{"id":25401,"date":"2024-10-26T09:54:46","date_gmt":"2024-10-26T09:54:46","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=25401"},"modified":"2024-11-26T08:01:53","modified_gmt":"2024-11-26T08:01:53","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-%ed%8a%b8%eb%9e%9c%ec%8a%a4%ed%8f%ac%eb%a8%b8%eb%a5%bc-%ec%9d%b4%ec%9a%a9%ed%95%9c","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/25401\/","title":{"rendered":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uc774\uc6a9\ud55c \ud55c\uad6d\uc5b4 \ucc57\ubd07 (Transformer Chatbot Tutorial)"},"content":{"rendered":"<p>\ucd5c\uadfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\ub294 \uc778\uacf5\uc9c0\ub2a5\uc758 \ubc1c\uc804\uc5d0 \ud798\uc785\uc5b4 \ube44\uc57d\uc801\uc73c\ub85c \ubc1c\uc804\ud558\uc600\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ub525 \ub7ec\ub2dd \ubaa8\ub378, \uadf8\uc911\uc5d0\uc11c\ub3c4 \ud2b8\ub79c\uc2a4\ud3ec\uba38(Transformer) \uc544\ud0a4\ud14d\ucc98\ub294 NLP\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \uc131\uacfc\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \ud65c\uc6a9\ud558\uc5ec \ud55c\uad6d\uc5b4 \ucc57\ubd07\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ub2e8\uacc4\ubcc4\ub85c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\ub294 \ucd08\uae09\ubd80\ud130 \uc911\uae09 \uc218\uc900\uc758 \ub3c5\uc790\ub97c \ub300\uc0c1\uc73c\ub85c \ud558\uba70, \ud30c\uc774\uc36c\uc744 \uc774\uc6a9\ud55c \uc2e4\uc2b5\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/p>\n<h2>1. \ub525 \ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n<p>\uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uc0ac\ub78c\uc774 \uc0ac\uc6a9\ud558\ub294 \uc5b8\uc5b4\ub97c \ucef4\ud4e8\ud130\uac00 \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. NLP\uc758 \uc8fc\uc694 \uacfc\uc81c\ub294 \ubb38\uc7a5\uc758 \uc758\ubbf8 \ubd84\uc11d, \ubb38\ub9e5 \uc774\ud574, \ubb38\uc11c \uc694\uc57d, \uae30\uacc4 \ubc88\uc5ed \ub4f1\uc785\ub2c8\ub2e4. \ub525 \ub7ec\ub2dd\uc740 \uc774\ub7ec\ud55c \uacfc\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \ud6a8\uacfc\uc801\uc778 \ubc29\ubc95\uc73c\ub85c \uc790\ub9ac\uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>1.1 \ub525 \ub7ec\ub2dd\uc758 \uae30\ucd08<\/h3>\n<p>\ub525 \ub7ec\ub2dd\uc740 \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uba38\uc2e0 \ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc740 \uc5ec\ub7ec \uac1c\uc758 \ub178\ub4dc\ub85c \uad6c\uc131\ub418\uba70, \uac01\uac01\uc758 \ub178\ub4dc\ub294 \uc785\ub825\uacfc \ucd9c\ub825\uc744 \uac16\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub525 \ub7ec\ub2dd\uc740 \uc774\ub7ec\ud55c \uad6c\uc870\ub97c \uae4a\uac8c \uc313\uc544 \uc62c\ub824 \ud559\uc2b5\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc8fc\ub85c \ub9ce\uc774 \ud65c\uc6a9\ub418\ub294 \ub525 \ub7ec\ub2dd\uc758 \uae30\ubc95 \uc911 \ud558\ub098\ub294 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN)\uacfc \uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN)\uc785\ub2c8\ub2e4.<\/p>\n<h3>1.2 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ucd08<\/h3>\n<p>NLP\uc758 \uacfc\uc815\uc740 \ubcf4\ud1b5 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4:<\/p>\n<ul>\n<li>\ub370\uc774\ud130 \uc218\uc9d1<\/li>\n<li>\ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/li>\n<li>\ud53c\ucc98 \ucd94\ucd9c<\/li>\n<li>\ubaa8\ub378 \ud6c8\ub828 \ubc0f \ud3c9\uac00<\/li>\n<li>\uc608\uce21 \ubc0f \uacb0\uacfc \ubd84\uc11d<\/li>\n<\/ul>\n<p>\ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 \uc774\ub7ec\ud55c \uacfc\uc815\uc5d0\uc11c \ud2b9\ud788 \ubaa8\ub378 \ud6c8\ub828 \ubc0f \uc608\uce21 \ub2e8\uacc4\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4.<\/p>\n<h2>2. \ud2b8\ub79c\uc2a4\ud3ec\uba38(Transformer) \uc544\ud0a4\ud14d\ucc98<\/h2>\n<p>\ud2b8\ub79c\uc2a4\ud3ec\uba38 \uc544\ud0a4\ud14d\ucc98\ub294 \uad6c\uae00\uc5d0\uc11c 2017\ub144\uc5d0 \ubc1c\ud45c\ud55c \ubaa8\ub378\ub85c, NLP \ubd84\uc57c\uc5d0\uc11c \ud68d\uae30\uc801\uc778 \ud601\uc2e0\uc744 \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ud2b8\ub79c\uc2a4\ud3ec\uba38\uc758 \ud575\uc2ec\uc740 &#8216;\uc5b4\ud150\uc158 \uba54\ucee4\ub2c8\uc998&#8217;\uc785\ub2c8\ub2e4. \uc774 \uba54\ucee4\ub2c8\uc998\uc744 \ud1b5\ud574 \ubaa8\ub378\uc740 \uc785\ub825\ub41c \ub370\uc774\ud130\uc758 \uc911\uc694\uc131\uc744 \ud310\ub2e8\ud558\uace0, \ub9e5\ub77d\uc744 \uc774\ud574\ud558\uba70, \ub354 \ub098\uc544\uac00 \ud6a8\uc728\uc801\uc778 \uc815\ubcf4 \ucc98\ub9ac\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.1 \uc5b4\ud150\uc158 \uba54\ucee4\ub2c8\uc998<\/h3>\n<p>\uc5b4\ud150\uc158 \uba54\ucee4\ub2c8\uc998\uc740 \uc785\ub825\ub41c \uc2dc\ud000\uc2a4\uc758 \uac01 \uc694\uc18c\uac00 \uc5bc\ub9c8\ub098 \uc911\uc694\ud55c\uc9c0\ub97c \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc740 \ud544\uc694\ud55c \uc815\ubcf4\uc5d0 \uc9d1\uc911\ud558\uace0, \ubd88\ud544\uc694\ud55c \uc815\ubcf4\ub97c \ubb34\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uae30\ubcf8 \uc5b4\ud150\uc158 \uc2a4\ucf54\uc5b4\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uacc4\uc0b0\ub429\ub2c8\ub2e4:<\/p>\n<pre><code>S(i,j) = softmax(A(i,j))<\/code><\/pre>\n<p>\uc5ec\uae30\uc11c S(i,j)\ub294 i\ubc88\uc9f8 \ub2e8\uc5b4\uac00 j\ubc88\uc9f8 \ub2e8\uc5b4\uc640\uc758 \uad00\uacc4\ub97c \ub098\ud0c0\ub0b4\ub294 \uc5b4\ud150\uc158 \uc2a4\ucf54\uc5b4\uc785\ub2c8\ub2e4.<\/p>\n<h3>2.2 \ud2b8\ub79c\uc2a4\ud3ec\uba38\uc758 \uad6c\uc131 \uc694\uc18c<\/h3>\n<p>\ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li>\uc778\ucf54\ub354(Encoder)<\/li>\n<li>\ub514\ucf54\ub354(Decoder)<\/li>\n<li>\ud3ec\uc9c0\uc154\ub110 \uc778\ucf54\ub529(Positional Encoding)<\/li>\n<li>\uba40\ud2f0\ud5e4\ub4dc \uc5b4\ud150\uc158(Multi-Head Attention)<\/li>\n<\/ul>\n<h2>3. \ud55c\uad6d\uc5b4 \ucc57\ubd07 \uac1c\ubc1c\uc744 \uc704\ud55c \ub370\uc774\ud130 \uc900\ube44<\/h2>\n<p>\ucc57\ubd07\uc744 \uac1c\ubc1c\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc801\uc808\ud55c \ub370\uc774\ud130\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \ud55c\uad6d\uc5b4 \ucc57\ubd07\uc758 \uacbd\uc6b0, \ub300\ud654 \ub370\uc774\ud130\uc14b\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\ub294 \ub300\ud654\uc758 \ub9e5\ub77d\uacfc \uc8fc\uc81c\ub97c \ud3ec\ud568\ud574\uc57c \ud558\uba70, \ub178\uc774\uc988\uac00 \uc801\uace0 \ud488\uc9c8\uc774 \ub192\uc740 \ub370\uc774\ud130\uc5ec\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<h3>3.1 \ub370\uc774\ud130\uc14b \uc218\uc9d1<\/h3>\n<p>\ub370\uc774\ud130\uc14b\uc740 \uc5ec\ub7ec \uc18c\uc2a4\uc5d0\uc11c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub300\ud45c\uc801\uc778 \ud55c\uad6d\uc5b4 \ub300\ud654 \ub370\uc774\ud130\uc14b\uc73c\ub85c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uac83\ub4e4\uc774 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li>\uce74\uce74\uc624\ud1a1 \ub300\ud654 \ub370\uc774\ud130<\/li>\n<li>\ub124\uc774\ubc84 \uace0\uac1d\uc13c\ud130 \uc0c1\ub2f4 \ub370\uc774\ud130<\/li>\n<li>\ud55c\uad6d\uc5b4 \uc704\ud0a4\ubc31\uacfc \ub300\ud654 \ub370\uc774\ud130<\/li>\n<\/ul>\n<h3>3.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n<p>\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uc804\ucc98\ub9ac \ub2e8\uacc4\uc5d0\uc11c \ud3ec\ud568\ub420 \uc218 \uc788\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li>\ubd88\uc6a9\uc5b4 \uc81c\uac70<\/li>\n<li>\ud1a0\ud070\ud654(Tokenization)<\/li>\n<li>\uc815\uaddc\ud654(Normalization)<\/li>\n<\/ul>\n<p>\uc608\ub97c \ub4e4\uc5b4, \ubd88\uc6a9\uc5b4 \uc81c\uac70 \uacfc\uc815\uc744 \ud1b5\ud574 \uc758\ubbf8\uac00 \uc5c6\ub294 \ub2e8\uc5b4\ub97c \uc81c\uac70\ud568\uc73c\ub85c\uc368 \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>4. \ud55c\uad6d\uc5b4 \ucc57\ubd07 \ubaa8\ub378 \uad6c\ucd95<\/h2>\n<p>\ub370\uc774\ud130\uac00 \uc900\ube44\ub418\uba74 \uc2e4\uc9c8\uc801\uc778 \ucc57\ubd07 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ub2e8\uacc4\ub85c \ub118\uc5b4\uac11\ub2c8\ub2e4. \uc774 \ub2e8\uacc4\uc5d0\uc11c\ub294 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \ubaa8\ub378\uc744 \uc124\uacc4\ud558\uace0 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n<h3>4.1 \ubaa8\ub378 \uc124\uacc4<\/h3>\n<p>\ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc740 \uc778\ucf54\ub354\uc640 \ub514\ucf54\ub354\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc778\ucf54\ub354\ub294 \uc0ac\uc6a9\uc790 \uc785\ub825\uc744 \ucc98\ub9ac\ud558\uace0, \ub514\ucf54\ub354\ub294 \uc751\ub2f5\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \ubaa8\ub378\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc694\uc18c\ub4e4\ub85c \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li>\uc784\ubca0\ub529 \ucc28\uc6d0<\/li>\n<li>\ud5e4\ub4dc \uc218<\/li>\n<li>\ub808\uc774\uc5b4 \uc218<\/li>\n<li>\ub4dc\ub86d\uc544\uc6c3 \ube44\uc728<\/li>\n<\/ul>\n<h3>4.2 \ubaa8\ub378 \uad6c\ud604<\/h3>\n<p>\ubaa8\ub378 \uad6c\ud604\uc740 TensorFlow \ub610\ub294 PyTorch \ub4f1\uc758 \ub525 \ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc218\ud589\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 PyTorch\ub97c \uc774\uc6a9\ud55c \uc608\uc81c\ub97c \uc18c\uac1c\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>import torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nclass TransformerChatbot(nn.Module):\n    def __init__(self, input_dim, output_dim, emb_dim, n_heads, n_layers):\n        super(TransformerChatbot, self).__init__()\n        self.encoder = nn.TransformerEncoder(...)\n        self.decoder = nn.TransformerDecoder(...)\n\n    def forward(self, src, trg):\n        enc_out = self.encoder(src)\n        dec_out = self.decoder(trg, enc_out)\n        return dec_out\n<\/code><\/pre>\n<h3>4.3 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n<p>\ubaa8\ub378\uc774 \uad6c\ud604\ub418\uba74 \ud6c8\ub828\uc744 \uc2dc\uc791\ud569\ub2c8\ub2e4. \ud6c8\ub828 \uacfc\uc815\uc740 \uc190\uc2e4 \ud568\uc218\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uace0, \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \ud1b5\ud574 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4:<\/p>\n<pre><code>criterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\nfor epoch in range(num_epochs):\n    ...\n    optimizer.step()\n<\/code><\/pre>\n<h2>5. \ucc57\ubd07 \ud3c9\uac00 \ubc0f \ud14c\uc2a4\ud2b8<\/h2>\n<p>\ubaa8\ub378\uc774 \ud6c8\ub828\ub41c \ud6c4\uc5d0\ub294 \ud3c9\uac00 \ub2e8\uacc4\ub85c \ub118\uc5b4\uac11\ub2c8\ub2e4. \ucc57\ubd07\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574\uc11c\ub294 BLEU \uc810\uc218\uc640 \uac19\uc740 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc9c0\ud45c\ub294 \uc0dd\uc131\ub41c \uc751\ub2f5\uacfc \uc2e4\uc81c \uc751\ub2f5\uc744 \ube44\uad50\ud558\uc5ec \uc815\ud655\uc131\uc744 \uce21\uc815\ud569\ub2c8\ub2e4.<\/p>\n<h3>5.1 \ud3c9\uac00 \ubc29\ubc95<\/h3>\n<p>BLEU \uc810\uc218\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<pre><code>from nltk.translate.bleu_score import sentence_bleu\n\nreference = [actual_response.split()]\ncandidate = generated_response.split()\nbleu_score = sentence_bleu(reference, candidate)\n<\/code><\/pre>\n<h3>5.2 \ud14c\uc2a4\ud2b8 \ubc0f \ud53c\ub4dc\ubc31<\/h3>\n<p>\ubaa8\ub378\uc744 \uc2e4\uc81c \ud658\uacbd\uc5d0\uc11c \ud14c\uc2a4\ud2b8\ud558\uace0, \uc0ac\uc6a9\uc790 \ud53c\ub4dc\ubc31\uc744 \ud1b5\ud574 \ubaa8\ub378\uc744 \uac1c\uc120\ud558\ub294 \uacfc\uc815\ub3c4 \uc911\uc694\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc548\uc815\uc131\uacfc \uc2e0\ub8b0\uc131\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>6. \uacb0\ub860<\/h2>\n<p>\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \ud55c\uad6d\uc5b4 \ucc57\ubd07\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38\uc758 \uc911\uc694\uc131\uacfc \uadf8 \uad6c\ud604 \ubc29\ubc95\uc744 \uc774\ud574\ud558\ub294\ub370 \ub3c4\uc6c0\uc774 \ub418\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uc774\uc81c \uc5ec\ub7ec\ubd84\uc774 \ubc30\uc6b4 \ub0b4\uc6a9\uc744 \ubc14\ud0d5\uc73c\ub85c \ub2e4\uc591\ud55c \ud504\ub85c\uc81d\ud2b8\uc5d0 \ub3c4\uc804\ud574 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n<h3>\ucc38\uace0\uc790\ub8cc<\/h3>\n<ul>\n<li>Vaswani, A., et al. (2017). &#8220;Attention is All You Need.&#8221;<\/li>\n<li>Brown, T. B., et al. (2020). &#8220;Language Models are Few-Shot Learners.&#8221;<\/li>\n<li>NLTK documentation: <a href=\"https:\/\/www.nltk.org\/\">https:\/\/www.nltk.org\/<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\ucd5c\uadfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\ub294 \uc778\uacf5\uc9c0\ub2a5\uc758 \ubc1c\uc804\uc5d0 \ud798\uc785\uc5b4 \ube44\uc57d\uc801\uc73c\ub85c \ubc1c\uc804\ud558\uc600\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ub525 \ub7ec\ub2dd \ubaa8\ub378, \uadf8\uc911\uc5d0\uc11c\ub3c4 \ud2b8\ub79c\uc2a4\ud3ec\uba38(Transformer) \uc544\ud0a4\ud14d\ucc98\ub294 NLP\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \uc131\uacfc\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \ud65c\uc6a9\ud558\uc5ec \ud55c\uad6d\uc5b4 \ucc57\ubd07\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ub2e8\uacc4\ubcc4\ub85c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\ub294 \ucd08\uae09\ubd80\ud130 \uc911\uae09 \uc218\uc900\uc758 \ub3c5\uc790\ub97c \ub300\uc0c1\uc73c\ub85c \ud558\uba70, \ud30c\uc774\uc36c\uc744 \uc774\uc6a9\ud55c \uc2e4\uc2b5\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4. 1. \ub525 \ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uac1c\ub150 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uc0ac\ub78c\uc774 \uc0ac\uc6a9\ud558\ub294 \uc5b8\uc5b4\ub97c \ucef4\ud4e8\ud130\uac00 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/25401\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uc774\uc6a9\ud55c \ud55c\uad6d\uc5b4 \ucc57\ubd07 (Transformer Chatbot Tutorial)&#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-25401","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - 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