{"id":30142,"date":"2024-10-28T03:19:47","date_gmt":"2024-10-28T03:19:47","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30142"},"modified":"2024-11-26T06:49:45","modified_gmt":"2024-11-26T06:49:45","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-%ed%95%9c%ea%b5%ad%ec%96%b4-%ec%9e%84%eb%b2%a0%eb%94%a9","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30142\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud55c\uad6d\uc5b4 \uc784\ubca0\ub529"},"content":{"rendered":"

<\/p>\n

\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ub9ce\uc740 \ud601\uc2e0\uc774 \uc774\ub8e8\uc5b4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \uc5b8\uc5b4\uc758 \ubca1\ud130 \ud45c\ud604\uc778 \uc784\ubca0\ub529(embedding)\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \ud65c\uc6a9\ud558\uc5ec \ud55c\uad6d\uc5b4 \uc784\ubca0\ub529\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uc784\ubca0\ub529\uc774\ub780?<\/h2>\n

\uc784\ubca0\ub529\uc740 \ub2e8\uc5b4\ub098 \ubb38\uc7a5\uc744 \uace0\ucc28\uc6d0 \uacf5\uac04\uc758 \ubca1\ud130\ub85c \ubcc0\ud658\ud558\uc5ec \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc774 \uc774\ud574\ud560 \uc218 \uc788\ub294 \ud615\ud0dc\ub85c \ub9cc\ub4dc\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774\ub294 \ub2e8\uc5b4 \uac04\uc758 \uc720\uc0ac\uc131\uc744 \ubc18\uc601\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, ‘\uc655’\uacfc ‘\uc5ec\uc655’\uc758 \uc784\ubca0\ub529 \ubca1\ud130\ub294 \uc11c\ub85c \uac00\uae5d\uac8c \uc704\uce58\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n

2. \ud55c\uad6d\uc5b4 \uc790\uc5f0\uc5b4 \ucc98\ub9ac<\/h2>\n

\ud55c\uad6d\uc5b4\ub294 \ub2e4\uc591\ud55c \ud615\ud0dc\uc18c\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc5b4 \uc601\uc5b4\uc640 \uac19\uc740 \uc5b8\uc5b4\uc5d0 \ube44\ud574 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c \ub354 \ubcf5\uc7a1\ud569\ub2c8\ub2e4. \uc774\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \ud55c\uad6d\uc5b4 \ud615\ud0dc\uc18c \ubd84\uc11d\uae30\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub300\ud45c\uc801\uc778 \ud615\ud0dc\uc18c \ubd84\uc11d\uae30\uc5d0\ub294 KoNLPy<\/strong>, mecab<\/strong>, khaiii<\/strong> \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2.1 KoNLPy \uc124\uce58 \ubc0f \uc0ac\uc6a9\ubc95<\/h3>\n

KoNLPy\ub294 \uac04\ud3b8\ud558\uac8c \ud55c\uad6d\uc5b4 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub97c \ud560 \uc218 \uc788\uac8c \ub3c4\uc640\uc8fc\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc544\ub798\ub294 KoNLPy\uc758 \uc124\uce58 \ubc29\ubc95\uacfc \uae30\ubcf8 \uc0ac\uc6a9\ubc95\uc785\ub2c8\ub2e4.<\/p>\n

!pip install konlpy<\/code><\/pre>\n
2.2 \uae30\ubcf8 \uc0ac\uc6a9 \uc608\uc81c<\/h3>\n
from konlpy.tag import Okt\n\nokt = Okt()\ntext = \"\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4.\"\nprint(okt.morphs(text))  # \ud615\ud0dc\uc18c \ubd84\uc11d\nprint(okt.nouns(text))   # \uba85\uc0ac \ucd94\ucd9c\nprint(okt.phrases(text))  # \uad6c \ucd94\ucd9c\n    <\/code><\/pre>\n
3. \ud30c\uc774\ud1a0\uce58(Pytorch)\ub85c \uc784\ubca0\ub529 \uad6c\ud604\ud558\uae30<\/h2>\n
\uc774\uc81c \uc6b0\ub9ac\ub294 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0, \ud55c\uad6d\uc5b4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uc5ec \uc784\ubca0\ub529\uc744 \uc2e4\ud589\ud560 \uc900\ube44\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
\ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uac04\ub2e8\ud55c \ud55c\uad6d\uc5b4 \ubb38\uc7a5 \ub9ac\uc2a4\ud2b8\ub97c \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
sentences = [\n    \"\uc548\ub155\ud558\uc138\uc694\",\n    \"\ub525\ub7ec\ub2dd\uc740 \uc7ac\ubbf8\uc788\uc2b5\ub2c8\ub2e4.\",\n    \"\ud30c\uc774\uc36c\uc744 \uc0ac\uc6a9\ud558\uc5ec \uba38\uc2e0\ub7ec\ub2dd\uc744 \ubc30\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\",\n    \"\uc778\uacf5\uc9c0\ub2a5\uc740 \uc6b0\ub9ac\uc758 \ubbf8\ub798\uc785\ub2c8\ub2e4.\"\n]\n    <\/code><\/pre>\n
3.2 \ud14d\uc2a4\ud2b8 \uc804\ucc98\ub9ac<\/h3>\n
\ud615\ud0dc\uc18c \ubd84\uc11d\uae30\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uc5b4\ub97c \ucd94\ucd9c\ud558\uace0, \uc774\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc784\ubca0\ub529\uc744 \ub9cc\ub4e4 \uc900\ube44\ub97c \ud569\ub2c8\ub2e4.<\/p>\n
from collections import Counter\nimport numpy as np\n\n# \ud615\ud0dc\uc18c \ubd84\uc11d\ndef preprocess(sentences):\n    okt = Okt()\n    tokens = [okt.morphs(sentence) for sentence in sentences]\n    return tokens\n\ntokens = preprocess(sentences)\n\n# \ub2e8\uc5b4 \uc9d1\ud569 \uc0dd\uc131\nflat_list = [item for sublist in tokens for item in sublist]\nword_counter = Counter(flat_list)\nword_vocab = {word: i + 1 for i, (word, _) in enumerate(word_counter.most_common())}  # 0\uc740 \ud328\ub529\uc744 \uc704\ud55c \uc790\ub9ac\n    <\/code><\/pre>\n
3.3 \ud30c\uc774\ud1a0\uce58 \ub370\uc774\ud130\ub85c\ub354 \uad6c\uc131<\/h3>\n
\ub2e8\uc5b4 \ubca1\ud130\ub97c \uc0dd\uc131\ud558\uae30 \uc704\ud574 \ud30c\uc774\ud1a0\uce58\uc758 \ub370\uc774\ud130\ub85c\ub354\ub97c \ud65c\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch\nfrom torch.utils.data import Dataset, DataLoader\n\nclass CustomDataset(Dataset):\n    def __init__(self, tokens, word_vocab):\n        self.tokens = tokens\n        self.word_vocab = word_vocab\n\n    def __len__(self):\n        return len(self.tokens)\n\n    def __getitem__(self, idx):\n        sentence = self.tokens[idx]\n        return torch.tensor([self.word_vocab[word] for word in sentence], dtype=torch.long)\n\ndataset = CustomDataset(tokens, word_vocab)\ndataloader = DataLoader(dataset, batch_size=2, shuffle=True)\n    <\/code><\/pre>\n
3.4 \uc784\ubca0\ub529 \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
\uc774\uc81c \uc784\ubca0\ub529 \ub808\uc774\uc5b4\ub97c \ud3ec\ud568\ud55c \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch.nn as nn\n\nclass WordEmbeddingModel(nn.Module):\n    def __init__(self, vocab_size, embedding_dim):\n        super(WordEmbeddingModel, self).__init__()\n        self.embeddings = nn.Embedding(vocab_size, embedding_dim)\n\n    def forward(self, input):\n        return self.embeddings(input)\n\nembedding_dim = 5\nmodel = WordEmbeddingModel(vocab_size=len(word_vocab) + 1, embedding_dim=embedding_dim)\n    <\/code><\/pre>\n
3.5 \uc784\ubca0\ub529 \ud559\uc2b5\ud558\uae30<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc124\uc815\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
loss_function = nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n\n# \uac04\ub2e8\ud55c \uc608\uc2dc\ub85c 5epoch\ub9cc \ud559\uc2b5\nfor epoch in range(5):\n    for i, data in enumerate(dataloader):\n        model.zero_grad()\n        output = model(data)\n        label = data.view(-1)  # \ub808\uc774\ube14 \uc124\uc815 (\uc608\uc2dc\ub85c \ub3d9\uc77c\ud55c \ub2e8\uc5b4\ub97c \uc0ac\uc6a9)\n        loss = loss_function(output.view(-1, len(word_vocab) + 1), label)\n        loss.backward()\n        optimizer.step()\n    print(f\"Epoch {epoch + 1}, Loss: {loss.item()}\")\n    <\/code><\/pre>\n
3.6 \uc784\ubca0\ub529 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\uc784\ubca0\ub529 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \ub2e8\uc5b4 \uac04\uc758 \uad00\uacc4\ub97c \uc9c1\uad00\uc801\uc73c\ub85c \uc774\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 t-SNE\ub97c \uc0ac\uc6a9\ud558\uc5ec 2D\ub85c \uc2dc\uac01\ud654\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.manifold import TSNE\nimport matplotlib.pyplot as plt\n\ndef visualize_embeddings(model, word_vocab):\n    embeddings = model.embeddings.weight.data.numpy()\n    words = list(word_vocab.keys())\n\n    tsne = TSNE(n_components=2)\n    embeddings_2d = tsne.fit_transform(embeddings)\n\n    plt.figure(figsize=(10, 10))\n    for i, word in enumerate(words):\n        plt.scatter(embeddings_2d[i, 0], embeddings_2d[i, 1])\n        plt.annotate(word, (embeddings_2d[i, 0], embeddings_2d[i, 1]), fontsize=9)\n    plt.show()\n\nvisualize_embeddings(model, word_vocab)\n    <\/code><\/pre>\n
4. \uacb0\ub860<\/h2>\n
\ubcf8 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec \ud55c\uad6d\uc5b4 \uc784\ubca0\ub529\uc744 \uad6c\ud604\ud558\ub294 \uacfc\uc815\uc744 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uc784\ubca0\ub529\uc740 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uba70, \ub2e4\uc591\ud55c \uc5b8\uc5b4\uc758 \ud2b9\uc131\uc5d0 \ub9de\ucd98 \uc804\ucc98\ub9ac\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \ud5a5\ud6c4 \ub354 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uacfc \ub370\uc774\ud130\uc14b\uc744 \ub2e4\ub8e8\uba70 \uae4a\uc774 \uc788\ub294 \uc5f0\uad6c\ub97c \uc9c4\ud589\ud558\ub294 \uac83\uc744 \ucd94\ucc9c\ud569\ub2c8\ub2e4.<\/p>\n
\uc774 \uac15\uc88c\uac00 \ub525\ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0 \ub300\ud55c \uc774\ud574\ub97c \ub192\uc774\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub418\uae38 \ubc14\ub78d\ub2c8\ub2e4. \ub354 \uad81\uae08\ud55c \uc810\uc774 \uc788\ub2e4\uba74 \ub313\uae00\ub85c \ub0a8\uaca8\uc8fc\uc138\uc694!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
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