b5 \uc608\uc81c\ub97c \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. BERT\ub780 \ubb34\uc5c7\uc778\uac00? BERT\ub294 \uad6c\uc5ed Transformers \uc544\ud0a4\ud14d\ucc98\ub97c \uae30\ubc18\uc73c\ub85c \ud558\uba70, \ubb38\uc7a5\uc5d0\uc11c \ub2e8\uc5b4\uc758 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/30052\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubc84\ud2b8(BERT)&#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":[33],"tags":[],"class_list":["post-30052","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubc84\ud2b8(BERT) - \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\/30052\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubc84\ud2b8(BERT) - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \ubc1c\uc804\uc740 \ucd5c\uadfc NLP(\uc790\uc5f0\uc5b4 \ucc98\ub9ac) \ubd84\uc57c\uc5d0\uc11c \ud2b9\ud788 \ub450\ub4dc\ub7ec\uc9c4 \uc131\uacfc\ub97c \ub0b4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 BERT(Bidirectional Encoder Representations from Transformers)\ub294 Google\uc5d0\uc11c \uac1c\ubc1c\ud55c \ud601\uc2e0\uc801\uc778 \ubaa8\ub378\ub85c, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \uc0c8\ub85c\uc6b4 \uae30\uc900\uc744 \uc81c\uc2dc\ud588\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 BERT\uc758 \uac1c\ub150, \uc791\ub3d9 \uc6d0\ub9ac, \uadf8\ub9ac\uace0 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud55c \uc2e4\uc2b5 \uc608\uc81c\ub97c \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. BERT\ub780 \ubb34\uc5c7\uc778\uac00? 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\ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ucd5c\uadfc\uc5d0\ub294 \ub525 \ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ub9ce\uc740 \ud601\uc2e0\uc774 \uc77c\uc5b4\ub098\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 KoBERT \ubaa8\ub378\uc744 \uc774\uc6a9\ud558\uc5ec KorNLI \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud55c \ub2e4\uc911 \ud074\ub798\uc2a4\u2026","rel":"","context":"&quot;\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac&quot;\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%9e%90%ec%97%b0%ec%96%b4%ec%b2%98%eb%a6%ac\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25381,"url":"https:\/\/atmokpo.com\/w\/25381\/","url_meta":{"origin":30052,"position":3},"title":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc11c\ube0c\uc6cc\ub4dc \ud1a0\ud06c\ub098\uc774\uc800","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc791\uc131\uc77c: YYYY-MM-DD \uc791\uc131\uc790: \uc870\uad11\ud615 1. \uc11c\ub860 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \ucef4\ud4e8\ud130\uac00 \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\uc774\ub2e4. \ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \ub525 \ub7ec\ub2dd \uae30\ubc18\uc758 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uae30\uc220\uc740 \ube44\uc57d\uc801\uc778 \ubc1c\uc804\uc744 \uc774\ub8e8\uc5c8\uc73c\uba70, \ud2b9\ud788 BERT, GPT\uc640 \uac19\uc740 \ub300\ud615 \uc5b8\uc5b4 \ubaa8\ub378\uc740 \ub180\ub77c\uc6b4 \uc131\uacfc\ub97c \uac70\ub450\uc5c8\ub2e4. \uadf8\ub7ec\ub098 \uc774\ub7ec\ud55c \ubaa8\ub378\uc740 \ud559\uc2b5 \ub370\uc774\ud130\uc5d0 \ub530\ub77c \uc131\ub2a5\uc774 \ud06c\uac8c \ub2ec\ub77c\uc9c0\uba70, \ub300\uc6a9\ub7c9\uc758\u2026","rel":"","context":"&quot;\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac&quot;\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%9e%90%ec%97%b0%ec%96%b4%ec%b2%98%eb%a6%ac\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25181,"url":"https:\/\/atmokpo.com\/w\/25181\/","url_meta":{"origin":30052,"position":4},"title":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc640 \uc815\uaddc \ud45c\ud604\uc2dd","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \uae30\uacc4\uac00 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ubd84\uc11d\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\ub294 \ucef4\ud4e8\ud130 \uacfc\ud559\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \ub525 \ub7ec\ub2dd(deep learning)\uc740 \uc778\uacf5 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\uc790\uc5f0\uc5b4\ucc98\ub9ac","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%9e%90%ec%97%b0%ec%96%b4%ec%b2%98%eb%a6%ac\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25197,"url":"https:\/\/atmokpo.com\/w\/25197\/","url_meta":{"origin":30052,"position":5},"title":"03-02 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ud1b5\uacc4\uc801 \uc5b8\uc5b4 \ubaa8\ub378(Statistical Language Model, SLM)","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc791\uc131\uc77c: 2023\ub144 10\uc6d4 05\uc77c \uc791\uc131\uc790: \uc870\uad11\ud615 1. \uc11c\ub860 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP)\ub294 \ucef4\ud4e8\ud130\uac00 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ud574\uc11d\ud558\ub294 \uae30\uc220 \ubd84\uc57c\ub85c, \ucd5c\uadfc \uba87 \ub144\uac04 \uc778\uacf5\uc9c0\ub2a5(AI)\uacfc \ub525 \ub7ec\ub2dd\uc758 \ubc1c\uc804\uc5d0 \ud798\uc785\uc5b4 \ud06c\uac8c \ubc1c\uc804\ud558\uc600\ub2e4. \ud2b9\ud788, \ud1b5\uacc4\uc801 \uc5b8\uc5b4 \ubaa8\ub378(Statistical Language Model, SLM)\uc740 \uc774\ub7ec\ud55c \ubc1c\uc804\uc758 \ud575\uc2ec\uc801\uc778 \uc694\uc18c\ub85c \uc790\ub9ac \uc7a1\uace0 \uc788\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4\u2026","rel":"","context":"&quot;\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac&quot;\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd 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