{"id":29710,"date":"2024-10-28T01:56:12","date_gmt":"2024-10-28T01:56:12","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29710"},"modified":"2024-11-26T06:51:38","modified_gmt":"2024-11-26T06:51:38","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-%ed%95%99%ec%8a%b5-%eb%8d%b0%ec%9d%b4%ed%84%b0%ec%84%b8%ed%8a%b8-2","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29710\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ud559\uc2b5 \ub370\uc774\ud130\uc138\ud2b8\ub97c \ud65c\uc6a9\ud55c BERT \uc559\uc0c1\ube14 \ud559\uc2b5 \ubc0f \uc608\uce21"},"content":{"rendered":"

<\/p>\n

\n \ub525\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP)\uc758 \ubc1c\uc804\uc5d0\ub294
\n \ub2e4\uc591\ud55c \ud601\uc2e0\uc801 \ubaa8\ub378\uc774 \uae30\uc5ec\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \ud558\ub098\uac00 \ubc14\ub85c BERT(Bidirectional Encoder Representations from Transformers)\uc785\ub2c8\ub2e4.
\n BERT\ub294 \ubb38\ub9e5\uc744 \uc774\ud574\ud558\ub294 \ub370 \ub9e4\uc6b0 \uac15\ub825\ud558\uba70, \ud14d\uc2a4\ud2b8 \ubd84\ub958, \uc9c8\uc758\uc751\ub2f5, \uac10\uc815 \ubd84\uc11d \ub4f1 \ub2e4\uc591\ud55c NLP \uacfc\uc81c\uc5d0\uc11c
\n \ucd5c\ucca8\ub2e8 \uc131\ub2a5\uc744 \ubcf4\uc774\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud574
\n BERT \ubaa8\ub378\uc744 \uc559\uc0c1\ube14 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uacfc \uc774\ub97c \ud1b5\ud55c \uc608\uce21 \uacfc\uc815\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

1. BERT \ubaa8\ub378 \uc774\ud574\ud558\uae30<\/h2>\n

\n BERT\ub294 Transformer \uc544\ud0a4\ud14d\ucc98\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \uc0ac\uc804 \ud559\uc2b5\ub41c \uc5b8\uc5b4 \ubaa8\ub378\ub85c,
\n \uc77c\ubc18\uc801\uc778 \ubc29\ud5a5\uc131\uc744 \uac00\uc9c0\uace0 \uc788\uc9c0 \uc54a\uace0 \uc591 \ubc29\ud5a5\uc73c\ub85c \ud14d\uc2a4\ud2b8\ub97c \uc778\ucf54\ub529\ud558\uc5ec \ubb38\ub9e5\uc744 \uc798 \ud30c\uc545\ud569\ub2c8\ub2e4.
\n BERT \ubaa8\ub378\uc740 \ub450 \uac00\uc9c0 \uc8fc\uc694 \uacfc\uc81c, \uc989 \ub9c8\uc2a4\ud06c \uc5b8\uc5b4 \ubaa8\ub378(Masked Language Model)\uacfc \ub2e4\uc74c \ubb38\uc7a5 \uc608\uce21(Next Sentence Prediction)\uc73c\ub85c \uc0ac\uc804 \ud559\uc2b5\ub429\ub2c8\ub2e4.\n <\/p>\n

1.1 \ub9c8\uc2a4\ud06c \uc5b8\uc5b4 \ubaa8\ub378<\/h3>\n

\n \ub9c8\uc2a4\ud06c \uc5b8\uc5b4 \ubaa8\ub378\uc5d0\uc11c\ub294 \uc785\ub825 \ubb38\uc7a5\uc5d0\uc11c \uc77c\ubd80 \ub2e8\uc5b4\ub97c \ub9c8\uc2a4\ud0b9\ud558\uace0,
\n \ubaa8\ub378\uc774 \ub9c8\uc2a4\ud06c\ub41c \ub2e8\uc5b4\ub97c \uc608\uce21\ud558\ub3c4\ub85d \ud559\uc2b5\ud569\ub2c8\ub2e4.
\n \uc774\ub97c \ud1b5\ud574 \ubb38\ub9e5\uc5d0 \ub530\ub978 \ub2e8\uc5b4\uc758 \uc758\ubbf8\ub97c \ud30c\uc545\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

1.2 \ub2e4\uc74c \ubb38\uc7a5 \uc608\uce21<\/h3>\n

\n \uc774 \uacfc\uc81c\uc5d0\uc11c\ub294 \ub450 \ubb38\uc7a5\uc744 \uc785\ub825\ubc1b\uc544 \ub450 \ubb38\uc7a5\uc774 \uc5f0\uc18d\uc801\uc778 \ubb38\uc7a5\uc778\uc9c0 \uc544\ub2cc\uc9c0\ub97c \ud310\ubcc4\ud569\ub2c8\ub2e4.
\n \uc774\ub97c \ud1b5\ud574 \ubb38\uc7a5 \uac04\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4.\n <\/p>\n

2. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \uc18c\uac1c<\/h2>\n

\n \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \uc804 \uc138\uacc4 \ub2e4\uc591\ud55c NLP \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud504\ub808\uc784\uc6cc\ud06c\uc785\ub2c8\ub2e4.
\n \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ubaa8\ub378 \ub85c\ub4dc\uc640 \ub370\uc774\ud130 \ucc98\ub9ac, \ud559\uc2b5, \uc608\uce21\uc744 \uc704\ud55c \ub2e4\uc591\ud55c \uc720\ud2f8\ub9ac\ud2f0\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.
\n \ud2b9\ud788 BERT \ubc0f \ub2e4\ub978 Transformer \uae30\ubc18 \ubaa8\ub378\ub4e4\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \uc778\ud130\ud398\uc774\uc2a4\ub97c \uac16\ucd94\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

3. \ub370\uc774\ud130 \uc900\ube44<\/h2>\n

\n \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 IMDB \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc601\ud654 \ub9ac\ubdf0\uc758 \uae0d\uc815\/\ubd80\uc815\uc744 \uc608\uce21\ud558\ub294 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uaca0\uc2b5\ub2c8\ub2e4.
\n \ub370\uc774\ud130\uc14b\uc740 \uacf5\uac1c\ub41c \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.
\n \uba3c\uc800 \ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0 \uc804\ucc98\ub9ac\ud558\ub294 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubd05\uc2dc\ub2e4.\n <\/p>\n

3.1 \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac<\/h3>\n
\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\n# IMDB \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc\nurl = \"https:\/\/ai.stanford.edu\/~amaas\/data\/sentiment\/aclImdb_v1.tar.gz\"\n!wget {url} -O aclImdb_v1.tar.gz\n!tar -xvf aclImdb_v1.tar.gz\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ntrain_data = pd.read_csv(\"aclImdb\/train.csv\")\ntest_data = pd.read_csv(\"aclImdb\/test.csv\")\n\n# \ud559\uc2b5 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c \ubd84\ub9ac\nX_train, X_test, y_train, y_test = train_test_split(train_data['review'], train_data['label'], \n                                                    test_size=0.2, random_state=42)\n    <\/pre>\n
4. BERT \ubaa8\ub378 \ub85c\ub4dc \ubc0f \ud559\uc2b5<\/h2>\n
\n        \uc774\uc81c BERT \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0 \ud559\uc2b5\ud560 \uc900\ube44\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4.
\n        \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 BERT \ubaa8\ub378\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n        \uba3c\uc800 \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800\ub97c \ub85c\ub4dc\ud55c \ud6c4, \ub370\uc774\ud130\uc14b\uc744 BERT \uc785\ub825 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
4.1 \ubaa8\ub378 \ubc0f \ud1a0\ud06c\ub098\uc774\uc800 \ub85c\ub4dc<\/h3>\n
\nfrom transformers import BertTokenizer, BertForSequenceClassification\nimport torch\n\n# BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800 \ub85c\ub4dc\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n    <\/pre>\n
4.2 \ub370\uc774\ud130\uc14b \ud1a0\ud070\ud654<\/h3>\n
\n# \ub370\uc774\ud130\uc14b\uc744 BERT \uc785\ub825 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ndef tokenize_data(texts):\n    return tokenizer(texts.tolist(), padding=True, truncation=True, return_tensors='pt')\n\ntrain_encodings = tokenize_data(X_train)\ntest_encodings = tokenize_data(X_test)\n    <\/pre>\n
5. \ubaa8\ub378 \uc559\uc0c1\ube14 \ud559\uc2b5<\/h2>\n
\n        \ubaa8\ub378 \uc559\uc0c1\ube14\uc740 \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \uacb0\ud569\ud558\uc5ec \ub354 \ub098\uc740 \uc131\ub2a5\uc744 \ub2ec\uc131\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.
\n        BERT\ub97c \uae30\ubc18\uc73c\ub85c \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0 \uc774\ub4e4\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uacb0\ud569\ud558\uc5ec \ucd5c\uc885 \uacb0\uacfc\ub97c \ub3c4\ucd9c\ud569\ub2c8\ub2e4.
\n        \ub2e4\uc74c\uc740 \ubaa8\ub378 \uc559\uc0c1\ube14\uc744 \uad6c\ud604\ud558\ub294 \ucf54\ub4dc\uc785\ub2c8\ub2e4.\n    <\/p>\n
5.1 \ud559\uc2b5 \ubc0f \uc608\uce21 \ud568\uc218 \uc815\uc758<\/h3>\n
\ndef train_and_evaluate(model, train_encodings, labels):\n    # \ubaa8\ub378 \ud6c8\ub828 \ubc0f \ud3c9\uac00 \ub85c\uc9c1\n    inputs = {'input_ids': train_encodings['input_ids'],\n              'attention_mask': train_encodings['attention_mask'],\n              'labels': torch.tensor(labels.tolist())}\n    \n    optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)\n    model.train()\n    \n    for epoch in range(3):  # \uc5ec\ub7ec \uc5d0\ud3ed \ub3d9\uc548 \ud559\uc2b5\n        outputs = model(**inputs)\n        loss = outputs[0]\n        loss.backward()\n        optimizer.step()\n        optimizer.zero_grad()\n        print(f'Epoch: {epoch}, Loss: {loss.item()}')\n\ndef predict(model, test_encodings):\n    model.eval()\n    with torch.no_grad():\n        outputs = model(**test_encodings)\n        logits = outputs[0]\n    return logits.argmax(dim=1)\n    <\/pre>\n
5.2 \ubaa8\ub378 \uc559\uc0c1\ube14 \uc2e4\ud589<\/h3>\n
\n# \uc559\uc0c1\ube14\ud560 \ubaa8\ub378 \ubaa9\ub85d\nmodels = [BertForSequenceClassification.from_pretrained('bert-base-uncased') for _ in range(5)]\npredictions = []\n\nfor model in models:\n    train_and_evaluate(model, train_encodings, y_train)\n    preds = predict(model, test_encodings)\n    predictions.append(preds)\n\n# \uc608\uce21 \uacb0\uacfc \uc559\uc0c1\ube14\nfinal_preds = torch.stack(predictions).mean(dim=0).round().long()\n    <\/pre>\n
6. \uacb0\uacfc \ubd84\uc11d \ubc0f \ud3c9\uac00<\/h2>\n
\n        \ucd5c\uc885 \uc608\uce21 \uacb0\uacfc\ub97c \ubc14\ud0d5\uc73c\ub85c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uaca0\uc2b5\ub2c8\ub2e4.
\n        \uc815\ud655\ub3c4\ub97c \uacc4\uc0b0\ud558\uace0 confusion matrix\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \ubaa8\ub378\uc758 \uc608\uce21 \uc131\ub2a5\uc744 \ubd84\uc11d\ud574\ubd05\uc2dc\ub2e4.\n    <\/p>\n
6.1 \uc131\ub2a5 \ud3c9\uac00<\/h3>\n
\nfrom sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n\n# \uc131\ub2a5 \ud3c9\uac00\naccuracy = accuracy_score(y_test, final_preds)\nprint(f'Accuracy: {accuracy * 100:.2f}%')\n\n# Confusion matrix \ucd9c\ub825\ncm = confusion_matrix(y_test, final_preds)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm)\ndisp.plot()\n    <\/pre>\n
7. \uacb0\ub860<\/h2>\n
\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 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4.
\n        BERT\ub294 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \uac00\uc9c4 \ubaa8\ub378\uc774\uba70, \uc559\uc0c1\ube14 \uae30\ubc95\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uc608\uce21 \uc131\ub2a5\uc744 \ub354\uc6b1 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc74c\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4.
\n        \ub2e4\uc591\ud55c NLP \ud0dc\uc2a4\ud06c\uc5d0 BERT\ub97c \ud65c\uc6a9\ud558\uc5ec \ub2e4\uc74c \ub2e8\uacc4\ub85c \ub098\uc544\uac00\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\n    <\/p>\n
\ucc38\uace0 \uc790\ub8cc<\/h2>\n