!pip install transformers torch numpy pandas<\/code><\/pre>\n2. \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\uc6b0\uc120, \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958 \ubb38\uc81c\ub97c \uc704\ud55c \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \uc608\uc2dc\ub85c, \uac04\ub2e8\ud55c \ud14d\uc2a4\ud2b8\uc640 \ub77c\ubca8\uc744 \uac00\uc9c4 \ub370\uc774\ud130\ud504\ub808\uc784\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import pandas as pd\n\ndata = {\n 'text': [\n '\ub098\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uac00 \uc88b\uc544\uc694.',\n 'PyTorch\uc640 TensorFlow\uac00 \uc778\uae30 \uc788\uc5b4\uc694.',\n '\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4.',\n '\ub300\ud654\ud615 AI\ub294 \ub9ce\uc740 \uad00\uc2ec\uc744 \ubc1b\uace0 \uc788\uc5b4\uc694.',\n '\ud14d\uc2a4\ud2b8 \ubd84\ub958\ub294 \uc911\uc694\ud55c \uc791\uc5c5\uc785\ub2c8\ub2e4.'\n ],\n 'label': [0, 1, 1, 2, 0]\n}\n\ndf = pd.DataFrame(data)<\/code><\/pre>\n3. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
BERT \ubaa8\ub378\uc758 \uc785\ub825 \ud615\uc2dd\uc5d0 \ub9de\uac8c \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud569\ub2c8\ub2e4. BERT Tokenizer\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8\ub97c \ud1a0\ud070\ud654\ud558\uace0, \uc785\ub825 \uc544\uc774\ub514\uc640 \uc5b4\ud150\uc158 \ub9c8\uc2a4\ud06c\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
from transformers import BertTokenizer\n\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n\n# \ud1a0\ud070\ud654 \ubc0f \uc785\ub825 \uc544\uc774\ub514, \uc5b4\ud150\uc158 \ub9c8\uc2a4\ud06c \uc0dd\uc131\ndef encode_data(text):\n return tokenizer(text, padding='max_length', truncation=True, max_length=128, return_tensors='pt')\n\nencoded_texts = [encode_data(text)['input_ids'] for text in df['text']]\nattention_masks = [encode_data(text)['attention_mask'] for text in df['text']]<\/code><\/pre>\n4. \ub370\uc774\ud130\uc14b \ubd84\ud560<\/h3>\n
\ub370\uc774\ud130\ub97c \ud559\uc2b5\uc6a9\uacfc \uac80\uc99d\uc6a9\uc73c\ub85c \ubd84\ud560\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \ub370\uc774\ud130\uc758 80%\ub97c \ud559\uc2b5 \ub370\uc774\ud130\ub85c, 20%\ub97c \uac80\uc99d \ub370\uc774\ud130\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\n\nX_train, X_val, y_train, y_val = train_test_split(\n df['text'],\n df['label'],\n test_size=0.2,\n random_state=42\n)<\/code><\/pre>\n5. \ub370\uc774\ud130 \ub85c\ub354 \uc0dd\uc131<\/h3>\n
PyTorch\uc758 DataLoader\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc30\uce58 \ucc98\ub9ac\ub97c \uc704\ud55c \ub370\uc774\ud130 \ub85c\ub354\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
import torch\nfrom torch.utils.data import Dataset, DataLoader\n\nclass TextDataset(Dataset):\n def __init__(self, texts, labels):\n self.texts = texts\n self.labels = labels\n\n def __len__(self):\n return len(self.texts)\n\n def __getitem__(self, idx):\n text = encode_data(self.texts[idx])\n return {\n 'input_ids': text['input_ids'].squeeze(),\n 'attention_mask': text['attention_mask'].squeeze(),\n 'labels': torch.tensor(self.labels[idx])\n }\n\ntrain_dataset = TextDataset(X_train.tolist(), y_train.tolist())\nval_dataset = TextDataset(X_val.tolist(), y_val.tolist())\n\ntrain_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)\nval_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)<\/code><\/pre>\n6. \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30<\/h3>\n
\ud5c8\uae45\ud398\uc774\uc2a4\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\uc5d0\uc11c \uc0ac\uc804 \ud559\uc2b5\ub41c BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4. \uc5ec\uae30\uc5d0 \ubd84\ub958\uae30\ub97c \ucd94\uac00\ud558\uc5ec \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud569\ub2c8\ub2e4.<\/p>\n
from transformers import BertForSequenceClassification\n\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=3)<\/code><\/pre>\n7. \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574 \uc624\ucc28 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc124\uc815\ud558\uace0, \ub2e8\uc21c\ud55c \ud559\uc2b5 \ub8e8\ud504\ub97c \uc791\uc131\ud574 \ubd05\ub2c8\ub2e4.<\/p>\n
from transformers import AdamW\nfrom tqdm import tqdm\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel.to(device)\n\noptimizer = AdamW(model.parameters(), lr=1e-5)\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.train()\nfor epoch in range(3): # \uc5d0\ud3ec\ud06c \uc218\n for batch in tqdm(train_loader):\n input_ids = batch['input_ids'].to(device)\n attention_mask = batch['attention_mask'].to(device)\n labels = batch['labels'].to(device)\n\n optimizer.zero_grad()\n outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n loss = outputs.loss\n loss.backward()\n optimizer.step()\n\n print(f'Epoch {epoch+1} Loss: {loss.item()}')<\/code><\/pre>\n8. \uac80\uc99d \ubc0f \uc131\ub2a5 \ud3c9\uac00<\/h3>\n
\uac80\uc99d \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uc815\ud655\ub3c4\ub97c \uce21\uc815\ud569\ub2c8\ub2e4.<\/p>\n
model.eval()\ncorrect = 0\ntotal = 0\n\nwith torch.no_grad():\n for batch in val_loader:\n input_ids = batch['input_ids'].to(device)\n attention_mask = batch['attention_mask'].to(device)\n labels = batch['labels'].to(device)\n\n outputs = model(input_ids, attention_mask=attention_mask)\n _, predicted = torch.max(outputs.logits, dim=1)\n\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\naccuracy = correct \/ total\nprint(f'Accuracy: {accuracy:.2f}') <\/code><\/pre>\n9. \uacb0\ub860<\/h3>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud558\uc5ec \uc0ac\uc804 \ud559\uc2b5\ub41c BERT \ubaa8\ub378\uc744 \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958 \ubb38\uc81c\uc5d0 \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4. BERT\ub294 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubcf4\uc774\ubbc0\ub85c, \ubd84\uc11d\ud558\uace0\uc790 \ud558\ub294 \ub9ce\uc740 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc2e4\uc81c \ud504\ub85c\uc81d\ud2b8\uc5d0 \uc0ac\uc6a9\ud558\ub294 \uacfc\uc815\uc5d0\uc11c \uc5ec\ub7ec \uc2e4\ud5d8\uacfc \ud29c\ub2dd\uc744 \ud1b5\ud574 \ucd5c\uc801\uc758 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\ub3c4\ub85d \ud574\uc57c \ud569\ub2c8\ub2e4. \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ubaa8\ub378\ub4e4\uc740 \ube60\ub974\uac8c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\ubbc0\ub85c, \uc9c0\uc18d\uc801\uc778 \ud559\uc2b5\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\ub354 \uad81\uae08\ud55c \uc810\uc774 \uc788\ub2e4\uba74 \uc5b8\uc81c\ub4e0\uc9c0 \uc9c8\ubb38\ud574 \uc8fc\uc138\uc694!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958\uc6a9 BERT \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30 BERT(Bidirectional Encoder Representations from Transformers)\ub294 \uad6c\uae00\uc5d0\uc11c \uc81c\uc548\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378\ub85c, \ubb38\ub9e5 \uc774\ud574\ub97c \uc704\ud574 \uc591\ubc29\ud5a5 Transformer \uad6c\uc870\ub97c \ud65c\uc6a9\ud569\ub2c8\ub2e4. BERT\ub294 \uc0ac\uc804 \ud559\uc2b5(pre-training) \ubc0f \ubbf8\uc138 \uc870\uc815(fine-tuning) \ub2e8\uacc4\ub97c \ud1b5\ud574 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ud0dc\uc2a4\ud06c\uc5d0 \uc801\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\ud559\uc2b5\ub41c BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc640 \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 … \ub354 \ubcf4\uae30 “\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958\uc6a9 BERT \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30”<\/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-29652","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, \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958\uc6a9 BERT \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/29652\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958\uc6a9 BERT \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958\uc6a9 BERT \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30 BERT(Bidirectional Encoder Representations from Transformers)\ub294 \uad6c\uae00\uc5d0\uc11c \uc81c\uc548\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378\ub85c, \ubb38\ub9e5 \uc774\ud574\ub97c \uc704\ud574 \uc591\ubc29\ud5a5 Transformer \uad6c\uc870\ub97c \ud65c\uc6a9\ud569\ub2c8\ub2e4. BERT\ub294 \uc0ac\uc804 \ud559\uc2b5(pre-training) \ubc0f \ubbf8\uc138 \uc870\uc815(fine-tuning) \ub2e8\uacc4\ub97c \ud1b5\ud574 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ud0dc\uc2a4\ud06c\uc5d0 \uc801\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\ud559\uc2b5\ub41c BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc640 \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 … \ub354 \ubcf4\uae30 "\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958\uc6a9 BERT \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29652\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T01:55:55+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:51:52+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"2\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/29652\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29652\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958\uc6a9 BERT \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30\",\"datePublished\":\"2024-10-28T01:55:55+00:00\",\"dateModified\":\"2024-11-26T06:51:52+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29652\/\"},\"wordCount\":29,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud65c\uc6a9\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/29652\/\",\"url\":\"https:\/\/atmokpo.com\/w\/29652\/\",\"name\":\"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ub2e4\uc911 \ud074\ub798\uc2a4 \ubd84\ub958\uc6a9 BERT \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30 - 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