{"id":29544,"date":"2024-10-28T01:55:22","date_gmt":"2024-10-28T01:55:22","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29544"},"modified":"2024-11-26T06:52:20","modified_gmt":"2024-11-26T06:52:20","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-bert-%ec%95%99%ec%83%81%eb%b8%94-%ed%81%b4%eb%9e%98%ec%8a%a4-%ec%a0%95","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29544\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, BERT \uc559\uc0c1\ube14 \ud074\ub798\uc2a4 \uc815\uc758"},"content":{"rendered":"

<\/p>\n

\n \ud604\ub300\uc758 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ucd5c\uadfc\uc5d0\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574, \ub2e4\uc591\ud55c \ubaa8\ub378\ub4e4\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 BERT(Bidirectional Encoder Representations from Transformers) \ubaa8\ub378\uc744 \ud65c\uc6a9\ud55c \uc559\uc0c1\ube14 \ud074\ub798\uc2a4 \uc815\uc758\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uace0, \uc2e4\uc2b5\uc744 \ud1b5\ud574 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

1. BERT \ubaa8\ub378 \uc18c\uac1c<\/h2>\n

\n BERT\ub294 Google\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc0ac\uc804 \ud6c8\ub828\ub41c \uc5b8\uc5b4 \ubaa8\ub378\ub85c, Transformer \uc544\ud0a4\ud14d\ucc98\ub97c \uae30\ubc18\uc73c\ub85c \ud569\ub2c8\ub2e4. BERT\uc758 \uc8fc\uc694 \ud2b9\uc9d5\uc740 Bidirectional \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubb38\ub9e5\uc744 \uc591 \ubc29\ud5a5\uc5d0\uc11c \uace0\ub824\ud55c\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4. \uc774\ub294 \ud14d\uc2a4\ud2b8\uc758 \uc758\ubbf8\ub97c \ubcf4\ub2e4 \uc815\uad50\ud558\uac8c \ud30c\uc545\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uc2b5\ub2c8\ub2e4.\n <\/p>\n

\n BERT\ub294 \ub2e4\uc591\ud55c \ub2e4\uc6b4\uc2a4\ud2b8\ub9bc \ud0dc\uc2a4\ud06c(\uc608. \uc9c8\ubb38 \ub2f5\ubcc0, \uac10\uc815 \ubd84\uc11d \ub4f1)\uc5d0 Fine-tuning\ud558\uc5ec \uc0ac\uc6a9\ub420 \uc218 \uc788\uc73c\uba70, Hugging Face\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \uc27d\uac8c \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

2. \uc559\uc0c1\ube14 \ud559\uc2b5(Ensemble Learning)<\/h2>\n

\n \uc559\uc0c1\ube14 \ud559\uc2b5\uc740 \uc5ec\ub7ec \ubaa8\ub378\uc744 \uacb0\ud569\ud558\uc5ec \ubcf4\ub2e4 \uc815\ud655\ud55c \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub2e8\uc77c \ubaa8\ub378\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uc624\ucc28\ub97c \uc904\uc774\uace0, \ubaa8\ub378\uc774 \uc81c\uacf5\ud558\ub294 \uc608\uce21\uc758 \ub2e4\uc591\uc131\uc744 \ud655\ubcf4\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

\n \uc77c\ubc18\uc801\uc73c\ub85c \uc559\uc0c1\ube14\uc758 \ubc29\ubc95\uc5d0\ub294 \ubcf4\ud305, \ubc30\uae45, \ubd80\uc2a4\ud305 \ub4f1\uc774 \uc788\uc73c\uba70, \uac01 \ubc29\ubc95\ub9c8\ub2e4 \uc0ac\uc6a9\ub418\ub294 \uc54c\uace0\ub9ac\uc998\uacfc \ubc29\ubc95\uc774 \ub2e4\ub985\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uc5ec\ub7ec BERT \ubaa8\ub378\uc744 \uacb0\ud569\ud558\uc5ec \ub354\uc6b1 \uac15\ub825\ud55c \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

3. \ud658\uacbd \uc124\uc815<\/h2>\n

\n \uc559\uc0c1\ube14 \ud074\ub798\uc2a4\ub97c \uad6c\ud604\ud558\uae30 \uc704\ud574, \uba3c\uc800 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc740 \ud328\ud0a4\uc9c0\ub97c \uc900\ube44\ud574\uc8fc\uc138\uc694:\n <\/p>\n

pip install transformers torch numpy<\/code><\/pre>\n
4. BERT \uc559\uc0c1\ube14 \ud074\ub798\uc2a4 \uc815\uc758<\/h2>\n
\n        \uc774\uc81c \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uc704\ud55c BERT \ud074\ub798\uc2a4\uc758 \uae30\ubcf8 \uad6c\uc870\ub97c \uc815\uc758\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \uc5ec\ub7ec \uac1c\uc758 BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uace0 \uc774\ub4e4\uc758 \ucd9c\ub825\uc744 \uacb0\ud569\ud558\uc5ec \ucd5c\uc885 \uacb0\uacfc\ub97c \ub3c4\ucd9c\ud560 \uac83\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c Hugging Face\uc758 transformers<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4.\n    <\/p>\n
4.1 BERT \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30<\/h3>\n
\n        \uba3c\uc800 BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800\ub97c \ubd88\ub7ec\uc624\ub294 \uba54\uc11c\ub4dc\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.\n    <\/p>\n
\nimport torch\nfrom transformers import BertTokenizer, BertForSequenceClassification\n\nclass BertEnsemble:\n    def __init__(self, model_paths):\n        self.models = []\n        self.tokenizers = []\n        \n        for model_path in model_paths:\n            tokenizer = BertTokenizer.from_pretrained(model_path)\n            model = BertForSequenceClassification.from_pretrained(model_path)\n            self.tokenizers.append(tokenizer)\n            self.models.append(model)\n\n    def predict(self, text):\n        inputs = [tokenizer(text, return_tensors='pt') for tokenizer in self.tokenizers]\n        outputs = [model(**input).logits for model, input in zip(self.models, inputs)]\n        return outputs\n    <\/code><\/pre>\n
4.2 \uc608\uce21 \uba54\uc11c\ub4dc \uad6c\ud604<\/h3>\n
\n        \uac01 \ubaa8\ub378\uc758 \uc608\uce21\uac12\uc744 \ud3c9\uade0\ub0b4\uc5b4 \ucd5c\uc885 \uacb0\uacfc\ub97c \ub3c4\ucd9c\ud558\ub294 \uba54\uc11c\ub4dc\ub97c \uad6c\ud604\ud569\ub2c8\ub2e4.\n    <\/p>\n
\n    def ensemble_predict(self, text):\n        outputs = self.predict(text)\n        # \uc608\uce21 \uacb0\uacfc \ud3c9\uade0 \uacc4\uc0b0\n        summed_outputs = torch.mean(torch.stack(outputs), dim=0)\n        return summed_outputs\n    <\/code><\/pre>\n
5. \ubaa8\ub378 \ud559\uc2b5 \ubc0f \ud3c9\uac00<\/h2>\n
\n        \uc559\uc0c1\ube14 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uace0 \ud3c9\uac00\ud558\ub294 \uacfc\uc815\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud558\uace0, \uac01 \ubaa8\ub378\uc744 Fine-tune \ud55c \ub4a4, \uc559\uc0c1\ube14 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.\n    <\/p>\n
\ndef fine_tune_model(model, train_dataloader, num_epochs=3):\n    model.train()\n    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)\n    \n    for epoch in range(num_epochs):\n        for batch in train_dataloader:\n            optimizer.zero_grad()\n            outputs = model(batch['input_ids'], attention_mask=batch['attention_mask'], labels=batch['labels'])\n            loss = outputs.loss\n            loss.backward()\n            optimizer.step()\n            print(f'Epoch {epoch+1}, Loss: {loss.item()}')\n    <\/code><\/pre>\n
6. \uacb0\ub860<\/h2>\n
\n        \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uc559\uc0c1\ube14\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. BERT\uc758 \ud6a8\uc728\uc131\uacfc \uc559\uc0c1\ube14 \ud559\uc2b5\uc758 \uc870\ud569\uc744 \ud1b5\ud574 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ud0dc\uc2a4\ud06c\uc5d0\uc11c \ub354 \ud5a5\uc0c1\ub41c \uc131\ub2a5\uc744 \uae30\ub300\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc2e4\uc81c \ub370\uc774\ud130\uc14b\uc5d0 \ub9de\ucdb0 Fine-tuning\uc744 \uc218\ud589\ud558\uace0, \uc774 \uacfc\uc815\uc744 \ud1b5\ud574 \uc5bb\uc5b4\uc9c4 \uc778\uc0ac\uc774\ud2b8\ub97c \ubc14\ud0d5\uc73c\ub85c \ub098\ub9cc\uc758 \ubaa8\ub378\uc744 \uad6c\ucd95\ud574 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\n    <\/p>\n
7. \ucc38\uace0 \ubb38\ud5cc<\/h2>\n