{"id":29698,"date":"2024-10-28T01:56:08","date_gmt":"2024-10-28T01:56:08","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29698"},"modified":"2024-11-26T06:51:41","modified_gmt":"2024-11-26T06:51:41","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%86%a0%ed%81%ac%eb%82%98%ec%9d%b4%ec%a7%95-%eb%b0%8f-%ec%9d%b8","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29698\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ud1a0\ud06c\ub098\uc774\uc9d5 \ubc0f \uc778\ucf54\ub529"},"content":{"rendered":"

<\/p>\n

\ub525\ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \uae09\uaca9\ud788 \ubc1c\uc804\ud588\uc73c\uba70, \uc774 \uacfc\uc815\uc5d0\uc11c \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \uc778\uae30 \uc788\ub294 \ub3c4\uad6c \uc911 \ud558\ub098\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud1a0\ud06c\ub098\uc774\uc9d5(Tokenizing)\uacfc \uc778\ucf54\ub529(Encoding)\uc758 \uac1c\ub150\uc744 \uc2ec\ub3c4 \uc788\uac8c \uc124\uba85\ud558\uace0, \uc774\ub97c \ud30c\uc774\uc36c \ucf54\ub4dc\ub85c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc774\ub780?<\/h2>\n

\ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc740 Attention \uba54\ucee4\ub2c8\uc998\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\ub85c, \uc5b8\uc5b4 \ucc98\ub9ac \uc791\uc5c5\uc5d0\uc11c \ub192\uc740 \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. 2017\ub144 “Attention is All You Need”\ub77c\ub294 \ub17c\ubb38\uc5d0\uc11c \ucc98\uc74c \uc18c\uac1c\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \uc785\ub825 \uc2dc\ud000\uc2a4\uc758 \ubaa8\ub4e0 \ub2e8\uc5b4\ub97c \ud55c \ubc88\uc5d0 \uace0\ub824\ud558\uc5ec \ucee8\ud14d\uc2a4\ud2b8 \uc815\ubcf4\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ud3ec\ucc29\ud569\ub2c8\ub2e4.<\/p>\n

2. \ud5c8\uae45\ud398\uc774\uc2a4\uc640 \uadf8 \uae30\ucd08<\/h2>\n

\ud5c8\uae45\ud398\uc774\uc2a4\ub294 \ub9ce\uc740 \uc0ac\uc804 \ud6c8\ub828\ub41c \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\ub4e4\uc744 \ubb34\ub8cc\ub85c \uc81c\uacf5\ud558\ub294 \ud50c\ub7ab\ud3fc\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790\ub294 \ub2e4\uc591\ud55c NLP \uc791\uc5c5(\uc608: \uc9c8\ubb38 \ub2f5\ubcc0, \ud14d\uc2a4\ud2b8 \uc0dd\uc131, \uac10\uc815 \ubd84\uc11d)\uc744 \uac04\ud3b8\ud558\uac8c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud5c8\uae45\ud398\uc774\uc2a4\uc758 transformers<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uba74 \ubcf5\uc7a1\ud55c NLP \uc791\uc5c5\uc744 \uc27d\uac8c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

3. \ud1a0\ud06c\ub098\uc774\uc9d5(Tokenizing)<\/h2>\n

\ud1a0\ud06c\ub098\uc774\uc9d5\uc740 \ud14d\uc2a4\ud2b8\ub97c \uac1c\ubcc4\uc801\uc778 \ub2e8\uc704(\ud1a0\ud070)\ub85c \ub098\ub204\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ubb38\uc7a5\uc744 \ub2e8\uc5b4\ub85c \ub098\ub204\uac70\ub098, \ub2e8\uc5b4\ub97c \uc11c\ube0c\uc6cc\ub4dc(subword)\ub85c \ub098\ub204\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \ubaa8\ub378\uc774 \uc774\ud574\ud560 \uc218 \uc788\ub294 \ud615\ud0dc\ub85c \ub370\uc774\ud130\ub97c \ubcc0\ud658\ud558\ub294 \ub370 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n

3.1 \uc65c \ud1a0\ud06c\ub098\uc774\uc9d5\uc774 \uc911\uc694\ud55c\uac00?<\/h3>\n

\ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc740 \uc785\ub825 \ud14d\uc2a4\ud2b8\ub97c \uace0\uc815\ub41c \uae38\uc774\uc758 \ud1a0\ud070 \uc2dc\ud000\uc2a4\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4. \uc798 \uad6c\ud604\ub41c \ud1a0\ud06c\ub098\uc774\uc800\ub294 \uc785\ub825 \ub370\uc774\ud130\ub97c \ub354 \uc798 \ucc98\ub9ac\ud558\uace0 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

3.2 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud1a0\ud06c\ub098\uc774\uc800 \uc0ac\uc6a9\ud558\uae30<\/h3>\n

\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc5d0\ub294 \uc5ec\ub7ec \uc885\ub958\uc758 \ud1a0\ud06c\ub098\uc774\uc800\uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub4e4\uc740 \uac01 \ubaa8\ub378\uc5d0 \ucd5c\uc801\ud654\ub418\uc5b4 \uc788\uc73c\uba70, \uc608\ub97c \ub4e4\uc5b4 BERT, GPT-2, T5 \ubaa8\ub378\uc5d0 \uc0ac\uc6a9\ub420 \ud1a0\ud06c\ub098\uc774\uc800\uac00 \ub2e4\ub985\ub2c8\ub2e4.<\/p>\n

\uc608\uc81c: BERT \ubaa8\ub378\uc758 \ud1a0\ud06c\ub098\uc774\uc9d5<\/h4>\n
from transformers import BertTokenizer\n\n# BERT \ud1a0\ud06c\ub098\uc774\uc800 \ucd08\uae30\ud654\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n\n# \ud14d\uc2a4\ud2b8 \uc785\ub825\ntext = \"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \ud65c\uc6a9\ud55c NLP \uac15\uc88c\uc5d0 \uc624\uc2e0 \uac83\uc744 \ud658\uc601\ud569\ub2c8\ub2e4!\"\n\n# \ud14d\uc2a4\ud2b8 \ud1a0\ud06c\ub098\uc774\uc9d5\ntokens = tokenizer.tokenize(text)\nprint(tokens)\n<\/code><\/pre>\n
\uc704 \ucf54\ub4dc\ub97c \uc2e4\ud589\ud558\uba74 \uc785\ub825\ub41c \ud14d\uc2a4\ud2b8\uac00 \uac01 \ud1a0\ud070\uc73c\ub85c \ubd84\ub9ac\ub41c \uacb0\uacfc\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc774 \ub610\ud55c \ubaa8\ub378\uc5d0 \ub9de\ub294 \ud615\ud0dc\ub85c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
4. \uc778\ucf54\ub529(Encoding)<\/h3>\n
\ud1a0\ud06c\ub098\uc774\uc9d5 \ud6c4, \uc6b0\ub9ac\ub294 \ud1a0\ud070\uc744 \ubaa8\ub378\uc5d0 \uc785\ub825\ud560 \uc218 \uc788\ub294 \uc22b\uc790 \ud615\ud0dc\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc744 \uc778\ucf54\ub529\uc774\ub77c\uace0 \ud558\uba70, \uc77c\ubc18\uc801\uc73c\ub85c \uac01 \ub2e8\uc5b4\uc758 \uc778\ub371\uc2a4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud1a0\ud070\uc744 \uc22b\uc790\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c: BERT \ubaa8\ub378\uc758 \uc778\ucf54\ub529<\/h4>\n
# \ud14d\uc2a4\ud2b8 \uc778\ucf54\ub529\nencoded_input = tokenizer.encode(text, return_tensors='pt')\nprint(encoded_input)\n<\/code><\/pre>\n
\uc5ec\uae30\uc11c return_tensors='pt'<\/code>\ub294 PyTorch \ud150\uc11c\ub97c \ubc18\ud658\ud558\ub77c\ub294 \uc758\ubbf8\uc785\ub2c8\ub2e4. \uc774\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc5d0 \uc9c1\uc811 \uc785\ub825\ud560 \uc218 \uc788\ub294 \ud615\ud615\ud0dc\uc785\ub2c8\ub2e4.<\/p>\n
5. \ud1b5\ud569 \uc608\uc81c: \ud14d\uc2a4\ud2b8 \ubd84\ub958\ub97c \uc704\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\uc774\uc81c\uae4c\uc9c0 \ubc30\uc6b4 \ud1a0\ud06c\ub098\uc774\uc9d5\uacfc \uc778\ucf54\ub529 \uacfc\uc815\uc744 \ud558\ub098\uc758 \uc608\uc81c\ub85c \ud1b5\ud569\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uac04\ub2e8\ud55c \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubaa8\ub378\uc744 \uc704\ud574 \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud558\ub294 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
5.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\uc6b0\uc120 \uac04\ub2e8\ud55c \ud14d\uc2a4\ud2b8 \ubd84\ub958\ub97c \uc704\ud55c \ub370\uc774\ud130\ub97c \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \ud14d\uc2a4\ud2b8\uc640 \ud574\ub2f9 \ub808\uc774\ube14\uc744 \ud3ec\ud568\ud55c \ub9ac\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
texts = [\n    \"\ub098\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uac00 \ub108\ubb34 \uc88b\ub2e4.\",\n    \"\ub525\ub7ec\ub2dd\uc740 \uc5b4\ub835\uc9c0\ub9cc \ud765\ubbf8\ub86d\ub2e4.\",\n    \"AI \uae30\uc220\uc740 \uc6b0\ub9ac\uc758 \uc0b6\uc744 \ubcc0\ud654\uc2dc\ud0a4\uace0 \uc788\ub2e4.\",\n    \"\ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc740 \uc815\ub9d0 \uac15\ub825\ud558\ub2e4.\",\n    \"\uc774\uac83\uc740 \uace0\uc591\uc774\uc5d0 \ub300\ud55c \ud14d\uc2a4\ud2b8\uc774\ub2e4.\"\n]\nlabels = [1, 1, 1, 1, 0]  # 1: \uae0d\uc815, 0: \ubd80\uc815\n<\/code><\/pre>\n
5.2 \ub370\uc774\ud130 \ud1a0\ud06c\ub098\uc774\uc9d5 \ubc0f \uc778\ucf54\ub529<\/h3>\n
\ub2e4\uc74c\uc73c\ub85c \ub370\uc774\ud130\ub97c \ud1a0\ud06c\ub098\uc774\uc9d5\ud558\uace0 \uc778\ucf54\ub529\ud558\ub294 \ucf54\ub4dc\ub97c \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub8e8\ud504\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac01 \ud14d\uc2a4\ud2b8\uc5d0 \ub300\ud574 \ud1a0\ud06c\ub098\uc774\uc9d5 \ubc0f \uc778\ucf54\ub529\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
# \ubaa8\ub4e0 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uae30 \uc704\ud55c \ube48 \ub9ac\uc2a4\ud2b8 \ucd08\uae30\ud654\nencoded_texts = []\n\n# \uac01 \ud14d\uc2a4\ud2b8\uc5d0 \ub300\ud574 \ud1a0\ud06c\ub098\uc774\uc9d5 \ubc0f \uc778\ucf54\ub529 \uc218\ud589\nfor text in texts:\n    encoded_text = tokenizer.encode(text, return_tensors='pt')\n    encoded_texts.append(encoded_text)\n\nprint(encoded_texts)\n<\/code><\/pre>\n
5.3 \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uae30<\/h3>\n
\uc774\uc81c \uc778\ucf54\ub529\ub41c \ud14d\uc2a4\ud2b8\ub97c \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uc5ec \uc608\uce21 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubaa8\ub378\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from transformers import BertForSequenceClassification\nimport torch\n\n# BERT \ubaa8\ub378 \ub85c\ub4dc\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n\n# \ubaa8\ub378\uc744 \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc804\ud658\nmodel.eval()\n\n# \uac01 \uc778\ucf54\ub529\ub41c \ud14d\uc2a4\ud2b8\uc5d0 \ub300\ud574 \uc608\uce21 \uc218\ud589\nfor encoded_text in encoded_texts:\n    with torch.no_grad():\n        outputs = model(encoded_text)\n        predictions = torch.argmax(outputs.logits, dim=-1)\n        print(f\"\uc608\uce21 \ub808\uc774\ube14: {predictions.item()}\")\n<\/code><\/pre>\n
\uc704 \ucf54\ub4dc\uc5d0\uc11c \uc6b0\ub9ac\ub294 \uac01\uac01\uc758 \uc778\ucf54\ub529\ub41c \ud14d\uc2a4\ud2b8\uc5d0 \ub300\ud574 \uc608\uce21\uc744 \uc218\ud589\ud558\uc5ec \ud574\ub2f9 \ud14d\uc2a4\ud2b8\uc758 \ub808\uc774\ube14\uc744 \uc608\uce21\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc29\uc2dd\uc73c\ub85c \uc6b0\ub9ac\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ud14d\uc2a4\ud2b8\uc758 \uce74\ud14c\uace0\ub9ac\ub97c \ubd84\ub958\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6. \uacb0\ub860<\/h2>\n
\uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud1a0\ud06c\ub098\uc774\uc9d5\uacfc \uc778\ucf54\ub529\uc758 \uc911\uc694\uc131\uc5d0 \ub300\ud574 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \uc774\ub97c \ud1b5\ud574 \uac04\ub2e8\ud55c \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubaa8\ub378\uc744 \uad6c\ud604\ud574 \ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ud5c8\uae45\ud398\uc774\uc2a4\ub294 \ub2e4\uc591\ud55c \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uacfc \ud568\uaed8 \uac15\ub825\ud55c API\ub97c \uc81c\uacf5\ud558\uc5ec NLP \uc791\uc5c5\uc744 \uc27d\uac8c \uc218\ud589\ud560 \uc218 \uc788\uac8c \ud574\uc90d\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 \ub525\ub7ec\ub2dd \ubc0f \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0 \ub300\ud55c \ud559\uc2b5\uc744 \uacc4\uc18d \uc774\uc5b4\uac00\uae38 \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n
7. \ucc38\uace0\uc790\ub8cc<\/h2>\n