\uc624\ub298\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc911\uc5d0\uc11c \uac00\uc7a5 \ub9ce\uc774 \uc0ac\uc6a9\ub418\ub294 BERT(Bidirectional Encoder Representations from Transformers)<\/strong> \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \uc559\uc0c1\ube14 \ud2b8\ub808\uc774\ub2dd\uc5d0 \uc801\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face)\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac<\/strong>\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\ud559\uc2b5\ub41c BERT \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc559\uc0c1\ube14 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n BERT \ubaa8\ub378\uc740 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\uae30 \uc704\ud574 \uc591\ubc29\ud5a5\uc73c\ub85c \uc804\uc774 \ud559\uc2b5\uc744 \uc218\ud589\ud558\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc989, \uc785\ub825 \ubb38\uc7a5\uc758 \uc67c\ud3b8\uacfc \uc624\ub978\ud3b8\uc758 \ub2e8\uc5b4\ub4e4\uc774 \ubb38\ub9e5\uc744 \uc5b4\ub5bb\uac8c \ud615\uc131\ud558\ub294\uc9c0\ub97c \ub3d9\uc2dc\uc5d0 \uace0\ub824\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub2e8\uc5b4\uc758 \uc758\ubbf8\ub97c \ub354\uc6b1 \uae4a\uc774 \uc788\ub294 \ubc29\uc2dd\uc73c\ub85c \uc774\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. BERT\ub294 unsupervised \ubc29\uc2dd\uc73c\ub85c \ub300\ub7c9\uc758 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0\uc11c \uc0ac\uc804\ud559\uc2b5\uc744 \uc9c4\ud589\ud55c \ud6c4, \ub2e4\uc591\ud55c \ub2e4\uc6b4\uc2a4\ud2b8\ub9bc \ud0dc\uc2a4\ud06c\uc5d0 \uc801\ud569\ud558\uac8c fine-tuning \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ud5c8\uae45\ud398\uc774\uc2a4 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 BERT\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc0ac\uc804\ud559\uc2b5 \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ud574\uc90d\ub2c8\ub2e4. \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\ud558\uc5ec \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n \uc774\uc81c Hugging Face Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\ud559\uc2b5\ub41c BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc624\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\uc758 \ucf54\ub4dc\ub294 BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800\ub97c \ubd88\ub7ec\uc624\ub294 \uac04\ub2e8\ud55c \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n \uc559\uc0c1\ube14 \ud559\uc2b5\uc740 \uc5ec\ub7ec \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc870\ud569\ud558\uc5ec \ucd5c\uc885 \uc608\uce21 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ub2e4\uc591\ud55c \ud559\uc2b5 \ubaa8\ub378\uc758 \uc7a5\uc810\uc744 \ud569\uccd0\uc11c \ubcf4\ub2e4 \uc2e0\ub8b0\ud560 \uc218 \uc788\ub294 \uc608\uce21 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc559\uc0c1\ube14 \ud559\uc2b5\uc5d0\ub294 \uc5ec\ub7ec\uac00\uc9c0 \uae30\ubc95\uc774 \uc788\uc744 \uc218 \uc788\uc73c\uba70, Bagging\uacfc Boosting \ubc29\uc2dd\uc774 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n \uc774\uc81c BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc559\uc0c1\ube14 \ubaa8\ub378\uc744 \uad6c\uc131\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec \uac1c\uc758 BERT \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0, \uadf8 \uc608\uce21 \uac12\uc744 \ud569\uccd0\uc11c \ucd5c\uc885 \uc608\uce21\uc744 \ub3c4\ucd9c\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \uc6b0\ub9ac\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uad6c\uc870\ub85c \uc559\uc0c1\ube14 \ubaa8\ub378\uc744 \uad6c\uc131\ud560 \uac83\uc785\ub2c8\ub2e4:<\/p>\n \uc6b0\ub9ac\ub294 \uac04\ub2e8\ud55c \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubb38\uc81c\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc774\uba54\uc77c \uc2a4\ud338 \ud544\ud130\ub9c1 \ub4f1\uc758 \ubb38\uc81c\ub97c \uac00\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uba3c\uc800, \uc544\ub798\uc640 \uac19\uc774 \ud3b8\ub9ac\ud55c \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud569\ub2c8\ub2e4.<\/p>\n \uc774\uc81c \uac01\uac01\uc758 BERT \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \ud559\uc2b5\ub41c \ubaa8\ub378\uc740 \uc559\uc0c1\ube14\uc744 \uc704\ud574 \uc800\uc7a5\ub429\ub2c8\ub2e4.<\/p>\n \ubaa8\ub378 \ud559\uc2b5\uc774 \uc644\ub8cc\ub418\uba74, \uac01 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uacb0\ud569\ud558\uc5ec \ucd5c\uc885 \uc608\uce21 \uac12\ub3c4 \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uc624\ub298\uc740 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\ud559\uc2b5\ub41c BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc624\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uac04\ub2e8\ud55c \uc559\uc0c1\ube14 \ud2b8\ub808\uc774\ub2dd \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. BERT\ub294 \ubcf5\uc7a1\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ud0dc\uc2a4\ud06c\uc5d0\uc11c \ud6cc\ub96d\ud55c \uc131\ub2a5\uc744 \ubcf4\uc774\uba70, \uc559\uc0c1\ube14 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uacbd\uc6b0 \ub354\uc6b1 \ud5a5\uc0c1\ub41c \uc131\ub2a5\uc744 \uae30\ub300\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub2e4\uc591\ud55c \ud0dc\uc2a4\ud06c\uc5d0\uc11c\ub3c4 \uc774\ub7ec\ud55c \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uc5ec \ub354 \ub098\uc740 \uacb0\uacfc\ub97c \ub9cc\ub4e4\uc5b4 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n\uc5b4\ub5bb\uac8c BERT\uac00 \uc791\ub3d9\ud558\ub294\uac00?<\/h2>\n
\ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h2>\n
pip install transformers torch<\/code><\/pre>\n\uc0ac\uc804\ud559\uc2b5\ub41c BERT \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30<\/h2>\n
\nfrom transformers import BertTokenizer, BertModel\n\n# BERT \ud1a0\ud06c\ub098\uc774\uc800\uc640 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertModel.from_pretrained('bert-base-uncased')\n\n# \ud14c\uc2a4\ud2b8 \ubb38\uc7a5\ntext = \"Hello, how are you?\"\ninputs = tokenizer(text, return_tensors='pt')\n\n# BERT \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uc5ec \ucd9c\ub825 \ubc1b\uae30\noutputs = model(**inputs)\nprint(outputs)\n <\/code><\/pre>\n\ucf54\ub4dc \uc124\uba85<\/h3>\n
\n
from transformers import BertTokenizer, BertModel<\/code>: \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38\uc5d0\uc11c BERT \ud1a0\ud06c\ub098\uc774\uc800\uc640 \ubaa8\ub378\uc744 \uac00\uc838\uc635\ub2c8\ub2e4.<\/li>\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')<\/code>: \uc0ac\uc804\ud559\uc2b5\ub41c BERT \ud1a0\ud06c\ub098\uc774\uc800\ub97c \ubd88\ub7ec\uc635\ub2c8\ub2e4.<\/li>\nmodel = BertModel.from_pretrained('bert-base-uncased')<\/code>: \uc0ac\uc804\ud559\uc2b5\ub41c BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4.<\/li>\ninputs = tokenizer(text, return_tensors='pt')<\/code>: \uc785\ub825 \ubb38\uc7a5\uc744 \ud1a0\ud070\ud654\ud558\uace0 PyTorch \ud150\uc11c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.<\/li>\noutputs = model(**inputs)<\/code>: \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uc5ec \ucd9c\ub825\uc744 \ubc1b\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n\uc559\uc0c1\ube14 \ud2b8\ub808\uc774\ub2dd \uac1c\uc694<\/h2>\n
BERT\ub85c \uc559\uc0c1\ube14 \ubaa8\ub378 \uad6c\uc131\ud558\uae30<\/h2>\n
\ubaa8\ub378 \uac1c\uc694<\/h3>\n
\n
\ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
\nimport pandas as pd\n\n# \uc608\uc2dc \ub370\uc774\ud130\uc14b \uc0dd\uc131\ndata = {'text': [\"Free money now\", \"Hello friend, how are you?\", \"Limited time offer\", \"Nice to see you\"],\n 'label': [1, 0, 1, 0]} # 1: \uc2a4\ud338, 0: \uc77c\ubc18 \uba54\uc77c\ndf = pd.DataFrame(data)\n <\/code><\/pre>\n\ubaa8\ub378 \ud559\uc2b5 \ubc0f \uc559\uc0c1\ube14 \uc218\ud589<\/h3>\n
\nfrom sklearn.model_selection import train_test_split\nimport torch\n\n# \ub370\uc774\ud130 \ubd84\ud560\ntrain_texts, test_texts, train_labels, test_labels = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42)\n\n# BERT \ubaa8\ub378\uc744 \uc704\ud55c \ub370\uc774\ud130 \uc900\ube44\ntrain_encodings = tokenizer(list(train_texts), truncation=True, padding=True, return_tensors='pt')\ntest_encodings = tokenizer(list(test_texts), truncation=True, padding=True, return_tensors='pt')\n\nclass BERTClassifier(torch.nn.Module):\n def __init__(self):\n super(BERTClassifier, self).__init__()\n self.bert = BertModel.from_pretrained('bert-base-uncased')\n self.classifier = torch.nn.Linear(self.bert.config.hidden_size, 2) # 2 \ud074\ub798\uc2a4 (\uc2a4\ud338, \ube44\uc2a4\ud338)\n\n def forward(self, input_ids, attention_mask):\n output = self.bert(input_ids, attention_mask=attention_mask)[1]\n return self.classifier(output)\n\n# \ubaa8\ub378 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc120\uc5b8\nmodel1 = BERTClassifier()\nmodel2 = BERTClassifier() # \ub450 \ubc88\uc9f8 \ubaa8\ub378 \uc608\uc2dc\noptimizer = torch.optim.Adam(model1.parameters(), lr=5e-5)\n\n# \uac04\ub2e8\ud55c \ud559\uc2b5 \ub8e8\ud504\nmodel1.train()\nfor epoch in range(3): # 3\ubc88 \uc5d0\ud3ed\n optimizer.zero_grad()\n outputs = model1(input_ids=train_encodings['input_ids'], attention_mask=train_encodings['attention_mask'])\n loss = torch.nn.CrossEntropyLoss()(outputs, torch.tensor(train_labels.values))\n loss.backward()\n optimizer.step()\n print(f'Epoch {epoch + 1}, Loss: {loss.item()}')\n # \ub3d9\uc77c\ud55c \ubc29\uc2dd\uc73c\ub85c model2\ub3c4 \ud559\uc2b5\n\n# \ubaa8\ub378 \uc800\uc7a5\ntorch.save(model1.state_dict(), 'bert_model1.pth')\ntorch.save(model2.state_dict(), 'bert_model2.pth')\n <\/code><\/pre>\n\uc608\uce21 \uc218\ud589 \ubc0f \uc559\uc0c1\ube14 \uacb0\uacfc<\/h2>\n
\n# \uc608\uce21 \ud568\uc218 \uc815\uc758\ndef predict(model, encodings):\n model.eval()\n with torch.no_grad():\n outputs = model(input_ids=encodings['input_ids'], attention_mask=encodings['attention_mask'])\n return torch.argmax(outputs, dim=1)\n\n# \ubaa8\ub378 \ub85c\ub4dc\nmodel1.load_state_dict(torch.load('bert_model1.pth'))\nmodel2.load_state_dict(torch.load('bert_model2.pth'))\n\n# \uac1c\ubcc4 \ubaa8\ub378 \uc608\uce21\npreds_model1 = predict(model1, test_encodings)\npreds_model2 = predict(model2, test_encodings)\n\n# \uc559\uc0c1\ube14 \uc608\uce21\nfinal_preds = (preds_model1 + preds_model2) \/ 2\nfinal_preds = (final_preds > 0.5).int() # 0.5\ub97c \uc784\uacc4\uac12\uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec \uc774\uc9c4 \uc608\uce21\nprint(f'\ucd5c\uc885 \uc608\uce21: {final_preds}')\n <\/code><\/pre>\n\uacb0\ub860<\/h2>\n
\ucc38\uace0 \uc790\ub8cc<\/h2>\n