{"id":29650,"date":"2024-10-28T01:55:55","date_gmt":"2024-10-28T01:55:55","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29650"},"modified":"2024-11-26T06:51:52","modified_gmt":"2024-11-26T06:51:52","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-%ea%b5%ac%ea%b8%80-%ec%bd%94%eb%9e%a9-%ed%99%98%ea%b2%bd-%ec%84%a4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29650\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \uad6c\uae00 \ucf54\ub7a9 \ud658\uacbd \uc124\uc815"},"content":{"rendered":"

<\/p>\n

\n \ub525\ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uc758 \ubc1c\uc804\uc5d0 \ub530\ub77c \ud6a8\uc728\uc801\uc774\uace0 \uac15\ub825\ud55c \ubcc0\ud658 \ubaa8\ub378\ub4e4\uc774 \ub4f1\uc7a5\ud558\uc600\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \ud558\ub098\uac00 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face)\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38(transformers) \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \uad6c\uae00 \ucf54\ub7a9 \ud658\uacbd\uc5d0\uc11c \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uacfc \uae30\ubcf8\uc801\uc778 \uc608\uc81c, \uadf8\ub9ac\uace0 \uc2e4\uc81c \ucf54\ub4dc \ud65c\uc6a9 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

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

\n \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ub2e4\uc591\ud55c \ucd5c\ucca8\ub2e8 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\ub294 \uc624\ud508 \uc18c\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. BERT, GPT-2, RoBERTa, T5 \ub4f1\uacfc \uac19\uc740 \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub7ec\ud55c \ubaa8\ub378\ub4e4\uc740 \uc0ac\uc804 \ud6c8\ub828(pre-trained)\ub418\uc5b4 \uc788\uc5b4, \uc801\uc740 \ub370\uc774\ud130\ub85c\ub3c4 \ub192\uc740 \uc131\ub2a5\uc744 \ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 PyTorch\uc640 TensorFlow \ub450 \uac00\uc9c0\uc758 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc9c0\uc6d0\ud569\ub2c8\ub2e4.\n <\/p>\n

2. \uad6c\uae00 \ucf54\ub7a9 \uac1c\uc694<\/h2>\n

\n \uad6c\uae00 \ucf54\ub7a9(Google Colaboratory)\uc740 \ud074\ub77c\uc6b0\ub4dc \uae30\ubc18\uc758 Jupyter \ub178\ud2b8\ubd81 \uc11c\ube44\uc2a4\uc785\ub2c8\ub2e4. \ubb34\ub8cc GPU \ub9ac\uc18c\uc2a4\ub97c \uc81c\uacf5\ud558\uc5ec \uc0ac\uc6a9\uc790\uac00 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uace0 \uc2e4\ud589\ud558\uae30\uc5d0 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc744 \ud1b5\ud574 \uad6c\uae00 \ucf54\ub7a9\uc744 \ud65c\uc6a9\ud558\uc5ec \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

3. \uad6c\uae00 \ucf54\ub7a9 \ud658\uacbd \uc124\uc815\ud558\uae30<\/h2>\n

3.1 \uad6c\uae00 \ucf54\ub7a9 \uc811\uc18d\ud558\uae30<\/h3>\n

\n \uad6c\uae00 \ucf54\ub7a9\uc5d0 \uc811\uc18d\ud558\ub824\uba74 \uc6f9 \ube0c\ub77c\uc6b0\uc800\uc5d0\uc11c Google Colab<\/a>\uc744 \ubc29\ubb38\ud558\uc2ed\uc2dc\uc624. \uad6c\uae00 \uacc4\uc815\uc73c\ub85c \ub85c\uadf8\uc778\ud558\uba74 \uc0c8\ub85c\uc6b4 \ub178\ud2b8\ubd81\uc744 \ub9cc\ub4e4 \uc218 \uc788\ub294 \ud654\uba74\uc774 \ub098\ud0c0\ub0a9\ub2c8\ub2e4.\n <\/p>\n

3.2 \uc0c8\ub85c\uc6b4 \ub178\ud2b8\ubd81 \ub9cc\ub4e4\uae30<\/h3>\n

\n \uc624\ub978\ucabd \uc0c1\ub2e8\uc758 ‘\uc0c8 \ub178\ud2b8\ubd81(New Notebook)’ \ubc84\ud2bc\uc744 \ud074\ub9ad\ud558\uc5ec \uc0c8\ub85c\uc6b4 Jupyter \ub178\ud2b8\ubd81\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \ub178\ud2b8\ubd81\uc758 \uc774\ub984\uc744 \uc124\uc815\ud558\uc5ec \uc791\uc5c5\uc744 \uad6c\ubd84\ud574\uc90d\ub2c8\ub2e4.\n <\/p>\n

3.3 \ub7f0\ud0c0\uc784 \uc720\ud615 \uc124\uc815\ud558\uae30<\/h3>\n

\n \uad6c\uae00 \ucf54\ub7a9\uc5d0\uc11c\ub294 GPU\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud6c8\ub828\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uc0c1\ub2e8 \uba54\ub274\uc5d0\uc11c \ub7f0\ud0c0\uc784(> Runtime)<\/strong> -> \ub7f0\ud0c0\uc784 \uc720\ud615 \ubcc0\uacbd(> Change runtime type)<\/strong>\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4. ‘\ud558\ub4dc\uc6e8\uc5b4 \uac00\uc18d\uae30(Hardware accelerator)’\uc5d0\uc11c ‘GPU’\ub97c \uc120\ud0dd\ud55c \ud6c4 \uc800\uc7a5(Save)<\/strong> \ubc84\ud2bc\uc744 \ud074\ub9ad\ud569\ub2c8\ub2e4.\n <\/p>\n

4. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58\ud558\uae30<\/h2>\n

\n \uc774\uc81c \ucf54\ub7a9 \ud658\uacbd\uc5d0\uc11c \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \ucf54\ub4dc\ub97c \uc785\ub825\ud558\uace0 \uc2e4\ud589\ud558\uc5ec \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.\n <\/p>\n

!pip install transformers<\/code><\/pre>\n
5. \uae30\ubcf8\uc801\uc778 \ud65c\uc6a9 \uc608\uc2dc<\/h2>\n
\n        \uc124\uce58\uac00 \uc644\ub8cc\ub418\uba74 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8 \ubd84\ub958 \uc791\uc5c5\uc744 \uc218\ud589\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
5.1 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc784\ud3ec\ud2b8 \ubc0f \ubaa8\ub378 \ucd08\uae30\ud654<\/h3>\n
import torch\nfrom transformers import BertTokenizer, BertForSequenceClassification\n\n# BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800 \ucd08\uae30\ud654\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n<\/code><\/pre>\n
5.2 \uc785\ub825\ubb38\uc7a5 \ud1a0\ud06c\ub098\uc774\uc9d5 \ubc0f \uc608\uce21 \uc218\ud589<\/h3>\n
\n        \ubaa8\ub378\uc5d0 \uc785\ub825\ud560 \ubb38\uc7a5\uc744 \uc815\uc758\ud558\uace0 \uc774\ub97c \ud1a0\ud06c\ub098\uc774\uc9d5\ud558\uc5ec \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.\n    <\/p>\n
# \uc785\ub825 \ubb38\uc7a5 \uc815\uc758\ninput_sentence = \"I love programming with Python!\"\n\n# \ubb38\uc7a5\uc744 \ud1a0\ud06c\ub098\uc774\uc9d5\ninputs = tokenizer(input_sentence, return_tensors=\"pt\")\n\n# \ubaa8\ub378 \uc608\uce21\nwith torch.no_grad():\n    logits = model(**inputs).logits\n\n# \uc608\uce21 \uacb0\uacfc \ucd9c\ub825\npredicted_class = torch.argmax(logits, dim=1)\nprint(f\"Predicted class: {predicted_class.item()}\")\n<\/code><\/pre>\n
6. \uacb0\ub860<\/h2>\n
\n        \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uad6c\uae00 \ucf54\ub7a9\uc5d0\uc11c \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ud658\uacbd \uc124\uc815\ubd80\ud130 \uae30\ubcf8\uc801\uc778 \ud14d\uc2a4\ud2b8 \ubd84\ub958 \uc608\uc81c\uae4c\uc9c0 \ubaa8\ub4e0 \uacfc\uc815\uc744 \uc218\ud589\ud574\ubcf4\uc558\uc73c\uba70, \uc774\ub97c \ud1b5\ud574 NLP \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc744 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4. \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ub2e4\ub978 \ub9ce\uc740 \uae30\ub2a5\ub3c4 \uc81c\uacf5\ud558\ubbc0\ub85c, \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \ub2e4\uc591\ud55c \ud504\ub85c\uc81d\ud2b8\ub97c \uc9c4\ud589\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.\n    <\/p>\n
7. \ucc38\uace0 \uc790\ub8cc<\/h2>\n