\ub525\ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uc758 \ubc1c\uc804\uc740 \ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \uae30\ud558\uae09\uc218\uc801\uc73c\ub85c \uc99d\uac00\ud558\uc600\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc2ec\uc5d0\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ub77c\uc774\ube0c\ub7ec\ub9ac\uac00 \uc790\ub9ac\ud558\uace0 \uc788\ub294\ub370, \uc774\ub294 \ud2b8\ub79c\uc2a4\ud3ec\uba38(Transformer) \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ud574\uc90d\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc758 \uac1c\uc694, \uc124\uce58 \ubc29\ubc95, \uae30\ucd08\ubd80\ud130 \uc2ec\ud654\uae4c\uc9c0 \uc0ac\uc6a9\ud574\ubcf4\ub294 \uc608\uc81c\ub97c \uc81c\uacf5\ud558\uba70, \uc774\ub97c \ud65c\uc6a9\ud55c \uc9c8\uc758\uc751\ub2f5 \uc2dc\uc2a4\ud15c \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ud574\uc8fc\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. BERT, GPT-2, T5\ub4f1\uc758 \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \uc9c0\uc6d0\ud558\uba70, \ub2e8\uc21c\ud55c API \ud638\ucd9c\ub85c \ud6a8\uacfc\uc801\uc73c\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub2e4\uc74c\uacfc \uac19\uc740 \uc791\uc5c5\ub4e4\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574 \uba3c\uc800 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
pip install transformers<\/code><\/pre>\n3. \uae30\ubcf8 \ucf54\ub4dc \uc608\uc81c<\/h2>\n3.1. BERT \ubaa8\ub378\uc744 \uc774\uc6a9\ud55c \ud14d\uc2a4\ud2b8 \ubd84\ub958<\/h3>\n
\uba3c\uc800, BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud55c \ud14d\uc2a4\ud2b8 \ubd84\ub958 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. BERT\ub294 Bidirectional Encoder Representations from Transformers\uc758 \uc904\uc784\ub9d0\ub85c, \ubb38\ub9e5\uc744 \uc774\ud574\ud558\ub294 \ub370 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc778 \ubaa8\ub378\uc785\ub2c8\ub2e4.<\/p>\n
from transformers import BertTokenizer, BertForSequenceClassification\nfrom transformers import Trainer, TrainingArguments\nimport torch\n\n# \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800 \ub85c\ub4dc\nmodel_name = 'bert-base-uncased'\ntokenizer = BertTokenizer.from_pretrained(model_name)\nmodel = BertForSequenceClassification.from_pretrained(model_name)\n\n# \ub370\uc774\ud130 \uc900\ube44\ntexts = [\"I love programming\", \"I hate bugs\"]\nlabels = [1, 0] # 1: \uae0d\uc815, 0: \ubd80\uc815\nencodings = tokenizer(texts, truncation=True, padding=True, return_tensors='pt')\n\n# \ub370\uc774\ud130\uc14b \uc0dd\uc131\nclass Dataset(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: 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\ndataset = Dataset(encodings, labels)\n\n# \ud6c8\ub828 \uc778\uc790 \uc124\uc815\ntraining_args = TrainingArguments(\n output_dir='.\/results',\n num_train_epochs=1,\n per_device_train_batch_size=2,\n logging_dir='.\/logs',\n)\n\n# \ud2b8\ub808\uc774\ub108 \uc0dd\uc131\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=dataset,\n)\n\n# \ubaa8\ub378 \ud6c8\ub828\ntrainer.train()\n<\/code><\/pre>\n3.2. \uc9c8\uc758 \uc751\ub2f5 \ubaa8\ub378 \uc0ac\uc6a9\ud558\uae30<\/h3>\n
\uc774\uc81c \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc9c8\uc758 \uc751\ub2f5 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\uc758 \uc608\uc81c \ucf54\ub4dc\ub294 \uc0ac\uc804 \ud6c8\ub828\ub41c BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc9c8\ubb38\uc5d0 \ub300\ud55c \ub2f5\ubcc0\uc744 \ucc3e\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n
from transformers import pipeline\n\n# \uc9c8\uc758 \uc751\ub2f5 \ud30c\uc774\ud504\ub77c\uc778 \ub85c\ub4dc\nqa_pipeline = pipeline('question-answering')\n\n# \uc9c8\ubb38\uacfc \ucee8\ud14d\uc2a4\ud2b8 \uc124\uc815\ncontext = \"Hugging Face is creating a tool that democratizes AI.\"\nquestions = [\"What is Hugging Face creating?\", \"What does it do?\"]\n\n# \uc9c8\uc758 \uc751\ub2f5 \uc218\ud589\nfor question in questions:\n result = qa_pipeline(question=question, context=context)\n print(f\"Question: {question}\\nAnswer: {result['answer']}\\n\")\n<\/code><\/pre>\n4. \uc2ec\ud654 \uc8fc\uc81c: \uc0ac\uc6a9\uc790 \uc815\uc758 \ub370\uc774\ud130\ub85c \ubaa8\ub378 \ud6c8\ub828\ud558\uae30<\/h2>\n
\ud5c8\uae45\ud398\uc774\uc2a4\ub294 \uc0ac\uc6a9\uc790\uac00 \uc790\uc2e0\uc758 \ub370\uc774\ud130\uc14b\uc73c\ub85c \ubaa8\ub378\uc744 \ud6c8\ub828\ud560 \uc218 \uc788\ub294 \uc720\uc5f0\ud568\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc2a4\ud338 \uba54\uc2dc\uc9c0\ub97c \ubd84\ub958\ud558\ub294 \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uace0 \uc2f6\ub2e4\uba74 \uc544\ub798\uc640 \uac19\uc740 \ubc29\ubc95\uc73c\ub85c \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1. \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
CSV \ud30c\uc77c \ud615\uc2dd\uc73c\ub85c \ub370\uc774\ud130\ub97c \uc900\ube44\ud588\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. \uac01 \ud589\uc740 \ud14d\uc2a4\ud2b8\uc640 \ub808\uc774\ube14\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4.<\/p>\n
import pandas as pd\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\ndf = pd.read_csv('spam_data.csv')\ntexts = df['text'].tolist()\nlabels = df['label'].tolist()\n<\/code><\/pre>\n4.2. \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
\uc774\uc81c \uc704\uc5d0\uc11c \uc124\uba85\ud55c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud6c8\ub828\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
encodings = tokenizer(texts, truncation=True, padding=True, return_tensors='pt')\n\n# \ub370\uc774\ud130\uc14b \uc0dd\uc131\ndataset = Dataset(encodings, labels)\n\n# \ud6c8\ub828 \uc2dc\uc791\ntrainer.train()\n<\/code><\/pre>\n5. \uc9c8\uc758\uc751\ub2f5<\/h2>\n5.1. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 \ubb34\uc5c7\uc778\uac00\uc694?<\/h3>\n
\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 NLP \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ud574\uc8fc\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub2e4\uc591\ud55c \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\uc5ec \ud14d\uc2a4\ud2b8 \ucc98\ub9ac \uc791\uc5c5\uc744 \uc6d0\ud65c\ud558\uac8c \uc218\ud589\ud560 \uc218 \uc788\uac8c \ud574\uc90d\ub2c8\ub2e4.<\/p>\n
5.2. \uc5b4\ub5bb\uac8c \uc124\uce58\ud558\ub098\uc694?<\/h3>\n
pip \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc790\uc138\ud55c \uc124\uce58 \ubc29\ubc95\uc740 \ubcf8 \uac15\uc88c\ub97c \ucc38\uace0\ud558\uc138\uc694.<\/p>\n
5.3. \uc608\uc81c \ucf54\ub4dc\uc5d0\uc11c \uc624\ub958\uac00 \ubc1c\uc0dd\ud588\uc2b5\ub2c8\ub2e4. \uc5b4\ub5bb\uac8c \ud574\uacb0\ud558\ub098\uc694?<\/h3>\n
\uc624\ub958\uac00 \ubc1c\uc0dd\ud588\uc744 \uacbd\uc6b0, \uc8fc\ub85c \ub77c\uc774\ube0c\ub7ec\ub9ac \ubc84\uc804\uc774\ub098 \ub370\uc774\ud130 \ud615\uc2dd \ubb38\uc81c\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc624\ub958 \uba54\uc2dc\uc9c0\ub97c \ud655\uc778\ud558\uace0 \ud544\uc694\uc5d0 \ub530\ub77c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\uac70\ub098 \ub370\uc774\ud130\ub97c \uc810\uac80\ud574\ubcf4\uc138\uc694.<\/p>\n
5.4. \uc0ac\uc6a9\uc790 \uc815\uc758 \ub370\uc774\ud130\ub85c \ud6c8\ub828\ud560 \uc218 \uc788\ub098\uc694?<\/h3>\n
\ub124, \ud5c8\uae45\ud398\uc774\uc2a4\ub294 \uac1c\uc778\uc758 \ub370\uc774\ud130\uc14b\uc73c\ub85c \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \ubc29\ubc95\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \ud544\uc694\ud55c \ub370\uc774\ud130 \ud615\uc2dd\uc73c\ub85c \uc900\ube44\ud55c \ud6c4, \uc704\uc758 \ud6c8\ub828 \uacfc\uc815\uc744 \ub530\ub974\uc2dc\uba74 \ub429\ub2c8\ub2e4.<\/p>\n
6. \uacb0\ub860<\/h2>\n
\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 NLP\uc640 \ub525\ub7ec\ub2dd \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc744 \uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc8fc\ub294 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uc774 \uac15\uc88c\ub97c \ud1b5\ud574 \uae30\ubcf8\uc801\uc778 \uc0ac\uc6a9\ubc95\uacfc \ubaa8\ub378 \ud6c8\ub828 \ubc29\ubc95\uc744 \uc775\ud614\uae38 \ubc14\ub77c\uba70, \uc55e\uc73c\ub85c\ub3c4 \ub2e4\uc591\ud55c \ud504\ub85c\uc81d\ud2b8\uc5d0 \ud65c\uc6a9\ud574\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
7. \ucc38\uace0 \uc790\ub8cc<\/h2>\n