{"id":29704,"date":"2024-10-28T01:56:10","date_gmt":"2024-10-28T01:56:10","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29704"},"modified":"2024-11-26T06:51:40","modified_gmt":"2024-11-26T06:51:40","slug":"%ed%97%88%ea%b9%85%ed%8e%98%ec%9d%b4%ec%8a%a4-%ed%8a%b8%eb%a0%8c%ec%8a%a4%ed%8f%ac%eb%a8%b8-%ed%99%9c%ec%9a%a9%ea%b0%95%ec%a2%8c-%ed%8c%8c%ec%9d%b8%ed%8a%9c%eb%8b%9d-%ec%97%86%eb%8a%94-bert-%eb%b6%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29704\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ud30c\uc778\ud29c\ub2dd \uc5c6\ub294 BERT \ubd84\ub958"},"content":{"rendered":"

<\/p>\n

\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd \ubc0f \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0\uc11c \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 BERT \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \ud30c\uc778\ud29c\ub2dd \uc5c6\uc774 \ubd84\ub958 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. BERT(Bidirectional Encoder Representations from Transformers)\ub294 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\ub294 \ub370 \uc788\uc5b4 \ud601\uc2e0\uc801\uc778 \ubaa8\ub378\ub85c, \uad6c\uae00\uc5d0 \uc758\ud574 \uac1c\ubc1c\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uac04\ud3b8\ud558\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc5d0 \uc911\uc810\uc744 \ub461\ub2c8\ub2e4.<\/p>\n

1. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub780?<\/h2>\n

\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ub2e4\uc591\ud55c \uc0ac\uc804 \ud559\uc2b5\ub41c NLP \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\ub294 \ud30c\uc774\uc36c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uba74 BERT, GPT-2, T5 \ub4f1 \uc5ec\ub7ec \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c \uc804\uc774 \ud559\uc2b5\uc744 \ud1b5\ud574 \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ub9de\uac8c \ubaa8\ub378\uc744 \uc870\uc815\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2. BERT\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n

BERT\ub294 Bidirectional Encoder Representations from Transformers\uc758 \uc57d\uc790\ub85c, \uc591\ubc29\ud5a5\uc758 \ubb38\ub9e5 \uc815\ubcf4\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \ud2b9\uc9d5\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 RNN\uc774\ub098 LSTM\uacfc \ub2ec\ub9ac, BERT\ub294 Transformer \uad6c\uc870\ub97c \uae30\ubc18\uc73c\ub85c \ud558\uc5ec, \uc785\ub825 \ub370\uc774\ud130\uc758 \ubaa8\ub4e0 \ub2e8\uc5b4\ub97c \ub3d9\uc2dc\uc5d0 \uace0\ub824\ud558\uc5ec \ubb38\ub9e5\uc744 \uc774\ud574\ud569\ub2c8\ub2e4.<\/p>\n

3. \ud30c\uc778\ud29c\ub2dd \uc5c6\uc774 BERT \ubd84\ub958\ud558\uae30<\/h2>\n

\uc77c\ubc18\uc801\uc73c\ub85c BERT \ubaa8\ub378\uc740 \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ub9de\uac8c \ud30c\uc778\ud29c\ub2dd\ud558\ub294 \uac83\uc774 \uc77c\ubc18\uc801\uc785\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc6b0\ub9ac\ub294 \ud30c\uc778\ud29c\ub2dd \uc5c6\uc774\ub3c4 BERT \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8 \ubd84\ub958 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ud30c\uc778\ud29c\ub2dd \uc5c6\uc774 BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc124\uba85\uc785\ub2c8\ub2e4.<\/p>\n

3.1 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n

\uba3c\uc800 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38\uc640 \ud1a0\ud06c\ub098\uc774\uc800 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.<\/p>\n

!pip install transformers torch<\/code><\/pre>\n
3.2 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\ub2e4\uc74c\uc73c\ub85c \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uae0d\uc815\uc801 \ubb38\uc7a5\uacfc \ubd80\uc815\uc801 \ubb38\uc7a5\uc744 \uad6c\ubd84\ud558\ub294 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uac04\ub2e8\ud55c \uc608\uc2dc\uc785\ub2c8\ub2e4.<\/p>\n
data = [\n        {\"text\": \"\uc774 \uc601\ud654\ub294 \uc815\ub9d0 \uc7ac\ubbf8\uc788\uc5c8\uc5b4\uc694.\", \"label\": \"positive\"},\n        {\"text\": \"\ucd5c\uc545\uc758 \uc601\ud654\uc785\ub2c8\ub2e4.\", \"label\": \"negative\"},\n        {\"text\": \"\uc815\ub9d0 \ud6cc\ub96d\ud55c \uc5f0\uae30\uc600\uc2b5\ub2c8\ub2e4.\", \"label\": \"positive\"},\n        {\"text\": \"\uc774\uac74 \uc2dc\uac04 \ub0ad\ube44\uc785\ub2c8\ub2e4.\", \"label\": \"negative\"},\n    ]<\/code><\/pre>\n
3.3 BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800 \ubd88\ub7ec\uc624\uae30<\/h3>\n
\ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804 \ud559\uc2b5\ub41c BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800\ub97c \ubd88\ub7ec\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\uc758 \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubd88\ub7ec\uc635\ub2c8\ub2e4.<\/p>\n
from transformers import BertTokenizer, BertModel\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertModel.from_pretrained('bert-base-uncased')<\/code><\/pre>\n
3.4 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\uc774\uc81c \ub370\uc774\ud130\uc5d0\uc11c \ud14d\uc2a4\ud2b8\ub97c \ud1a0\ud070\ud654\ud558\uace0 \uc785\ub825 \ud150\uc11c\ub97c \ub9cc\ub4e4\uae30 \uc704\ud55c \uc804\ucc98\ub9ac\ub97c \uc218\ud589\ud574\uc57c \ud569\ub2c8\ub2e4. BERT \ubaa8\ub378\uc758 \uc785\ub825 \ud615\uc2dd\uc5d0 \ub9de\uac8c \ub370\uc774\ud130\ub97c \ubcc0\ud658\ud569\ub2c8\ub2e4.<\/p>\n
inputs = tokenizer([d['text'] for d in data], padding=True, truncation=True, return_tensors=\"pt\")\nlabels = [d['label'] for d in data]<\/code><\/pre>\n
3.5 \ubaa8\ub378 \ucd9c\ub825 \ucd94\ucd9c<\/h3>\n
BERT \ubaa8\ub378\uc744 \ud1b5\ud574 \uac01 \ud14d\uc2a4\ud2b8\uc758 \ucd9c\ub825 \ubca1\ud130\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ubca1\ud130\ub294 \ub2e4\uc74c \ub2e8\uacc4\uc5d0\uc11c \ubd84\ub958 \uc791\uc5c5\uc5d0 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
with torch.no_grad():\n    outputs = model(**inputs)\n    embeddings = outputs.last_hidden_state[:, 0, :].numpy()  # CLS \ud1a0\ud070 \uc0ac\uc6a9<\/code><\/pre>\n
3.6 \ud14d\uc2a4\ud2b8 \ubd84\ub958\uae30 \uad6c\ud604<\/h3>\n
\uc774\uc81c \ubaa8\ub378\uc758 \ucd9c\ub825\uc778 \uc784\ubca0\ub529\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \ubd84\ub958\uae30\ub97c \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n# \uc784\ubca0\ub529\uacfc \ub808\uc774\ube14\uc744 \ubca1\ud130\ud654\nX = embeddings\ny = [1 if label == \"positive\" else 0 for label in labels]\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \ud559\uc2b5\nclassifier = LogisticRegression()\nclassifier.fit(X_train, y_train)\n\n# \uc608\uce21\ny_pred = classifier.predict(X_test)\n\n# \uc815\ud655\ub3c4 \ud3c9\uac00\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\ubaa8\ub378 \uc815\ud655\ub3c4: {accuracy:.2f}\")  # \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \ucd9c\ub825\ud569\ub2c8\ub2e4.<\/code><\/pre>\n
4. \uacb0\ub860<\/h2>\n
\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \ud30c\uc778\ud29c\ub2dd \uc5c6\uc774 BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4. BERT\uc758 \uc0ac\uc804 \ud559\uc2b5\ub41c \uc784\ubca0\ub529\uc744 \ud65c\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \ubd84\ub958 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc74c\uc744 \ud655\uc778\ud558\uc600\uace0, \uc774\ub97c \ud1b5\ud574 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ucd08\ub97c \ub2e4\uc9c8 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uac00 \ud544\uc694\ud55c \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c BERT \ubaa8\ub378\uc774 \uc720\uc6a9\ud558\uac8c \uc0ac\uc6a9\ub420 \uc218 \uc788\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5. \ucc38\uace0\uc790\ub8cc<\/h2>\n