{"id":29688,"date":"2024-10-28T01:56:06","date_gmt":"2024-10-28T01:56:06","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29688"},"modified":"2024-11-26T06:51:42","modified_gmt":"2024-11-26T06:51:42","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-%ec%a0%95%ea%b7%9c%ec%8b%9d-%ed%99%9c%ec%9a%a9-%ec%a0%84%ec%b2%98","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29688\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \uc815\uaddc\uc2dd \ud65c\uc6a9 \uc804\ucc98\ub9ac"},"content":{"rendered":"

<\/p>\n

\ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5 \ubc0f \uba38\uc2e0\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \ub9ce\uc740 \ubd84\uc57c\uc5d0\uc11c \ub525\ub7ec\ub2dd \uae30\uc220\uc774 \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \ud65c\uc6a9\ud55c \ubb38\uc11c \ubd84\ub958 \uc608\uc81c\uc640 \ud568\uaed8, \uc815\uaddc\uc2dd\uc744 \uc0ac\uc6a9\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uae30\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\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(Hugging Face Transformers)\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uc5d0\uc11c \uc8fc\ub85c \uc0ac\uc6a9\ud558\ub294 \uc5ec\ub7ec \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\ub294 Python \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. BERT, GPT-2, T5 \ub4f1 \ub9ce\uc740 \ucd5c\uc2e0 \ubaa8\ub378\ub4e4\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc73c\uba70, \uc0ac\uc6a9\uc790\uac00 \uc190\uc27d\uac8c \ubd88\ub7ec\uc640 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ud30c\uc774\uc36c\uc73c\ub85c \ub418\uc5b4 \uc788\uc5b4 \ub370\uc774\ud130 \uacfc\ud559\uc790\ub4e4\uacfc \uc5f0\uad6c\uc790\ub4e4\uc774 \ub110\ub9ac \uc0ac\uc6a9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2. \uc815\uaddc\uc2dd\uacfc \uc804\ucc98\ub9ac\uc758 \uc911\uc694\uc131<\/h2>\n

\uc815\uaddc\uc2dd(Regular Expression)\uc740 \ubb38\uc790\uc5f4\uc5d0\uc11c \ud2b9\uc815 \ud328\ud134\uc744 \ucc3e\uc544\ub0b4\uac70\ub098 \ubcc0\ud658\ud560 \ub54c \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uae30 \uc804\uc5d0 \uc815\uaddc\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubd88\ud544\uc694\ud55c \ubb38\uc790 \uc81c\uac70, \ud328\ud134 \ub9e4\uce6d \ub4f1\uc744 \uc218\ud589\ud568\uc73c\ub85c\uc368 \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc804\ucc98\ub9ac\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \uc9c1\uc811\uc801\uc778 \uc601\ud5a5\uc744 \ubbf8\uce58\ubbc0\ub85c, \ucda9\ubd84\ud55c \uc8fc\uc758\uac00 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n

3. \ud658\uacbd \uc124\uc815<\/h2>\n

\uba3c\uc800, \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubc0f \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4. \uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\ud558\uc5ec \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4:<\/p>\n

pip install transformers pandas re<\/code><\/pre>\n
4. \ub370\uc774\ud130 \uc900\ube44<\/h2>\n
\uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 \uac10\uc815 \ubd84\uc11d\uc744 \uc704\ud574 \uac04\ub2e8\ud55c \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\ub294 \uae0d\uc815\uacfc \ubd80\uc815\uc744 \ub098\ud0c0\ub0b4\ub294 \ubb38\uc7a5\uc73c\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import pandas as pd\n\ndata = {\n    \"text\": [\n        \"\uc774 \uc81c\ud488\uc740 \uc815\ub9d0 \uc88b\uc544\uc694!\",\n        \"\ubcc4\ub85c\uc608\uc694. \uc544\uc8fc \uc2e4\ub9dd\ud588\uc2b5\ub2c8\ub2e4.\",\n        \"\ub098\uc058\uc9c0 \uc54a\uc740 \uc81c\ud488\uc774\uc5d0\uc694.\",\n        \"\ud658\ubd88\uc774 \ub418\uc5c8\uc73c\uba74 \uc88b\uaca0\uc2b5\ub2c8\ub2e4.\",\n        \"\uc815\ub9d0 \uae30\ub300 \uc774\uc0c1\uc774\uc5d0\uc694!\",\n    ],\n    \"label\": [1, 0, 1, 0, 1]  # 1: \uae0d\uc815, 0: \ubd80\uc815\n}\n\ndf = pd.DataFrame(data)\nprint(df)<\/code><\/pre>\n
5. \uc815\uaddc\uc2dd\uc744 \ud65c\uc6a9\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\ub2e4\uc74c\uc73c\ub85c, \uc815\uaddc\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub97c \uc218\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud2b9\uc218 \ubb38\uc790\ub098 \uc22b\uc790\ub97c \uc81c\uac70\ud558\uace0, \ubaa8\ub4e0 \ubb38\uc790\ub97c \uc18c\ubb38\uc790\ub85c \ubcc0\ud658\ud558\ub294 \uc791\uc5c5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n
import re\n\ndef preprocess_text(text):\n    # \uc18c\ubb38\uc790\ub85c \ubcc0\ud658\n    text = text.lower()\n    # \ud2b9\uc218 \ubb38\uc790\uc640 \uc22b\uc790 \uc81c\uac70\n    text = re.sub(r'[^a-z\uac00-\ud7a3\\s]', '', text)\n    return text\n\ndf['cleaned_text'] = df['text'].apply(preprocess_text)\nprint(df[['text', 'cleaned_text']])<\/code><\/pre>\n
6. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \ud65c\uc6a9\ud55c \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\uc804\ucc98\ub9ac\uac00 \uc644\ub8cc\ub41c \ud6c4, \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac10\uc815 \ubd84\uc11d\uc744 \uc704\ud55c \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \uc608\uc81c \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n
from transformers import BertTokenizer, BertForSequenceClassification\nfrom transformers import Trainer, TrainingArguments\nimport torch\nfrom sklearn.model_selection import train_test_split\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(df['cleaned_text'], df['label'], test_size=0.2, random_state=42)\n\n# BERT \ud1a0\ud06c\ub098\uc774\uc800\uc640 \ubaa8\ub378 \ub85c\ub4dc\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n\n# \ub370\uc774\ud130 \ud1a0\ud06c\ub098\uc774\uc988\ntrain_encodings = tokenizer(X_train.tolist(), padding=True, truncation=True, return_tensors='pt')\ntest_encodings = tokenizer(X_test.tolist(), padding=True, truncation=True, return_tensors='pt')\n\n# PyTorch \ub370\uc774\ud130\uc14b \ud074\ub798\uc2a4 \uc815\uc758\nclass TextDataset(torch.utils.data.Dataset):\n    def __init__(self, encodings, labels):\n        self.encodings = encodings\n        self.labels = labels\n\n    def __getitem__(self, idx):\n        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n        item['labels'] = torch.tensor(self.labels[idx])\n        return item\n\n    def __len__(self):\n        return len(self.labels)\n\n# \ub370\uc774\ud130\uc14b \uc900\ube44\ntrain_dataset = TextDataset(train_encodings, y_train.tolist())\ntest_dataset = TextDataset(test_encodings, y_test.tolist())\n\n# \ud2b8\ub808\uc774\ub2dd \uc778\uc790 \uc124\uc815\ntraining_args = TrainingArguments(\n    output_dir='.\/results',\n    num_train_epochs=3,\n    per_device_train_batch_size=4,\n    per_device_eval_batch_size=4,\n    warmup_steps=500,\n    weight_decay=0.01,\n    logging_dir='.\/logs',\n)\n\n# \ud2b8\ub808\uc774\ub108 \uc815\uc758\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=train_dataset,\n    eval_dataset=test_dataset,\n)\n\n# \ubaa8\ub378 \ud559\uc2b5\ntrainer.train()<\/code><\/pre>\n
7. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
\ubaa8\ub378 \ud559\uc2b5\uc774 \uc644\ub8cc\ub418\uba74, \ud3c9\uac00\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc815\ud655\ub3c4\ub97c \uacc4\uc0b0\ud558\uace0, \ud63c\ub3c8\ud589\ub82c\uc744 \uc2dc\uac01\ud654\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ubd84\uc11d\ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.metrics import accuracy_score, confusion_matrix\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# \uc608\uce21 \uc218\ud589\npredictions = trainer.predict(test_dataset)\npreds = predictions.predictions.argmax(-1)\n\n# \uc815\ud655\ub3c4 \uacc4\uc0b0\naccuracy = accuracy_score(y_test, preds)\nprint(f'Accuracy: {accuracy:.2f}')\n\n# \ud63c\ub3c8 \ud589\ub82c \uc2dc\uac01\ud654\ncm = confusion_matrix(y_test, preds)\nplt.figure(figsize=(8, 6))\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive'])\nplt.ylabel('Actual')\nplt.xlabel('Predicted')\nplt.title('Confusion Matrix')\nplt.show()<\/code><\/pre>\n
8. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud55c \uae30\ubcf8\uc801\uc778 \uac10\uc815 \ubd84\uc11d \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud588\uc2b5\ub2c8\ub2e4. \uc815\uaddc\uc2dd \uc804\ucc98\ub9ac\ub97c \ud1b5\ud574 \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc744 \ub192\uc774\uace0, \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub192\uc740 \uc131\ub2a5\uc744 \ubc1c\ud718\ud560 \uc218 \uc788\ub294 \uac83\uc744 \ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 \ub354\uc6b1 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uae30\uc220\uc744 \ud65c\uc6a9\ud55c \ud504\ub85c\uc81d\ud2b8\ub97c \uc9c4\ud589\ud574\ubcf4\uc2dc\uba74 \uc88b\uc744 \uac83 \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
\uac10\uc0ac\ud569\ub2c8\ub2e4!<\/p>\n
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\ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5 \ubc0f \uba38\uc2e0\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \ub9ce\uc740 \ubd84\uc57c\uc5d0\uc11c \ub525\ub7ec\ub2dd \uae30\uc220\uc774 \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \ud65c\uc6a9\ud55c \ubb38\uc11c \ubd84\ub958 \uc608\uc81c\uc640 \ud568\uaed8, \uc815\uaddc\uc2dd\uc744 \uc0ac\uc6a9\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uae30\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub780? \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38(Hugging Face Transformers)\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uc5d0\uc11c \uc8fc\ub85c \uc0ac\uc6a9\ud558\ub294 \uc5ec\ub7ec … \ub354 \ubcf4\uae30 “\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \uc815\uaddc\uc2dd \ud65c\uc6a9 \uc804\ucc98\ub9ac”<\/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":[30],"tags":[],"class_list":["post-29688","post","type-post","status-publish","format-standard","hentry","category-30"],"yoast_head":"\n\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \uc815\uaddc\uc2dd \ud65c\uc6a9 \uc804\ucc98\ub9ac - 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