\ucd5c\uadfc \uba87 \ub144\uac04, \ub525\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uae09\uaca9\ud55c \ubc1c\uc804\uc744 \uc774\ub8e8\uc5c8\uc73c\uba70, \uadf8 \uc911 \uac00\uc7a5 \ud070 \uc120\ub450\uc8fc\uc790 \uc911 \ud558\ub098\ub294 \ubc14\ub85c BERT\uc785\ub2c8\ub2e4. BERT(Bidirectional Encoder Representations from Transformers)\ub294 \uad6c\uae00\uc5d0\uc11c \ubc1c\ud45c\ud55c \ubaa8\ub378\ub85c, \ubb38\ub9e5\uc744 \uc591 \ubc29\ud5a5\uc73c\ub85c \uc774\ud574\ud560 \uc218 \uc788\ub294 \ub2a5\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294BERT \ubaa8\ub378\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uace0, \uc5ec\ub7ec BERT \ubaa8\ub378\ub4e4\uc744 \uc559\uc0c1\ube14\ud558\uc5ec \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ubc29\ubc95\uacfc \ud568\uaed8 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \ud30c\uc778\ud29c\ub2dd\ud558\ub294 \uacfc\uc815\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
BERT\ub294 ‘Bidirectional Encoder Representations from Transformers’\uc758 \uc57d\uc790\ub85c, \ubb38\ub9e5\uc744 \uc774\ud574\ud558\ub294 \ub370 \ub9e4\uc6b0 \uac15\ub825\ud55c \ub2a5\ub825\uc744 \uc9c0\ub2cc \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 NLP \ubaa8\ub378\ub4e4\uc740 \uc77c\ubc18\uc801\uc73c\ub85c \ud55c \ubc29\ud5a5\uc73c\ub85c\ub9cc \ubb38\ub9e5\uc744 \uc774\ud574\ud588\uc9c0\ub9cc, BERT\ub294 Transformer \uc544\ud0a4\ud14d\ucc98\ub97c \uae30\ubc18\uc73c\ub85c \uc591 \ubc29\ud5a5\uc5d0\uc11c \ubb38\ub9e5 \uc815\ubcf4\ub97c \ub3d9\uc2dc\uc5d0 \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub2e8\uc5b4\uc758 \uc758\ubbf8\ub97c \ubb38\ub9e5\uc5d0 \ub9de\uac8c \ubf51\uc544\ub0b4\ub294 \ub370\uc5d0 \uac00\ub2a5\uc131\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n
\ud5c8\uae45\ud398\uc774\uc2a4\ub294 BERT\ub97c \ube44\ub86f\ud55c \ub2e4\uc591\ud55c \uc0ac\uc804 \ud559\uc2b5\ub41c NLP \ubaa8\ub378\ub4e4\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ud574\uc8fc\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uba74, \ubcf5\uc7a1\ud55c \uad6c\ud604 \uc5c6\uc774\ub3c4 \ub2e4\uc591\ud55c NLP \ud0dc\uc2a4\ud06c\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Hugging Face Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ub2e8\uc21c\ud55c \uc778\ud130\ud398\uc774\uc2a4\uc640 \ud568\uaed8 \uc9c1\uad00\uc801\uc778 API\ub97c \uc81c\uacf5\ud558\uc5ec \ud559\uc2b5 \ubc0f \ud30c\uc778\ud29c\ub2dd\uc744 \uc27d\uac8c \ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uc2b5\ub2c8\ub2e4.<\/p>\n
!pip install transformers<\/code><\/pre>\n3. BERT \uc559\uc0c1\ube14 \uae30\ubc95<\/h2>\n
\uc559\uc0c1\ube14 \uae30\ubc95\uc774\ub780 \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \uacb0\ud569\ud558\uc5ec \ub354 \ub098\uc740 \uc131\ub2a5\uc744 \uc5bb\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. BERT \ubaa8\ub378\uc744 \uc559\uc0c1\ube14\ud558\ub294 \uc774\uc720\ub294 \ubaa8\ub378 \uac04\uc758 \ub2e4\uc591\uc131\uc774 \uc788\uc5b4 \uc624\ubc84\ud53c\ud305\uc744 \ubc29\uc9c0\ud558\uace0, \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \uc559\uc0c1\ube14 \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uba74 BERT \ubaa8\ub378\uc758 \uac15\uc810\uc744 \ubcf4\ub2e4 \ud6a8\uacfc\uc801\uc73c\ub85c \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1. \uc559\uc0c1\ube14 \ubc29\ubc95\ub860<\/h3>\n
\uc804\ub7b5\uc740 \ub2e4\uc591\ud558\uc9c0\ub9cc, \uac00\uc7a5 \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \ubc29\ubc95 \uc911 \ub450 \uac00\uc9c0\ub294 \ud558\ub4dc \ubcf4\ud305<\/strong>\uacfc \uc18c\ud504\ud2b8 \ubcf4\ud305<\/strong>\uc785\ub2c8\ub2e4.<\/p>\n\n- \ud558\ub4dc \ubcf4\ud305:<\/strong> \uac01 \ubaa8\ub378\uc774 \uc608\uce21\ud55c \ud074\ub798\uc2a4 \ub77c\ubca8 \uc911 \uac00\uc7a5 \ub9ce\uc774 \uc120\ud0dd\ub41c \ub77c\ubca8\uc744 \uacb0\uacfc\ub85c \ucc44\ud0dd\ud569\ub2c8\ub2e4.<\/li>\n
- \uc18c\ud504\ud2b8 \ubcf4\ud305:<\/strong> \uac01 \ubaa8\ub378\uc774 \uc608\uce21\ud55c \ud074\ub798\uc2a4 \ud655\ub960\uc758 \ud3c9\uade0\uc744 \ub0b4\uc5b4, \uac00\uc7a5 \ub192\uc740 \ud655\ub960\uc758 \ud074\ub798\uc2a4\ub97c \uacb0\uacfc\ub85c \ucc44\ud0dd\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
4. BERT \ud30c\uc778\ud29c\ub2dd<\/h2>\n
\uc774\uc81c BERT \ubaa8\ub378\uc744 \ud30c\uc778\ud29c\ub2dd\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \ub2e8\uacc4\uc5d0 \ub530\ub77c BERT \ubaa8\ub378\uc744 \uc124\uc815\ud558\uace0, \uc559\uc0c1\ube14\ud558\ub294 \ubc29\ubc95\uc744 \uc9c4\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1. \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
\uba3c\uc800, \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \uc608\uc81c\uc5d0\uc11c\ub294 IMDB \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\ub294 \uae0d\uc815\/\ubd80\uc815 \ub9ac\ubdf0\ub85c \uad6c\ubd84\ub429\ub2c8\ub2e4.<\/p>\n
4.1.1. \ub370\uc774\ud130\uc14b \ub85c\ub529<\/h4>\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\n# IMDB \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ndata = pd.read_csv('imdb_reviews.csv')\ntrain_data, test_data = train_test_split(data, test_size=0.2)\n <\/code><\/pre>\n4.2. BERT \ubaa8\ub378 \ub85c\ub529<\/h3>\n
\uc774\uc81c \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom transformers import BertTokenizer, BertForSequenceClassification\n\n# BERT \ud1a0\ud06c\ub098\uc774\uc800 \ubc0f \ubaa8\ub378 \ub85c\ub4dc\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n <\/code><\/pre>\n4.3. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
BERT \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uae30 \uc704\ud574 \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \ud14d\uc2a4\ud2b8\ub97c \ud1a0\ud06c\ub098\uc774\uc988\ud558\uace0, \uc785\ub825 IDs\uc640 \uc5b4\ud150\uc158 \ub9c8\uc2a4\ud06c\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
\ndef preprocess_data(data):\n inputs = tokenizer(data['text'].tolist(), padding=True, truncation=True, return_tensors=\"pt\", max_length=512)\n labels = torch.tensor(data['label'].tolist())\n return inputs, labels\n\ntrain_inputs, train_labels = preprocess_data(train_data)\ntest_inputs, test_labels = preprocess_data(test_data)\n <\/code><\/pre>\n4.4. \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574 PyTorch\uc758 DataLoader\ub97c \uc0ac\uc6a9\ud558\uace0, Adam \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc124\uc815\ud569\ub2c8\ub2e4.<\/p>\n
\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom transformers import AdamW\n\ntrain_dataset = TensorDataset(train_inputs['input_ids'], train_inputs['attention_mask'], train_labels)\ntrain_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)\noptimizer = AdamW(model.parameters(), lr=1e-5)\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.train()\nfor epoch in range(3):\n for batch in train_loader:\n optimizer.zero_grad()\n input_ids, attention_mask, labels = batch\n outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n loss = outputs.loss\n loss.backward()\n optimizer.step()\n print(f'Epoch: {epoch}, Loss: {loss.item()}')\n <\/code><\/pre>\n4.5. \ud3c9\uac00 \ubc0f \uc559\uc0c1\ube14<\/h3>\n
\ud559\uc2b5\ud55c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uace0, \ub2e4\ub978 BERT \ubaa8\ub378\ub3c4 \ub3d9\uc77c\ud55c \ubc29\uc2dd\uc73c\ub85c \ud559\uc2b5\ud558\uc5ec \uc559\uc0c1\ube14\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \uac01 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc218\uc9d1\ud558\uace0, \ud558\ub4dc or \uc18c\ud504\ud2b8 \ubcf4\ud305\uc744 \ud1b5\ud574 \ucd5c\uc885 \uc608\uce21\uc744 \ud569\ub2c8\ub2e4.<\/p>\n
\n# \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uc559\uc0c1\ube14\ndef evaluate_and_ensemble(models, dataloader):\n ensemble_preds = []\n for model in models:\n model.eval()\n preds = []\n for batch in dataloader:\n input_ids, attention_mask = batch\n with torch.no_grad():\n outputs = model(input_ids, attention_mask=attention_mask)\n preds.append(torch.argmax(outputs.logits, dim=1))\n ensemble_preds.append(torch.cat(preds, dim=0))\n \n # \ud558\ub4dc \ubcf4\ud305\n final_preds = torch.mode(torch.stack(ensemble_preds), dim=0)[0]\n return final_preds\n\nfinal_predictions = evaluate_and_ensemble([model], test_loader)\n <\/code><\/pre>\n5. \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\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uc559\uc0c1\ube14\ud558\uc5ec \uc131\ub2a5\uc744 \ub192\uc774\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. BERT\uc758 \uac15\ub825\ud55c \uc5b8\uc5b4 \uc774\ud574 \ub2a5\ub825\uc744 \ubc14\ud0d5\uc73c\ub85c, \uc801\uc808\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc640 \uc559\uc0c1\ube14 \uae30\ubc95\uc744 \ud1b5\ud574 NLP \ud0dc\uc2a4\ud06c\uc5d0\uc11c \ub192\uc740 \uc131\ub2a5\uc744 \ub2ec\uc131\ud560 \uc218 \uc788\uc74c\uc744 \ubcf4\uc5ec\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 \uc774 \uae30\ubc95\uc744 \ud1b5\ud574 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\ub97c \ud574\uacb0\ud574 \ub098\uac08 \uc218 \uc788\uae30\ub97c \uae30\ub300\ud569\ub2c8\ub2e4.<\/p>\n
6. \ucc38\uace0 \uc790\ub8cc<\/h2>\n