{"id":30088,"date":"2024-10-28T03:19:32","date_gmt":"2024-10-28T03:19:32","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30088"},"modified":"2024-11-26T06:50:02","modified_gmt":"2024-11-26T06:50:02","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-%ec%9e%90%ec%97%b0%ec%96%b4-%ec%b2%98%eb%a6%ac-%ec%9a%a9%ec%96%b4-%eb%b0%8f-%ea%b3%bc%ec%a0%95","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30088\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc6a9\uc5b4 \ubc0f \uacfc\uc815"},"content":{"rendered":"<p><body><\/p>\n<p>\ub525\ub7ec\ub2dd\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud328\ud134\uc774\ub098 \uaddc\uce59\uc744 \ud559\uc2b5\ud558\ub294 \uac15\ub825\ud55c \uae30\uacc4\ud559\uc2b5 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uc5b8\uc5b4\uc640 \uad00\ub828\ub41c \ub370\uc774\ud130\uc758 \uc774\ud574, \ud574\uc11d, \uc0dd\uc131 \ub4f1\uc744 \ucef4\ud4e8\ud130\uac00 \uac00\ub2a5\ud558\ub3c4\ub85d \ud558\ub294 \ub525\ub7ec\ub2dd\uc758 \ud55c \ud2b9\uc815 \ubd84\uc57c\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uc2e0\uacbd\ub9dd\uc744 \uc27d\uac8c \uc815\uc758\ud558\uace0 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \ub9ce\uc740 \uc5f0\uad6c\uc790 \ubc0f \uc2e4\ubb34\uc790\ub4e4\uc774 \uc0ac\uc6a9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>\uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uc6a9\uc5b4<\/h2>\n<ul>\n<li><strong>\ud1a0\ud070\ud654(Tokenization)<\/strong>: \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub610\ub294 \ubb38\uc7a5 \ub2e8\uc704\ub85c \ub098\ub204\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\uc5b4\ud718(Vocabulary)<\/strong>: \ubaa8\ub378\uc774 \uc774\ud574\ud560 \uc218 \uc788\ub294 \ub2e8\uc5b4\uc758 \uc9d1\ud569\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\ubca1\ud130\ud654(Vectorization)<\/strong>: \ub2e8\uc5b4\ub97c \uc218\uce58\uc801 \ud45c\ud604\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\uc784\ubca0\ub529(Embedding)<\/strong>: \ub2e8\uc5b4\ub97c \uace0\ucc28\uc6d0 \ubca1\ud130\ub85c \ud45c\ud604\ud558\ub294 \ubc29\ubc95\uc73c\ub85c, \ub2e8\uc5b4 \uac04\uc758 \uad00\uacc4\ub97c \uc720\uc9c0\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN)<\/strong>: \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uc720\uc6a9\ud55c \uc2e0\uacbd\ub9dd \uad6c\uc870\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\ud2b8\ub79c\uc2a4\ud3ec\uba38(Transformer)<\/strong>: \uc5b4\ud150\uc158 \uba54\ucee4\ub2c8\uc998\uc744 \ud65c\uc6a9\ud558\uc5ec \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ucc98\ub9ac\ud558\ub294 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\ud30c\uc774\ud1a0\uce58 \uae30\ubcf8 \uac1c\ub150<\/h2>\n<p>\ud30c\uc774\ud1a0\uce58(PyTorch)\ub294 Facebook\uc5d0\uc11c \uac1c\ubc1c\ud55c \ub525\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub3d9\uc801 \uadf8\ub798\ud504 \uad6c\uc131 \ubc0f GPU \uac00\uc18d\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 Tensor\ub77c\ub294 \uae30\ubcf8 \ub370\uc774\ud130 \uad6c\uc870\ub97c \uae30\ubc18\uc73c\ub85c \ud558\uba70, \uc774 Tensor\ub294 NumPy\uc758 \ubc30\uc5f4\uc5d0\uc11c \uc601\uac10\uc744 \ubc1b\uc558\uc2b5\ub2c8\ub2e4. PyTorch\uc758 \uc7a5\uc810 \uc911 \ud558\ub098\ub294 \uc9c1\uad00\uc801\uc778 API\uc640 \uc720\uc5f0\ud55c \uac1c\ubc1c\ud658\uacbd\uc785\ub2c8\ub2e4.<\/p>\n<h3>\ud30c\uc774\ud1a0\uce58 \uc124\uce58<\/h3>\n<p>\ud30c\uc774\ud1a0\uce58\ub294 pip\ub098 Conda\ub97c \uc0ac\uc6a9\ud574 \uc27d\uac8c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>pip install torch torchvision torchaudio<\/code><\/pre>\n<h2>\ud30c\uc774\ud1a0\uce58\ub85c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378 \uad6c\ud604<\/h2>\n<p>\uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378\uc744 \uac04\ub2e8\ud788 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>\ub370\uc774\ud130 \uc900\ube44<\/h3>\n<p>\uba3c\uc800, \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uac04\ub2e8\ud55c \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>\nimport pandas as pd\n\n# \ub370\uc774\ud130 \uc0dd\uc131 \uc608\uc2dc\ndata = {\n    'review': ['\uac00\uc7a5 \uc88b\uc558\ub358 \uc601\ud654', '\uc644\uc804 \uc9c0\ub8e8\ud55c \uc601\ud654', '\uc815\ub9d0 \uc7ac\ubc0c\uc5b4\uc694', '\uc2dc\uac04 \ub0ad\ube44\uc785\ub2c8\ub2e4'],\n    'label': [1, 0, 1, 0]\n}\ndf = pd.DataFrame(data)\n\n# \ub370\uc774\ud130 \ud655\uc778\nprint(df)\n    <\/code><\/pre>\n<h3>\ud1a0\ud070\ud654 \ubc0f \ubca1\ud130\ud654<\/h3>\n<p>\ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc22b\uc790\ub85c \ubcc0\ud658\ud558\uae30 \uc704\ud574 \ud1a0\ud070\ud654 \ubc0f \ubca1\ud130\ud654\ub97c \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>\nfrom torchtext.data import Field, TabularDataset, BucketIterator\n\n# \ud544\ub4dc \uc815\uc758\nTEXT = Field(sequential=True, tokenize='basic_english', lower=True)\nLABEL = Field(sequential=False, use_vocab=False)\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc\nfields = {'review': ('text', TEXT), 'label': ('label', LABEL)}\ntrain_data, valid_data = TabularDataset.splits(\n    path='', train='train.csv', validation='valid.csv', format='csv', fields=fields)\n\n# \ub2e8\uc5b4 \ubaa9\ub85d \uad6c\ucd95\nTEXT.build_vocab(train_data, max_size=10000)\n    <\/code><\/pre>\n<h3>\uc2e0\uacbd\ub9dd \ubaa8\ub378 \uc815\uc758<\/h3>\n<p>\ub2e4\uc74c\uc73c\ub85c, RNN \uad6c\uc870\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>\nimport torch.nn as nn\n\nclass RNNModel(nn.Module):\n    def __init__(self, input_dim, emb_dim, hidden_dim, output_dim):\n        super().__init__()\n        self.embedding = nn.Embedding(input_dim, emb_dim)\n        self.rnn = nn.RNN(emb_dim, hidden_dim)\n        self.fc = nn.Linear(hidden_dim, output_dim)\n    \n    def forward(self, text):\n        embedded = self.embedding(text)\n        output, hidden = self.rnn(embedded)\n        return self.fc(hidden)\n    \n# \ubaa8\ub378 \uc778\uc2a4\ud134\uc2a4\ud654\ninput_dim = len(TEXT.vocab)\nemb_dim = 100\nhidden_dim = 256\noutput_dim = 1\n\nmodel = RNNModel(input_dim, emb_dim, hidden_dim, output_dim)\n    <\/code><\/pre>\n<h3>\ubaa8\ub378 \ud559\uc2b5<\/h3>\n<p>\uc774\uc81c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574 \ud559\uc2b5\ub960\uacfc \uc190\uc2e4 \ud568\uc218\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. \uc774\uc5b4\uc11c \uc5d0\ud3ec\ud06c \ub3d9\uc548 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n<pre><code>\nimport torch.optim as optim\n\noptimizer = optim.Adam(model.parameters())\ncriterion = nn.BCEWithLogitsLoss()\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.train()\nfor epoch in range(10):\n    for batch in BucketIterator(train_data, batch_size=32):\n        optimizer.zero_grad()\n        predictions = model(batch.text).squeeze()\n        loss = criterion(predictions, batch.label.float())\n        loss.backward()\n        optimizer.step()\n    print(f'Epoch {epoch+1}, Loss: {loss.item()}')\n    <\/code><\/pre>\n<h2>\uc815\ub9ac<\/h2>\n<p>\ubcf8 \uae00\uc5d0\uc11c\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uc6a9\uc5b4\uc640 \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \uae30\ubcf8\uc801\uc778 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378 \uad6c\ucd95 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc2e4\uc81c \uc5c5\ubb34\uc5d0\uc11c\ub294 \ub354 \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac \ubc0f \ubaa8\ub378 \ud29c\ub2dd\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \uae4a\uc774 \uc788\ub294 \ub525\ub7ec\ub2dd \uacf5\ubd80\uac00 \ud544\uc694\ud558\uba70, \ub2e4\uc591\ud55c \ud328\ud0a4\uc9c0\uc640 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud574\ubcf4\ub294 \uac83\uc744 \ucd94\ucc9c\ub4dc\ub9bd\ub2c8\ub2e4.<\/p>\n<div class=\"note\">\n<strong>\ucc38\uace0 \uc790\ub8cc:<\/strong><\/p>\n<ul>\n<li>Deep Learning for Natural Language Processing by Palash Goyal<\/li>\n<li>PyTorch Documentation<\/li>\n<li>Natural Language Processing with PyTorch by Delip Rao and Greg Diamos<\/li>\n<\/ul>\n<\/div>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub525\ub7ec\ub2dd\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud328\ud134\uc774\ub098 \uaddc\uce59\uc744 \ud559\uc2b5\ud558\ub294 \uac15\ub825\ud55c \uae30\uacc4\ud559\uc2b5 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uc5b8\uc5b4\uc640 \uad00\ub828\ub41c \ub370\uc774\ud130\uc758 \uc774\ud574, \ud574\uc11d, \uc0dd\uc131 \ub4f1\uc744 \ucef4\ud4e8\ud130\uac00 \uac00\ub2a5\ud558\ub3c4\ub85d \ud558\ub294 \ub525\ub7ec\ub2dd\uc758 \ud55c \ud2b9\uc815 \ubd84\uc57c\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uc2e0\uacbd\ub9dd\uc744 \uc27d\uac8c \uc815\uc758\ud558\uace0 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \ub9ce\uc740 \uc5f0\uad6c\uc790 \ubc0f \uc2e4\ubb34\uc790\ub4e4\uc774 \uc0ac\uc6a9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uc6a9\uc5b4 \ud1a0\ud070\ud654(Tokenization): \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub610\ub294 \ubb38\uc7a5 \ub2e8\uc704\ub85c \ub098\ub204\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc5b4\ud718(Vocabulary): \ubaa8\ub378\uc774 \uc774\ud574\ud560 \uc218 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/30088\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc6a9\uc5b4 \ubc0f \uacfc\uc815&#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-30088","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, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc6a9\uc5b4 \ubc0f \uacfc\uc815 - \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\/30088\/\" \/>\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, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc6a9\uc5b4 \ubc0f \uacfc\uc815 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud328\ud134\uc774\ub098 \uaddc\uce59\uc744 \ud559\uc2b5\ud558\ub294 \uac15\ub825\ud55c \uae30\uacc4\ud559\uc2b5 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uc5b8\uc5b4\uc640 \uad00\ub828\ub41c \ub370\uc774\ud130\uc758 \uc774\ud574, \ud574\uc11d, \uc0dd\uc131 \ub4f1\uc744 \ucef4\ud4e8\ud130\uac00 \uac00\ub2a5\ud558\ub3c4\ub85d \ud558\ub294 \ub525\ub7ec\ub2dd\uc758 \ud55c \ud2b9\uc815 \ubd84\uc57c\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uc2e0\uacbd\ub9dd\uc744 \uc27d\uac8c \uc815\uc758\ud558\uace0 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \ub9ce\uc740 \uc5f0\uad6c\uc790 \ubc0f \uc2e4\ubb34\uc790\ub4e4\uc774 \uc0ac\uc6a9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uc6a9\uc5b4 \ud1a0\ud070\ud654(Tokenization): \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub610\ub294 \ubb38\uc7a5 \ub2e8\uc704\ub85c \ub098\ub204\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc5b4\ud718(Vocabulary): \ubaa8\ub378\uc774 \uc774\ud574\ud560 \uc218 &hellip; \ub354 \ubcf4\uae30 &quot;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc6a9\uc5b4 \ubc0f \uacfc\uc815&quot;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/30088\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:19:32+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:50:02+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 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\uac15\uc88c","link":"https:\/\/atmokpo.com\/w\/category\/%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25421,"url":"https:\/\/atmokpo.com\/w\/25421\/","url_meta":{"origin":30088,"position":5},"title":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, BERT(Bidirectional Encoder Representations from Transformers)","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc791\uc131\uc77c: [\ub0a0\uc9dc] \uc791\uc131\uc790: \uc870\uad11\ud615 1. \uc11c\ub860 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP, Natural Language Processing)\ub294 \ucef4\ud4e8\ud130\uac00 \uc778\uac04 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud558\ub294 \ubd84\uc57c\ub85c, \ucd5c\uadfc \uba87 \ub144 \uac04 \uae09\uaca9\ud55c \ubc1c\uc804\uc744 \uc774\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc2ec\uc5d0\ub294 \ub525 \ub7ec\ub2dd\uc774 \uc788\uc73c\uba70, \uc774\ub294 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