{"id":29714,"date":"2024-10-28T01:56:13","date_gmt":"2024-10-28T01:56:13","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29714"},"modified":"2024-11-26T06:51:37","modified_gmt":"2024-11-26T06:51:37","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%b0%8f-%ea%b2%80%ec%a6%9d-%eb%8d%b0%ec%9d%b4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29714\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \ud559\uc2b5 \ubc0f \uac80\uc99d \ub370\uc774\ud130\uc138\ud2b8 \ubd84\ub9ac"},"content":{"rendered":"

<\/p>\n

\n \ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5(AI) \ubc0f \uba38\uc2e0\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uc758 \uc911\uc694\uc131\uc774 \ub0a0\ub85c \uc99d\uac00\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc2ec\uc5d0\ub294 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face)<\/strong>\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ub2e4\uc591\ud55c NLP \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ud574\uc8fc\uba70, \ud2b9\ud788 \uc0ac\uc804 \ud6c8\ub828\ub41c(pre-trained) \ubaa8\ub378\uc744 \ud1b5\ud574 \ub9e4\uc6b0 \uc27d\uac8c \uc751\uc6a9\ud560 \uc218 \uc788\ub294 \uc7a5\uc810\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5 \ubc0f \uac80\uc99d \ub370\uc774\ud130\uc14b\uc744 \ubd84\ub9ac\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\ub824\ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n

1. \ub370\uc774\ud130\uc14b \uc900\ube44<\/h2>\n

\n \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c NLP \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub808\uc774\ube14\uc774 \uc788\ub294 \ub370\uc774\ud130\uc14b\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 IMDb \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b<\/strong>\uc744 \uc0ac\uc6a9\ud558\uc5ec \uae0d\uc815\uc801\uc778 \ub9ac\ubdf0\uc640 \ubd80\uc815\uc801\uc778 \ub9ac\ubdf0\ub97c \ubd84\ub958\ud558\ub294 \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \uac83\uc785\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\uc14b\uc740 \ub300\uc911\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\uba70, \uc601\ud654 \ub9ac\ubdf0\uc758 \ud14d\uc2a4\ud2b8\uc640 \uadf8\uc5d0 \ub300\ud55c \ub808\uc774\ube14(\uae0d\uc815\/\ubd80\uc815)\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n

1.1 \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc<\/h3>\n
python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"imdb\")\n<\/code><\/pre>\n
\n    \uc704 \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec IMDb \ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. load_dataset<\/code> \ud568\uc218\ub294 Hugging Face \ub370\uc774\ud130\uc14b \ub77c\uc774\ube0c\ub7ec\ub9ac\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud568\uc218\ub85c, \ub2e4\uc591\ud55c \uacf5\uac1c \ub370\uc774\ud130\uc14b\uc744 \uc190\uc27d\uac8c \ub2e4\uc6b4\ub85c\ub4dc\ud560 \uc218 \uc788\ub3c4\ub85d \ud574\uc90d\ub2c8\ub2e4.\n<\/p>\n
1.2 \ub370\uc774\ud130\uc14b \uad6c\uc870 \ud655\uc778<\/h3>\n
python\nprint(dataset)\n<\/code><\/pre>\n
\n    \ub2e4\uc6b4\ub85c\ub4dc\ud55c \ub370\uc774\ud130\uc14b\uc758 \uad6c\uc870\ub97c \ud655\uc778\ud574 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc740 \ud6c8\ub828(train), \ud14c\uc2a4\ud2b8(test) \ubc0f \uac80\uc99d(validation)\uc73c\ub85c \ub098\ub204\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
2. \ub370\uc774\ud130\uc14b \ubd84\ub9ac<\/h2>\n
\n    \uc77c\ubc18\uc801\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd\uc5d0\uc11c\ub294 \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574 \ub370\uc774\ud130\ub97c \uc5ec\ub7ec \ubd80\ubd84\uc73c\ub85c \ub098\ub204\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uc8fc\ub85c \ud6c8\ub828 \ub370\uc774\ud130\uc640 \uac80\uc99d \ub370\uc774\ud130\ub97c \ub098\ub204\ub294\ub370, \ud6c8\ub828 \ub370\uc774\ud130\ub294 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uc0ac\uc6a9\ub418\uace0, \uac80\uc99d \ub370\uc774\ud130\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774\ubc88\uc5d0\ub294 \ud6c8\ub828 \ub370\uc774\ud130\uc5d0\uc11c \uc77c\ubd80\ub97c \ucd94\ucd9c\ud558\uc5ec \uac80\uc99d \ub370\uc774\ud130\ub85c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.\n<\/p>\n
2.1 \ud6c8\ub828 \ub370\uc774\ud130\uc640 \uac80\uc99d \ub370\uc774\ud130 \ubd84\ub9ac<\/h3>\n
python\nfrom sklearn.model_selection import train_test_split\n\ntrain_data = dataset['train']\ntrain_texts = train_data['text']\ntrain_labels = train_data['label']\n\ntrain_texts, val_texts, train_labels, val_labels = train_test_split(\n    train_texts,\n    train_labels,\n    test_size=0.1,  # 10%\ub97c \uac80\uc99d \uc138\ud2b8\ub85c \uc0ac\uc6a9\n    random_state=42\n)\n<\/code><\/pre>\n
\n    \uc704 \ucf54\ub4dc\ub294 train_test_split<\/code> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud6c8\ub828 \ub370\uc774\ud130\ub97c 90%\uc640 10%\ub85c \ub098\ub204\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc11c test_size=0.1<\/code>\ub85c \uc124\uc815\ud558\uc600\uc73c\ubbc0\ub85c, \uc6d0\ub798 \ud6c8\ub828 \ub370\uc774\ud130\uc758 10%\ub97c \uac80\uc99d \ub370\uc774\ud130\ub85c \uc120\ud0dd\ud569\ub2c8\ub2e4. random_state<\/code> \uc778\uc790\ub294 \ubd84\ud560\uc758 \uc77c\uad00\uc131\uc744 \ubcf4\uc7a5\ud569\ub2c8\ub2e4.\n<\/p>\n
2.2 \ubd84\ub9ac\ub41c \ub370\uc774\ud130 \ud655\uc778<\/h3>\n
python\nprint(\"\ud6c8\ub828 \ub370\uc774\ud130 \uac1c\uc218:\", len(train_texts))\nprint(\"\uac80\uc99d \ub370\uc774\ud130 \uac1c\uc218:\", len(val_texts))\n<\/code><\/pre>\n
\n    \uc774\uc81c \ubd84\ub9ac\ub41c \ud6c8\ub828 \ubc0f \uac80\uc99d \ub370\uc774\ud130\uc758 \uac1c\uc218\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc815\ubcf4\ub294 \uc6b0\ub9ac\uc758 \ub370\uc774\ud130\uac00 \uc801\uc808\ud558\uac8c \ubd84\ub9ac\ub418\uc5c8\ub294\uc9c0\ub97c \ud310\ub2e8\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.\n<\/p>\n
3. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378 \uc900\ube44<\/h2>\n
\n    \ub370\uc774\ud130\uc14b\uc744 \ubd84\ub9ac\ud55c \ud6c4\uc5d0\ub294 \uc774\uc81c \ubaa8\ub378\uc744 \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac<\/strong>\uc5d0\uc11c\ub294 \ub2e4\uc591\ud55c \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\ubbc0\ub85c, \uc6b0\ub9ac\uac00 \uc6d0\ud558\ub294 \uc6a9\ub3c4\uc5d0 \ub9de\ub294 \ubaa8\ub378\uc744 \uc120\ud0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
3.1 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378 \uc120\ud0dd<\/h3>\n
python\nfrom transformers import BertTokenizer, BertForSequenceClassification\n\nmodel_name = \"bert-base-uncased\"\ntokenizer = BertTokenizer.from_pretrained(model_name)\nmodel = BertForSequenceClassification.from_pretrained(model_name)\n<\/code><\/pre>\n
\nBertTokenizer<\/code>\uc640 BertForSequenceClassification<\/code>\ub97c \uc0ac\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uc900\ube44\ud569\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \ud14d\uc2a4\ud2b8 \ubd84\ub958 \uc791\uc5c5\uc5d0 \uc801\ud569\ud558\uba70, “bert-base-uncased”\ub77c\ub294 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubc84\uc804\uc744 \uc0ac\uc6a9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
3.2 \ub370\uc774\ud130 \ud1a0\ud070\ud654<\/h3>\n
python\ntrain_encodings = tokenizer(train_texts, truncation=True, padding=True, return_tensors='pt')\nval_encodings = tokenizer(val_texts, truncation=True, padding=True, return_tensors='pt')\n<\/code><\/pre>\n
\ntokenizer<\/code>\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud6c8\ub828 \ubc0f \uac80\uc99d \ub370\uc774\ud130\ub97c \ud1a0\ud070\ud654\ud569\ub2c8\ub2e4. truncation=True<\/code>\ub294 \uae38\uc774\uac00 \uc81c\ud55c\ub41c \uc785\ub825\uc744 \ucc98\ub9ac\ud558\uba70, padding=True<\/code>\ub294 \ubaa8\ub4e0 \uc785\ub825\uc758 \uae38\uc774\ub97c \ub3d9\uc77c\ud558\uac8c \ub9de\ucdb0\uc90d\ub2c8\ub2e4.\n<\/p>\n
4. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\n    \ubaa8\ub378\uc758 \ud559\uc2b5\uc744 \uc704\ud574 PyTorch\uc758 DataLoader<\/strong>\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc30\uce58\ub85c \ub370\uc774\ud130\ub97c \uc870\uc791\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c, optimizer\uc640 loss \ud568\uc218\ub97c \uc124\uc815\ud558\uc5ec \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0ac \uac83\uc785\ub2c8\ub2e4.\n<\/p>\n
4.1 \ub370\uc774\ud130 \ub85c\ub354 \uc900\ube44<\/h3>\n
python\nimport torch\nfrom torch.utils.data import DataLoader, Dataset\n\nclass IMDbDataset(Dataset):\n    def __init__(self, encodings, labels):\n        self.encodings = encodings\n        self.labels = labels\n\n    def __getitem__(self, idx):\n        item = {key: val[idx] for key, val in self.encodings.items()}\n        item['labels'] = torch.tensor(self.labels[idx])\n        return item\n\n    def __len__(self):\n        return len(self.labels)\n\ntrain_dataset = IMDbDataset(train_encodings, train_labels)\nval_dataset = IMDbDataset(val_encodings, val_labels)\n\ntrain_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)\nval_loader = DataLoader(val_dataset, batch_size=16, shuffle=False)\n<\/code><\/pre>\n
\n    \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc14b \ud074\ub798\uc2a4\ub97c \uc815\uc758\ud558\uc5ec Dataset<\/code> \ud074\ub798\uc2a4\uc5d0\uc11c \uc0c1\uc18d\ubc1b\uace0, DataLoader<\/code>\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc30\uce58 \ucc98\ub9ac\ub97c \ud569\ub2c8\ub2e4. \ubc30\uce58 \ud06c\uae30\ub85c 16\uc744 \uc0ac\uc6a9\ud558\uc600\uc2b5\ub2c8\ub2e4.\n<\/p>\n
4.2 \ubaa8\ub378 \ud559\uc2b5 \uc124\uc815<\/h3>\n
python\nfrom transformers import AdamW\n\noptimizer = AdamW(model.parameters(), lr=5e-5)\n\nmodel.train()\nfor epoch in range(3):  # \ucd1d 3 \uc5d0\ud3ed\n    total_loss = 0\n    for batch in train_loader:\n        optimizer.zero_grad()\n        outputs = model(**batch)\n        loss = outputs.loss\n        total_loss += loss.item()\n        loss.backward()\n        optimizer.step()\n    print(f\"Epoch: {epoch + 1}, Loss: {total_loss \/ len(train_loader)}\")\n<\/code><\/pre>\n
\n    AdamW \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \ub9e4 \uc5d0\ud3ed\ub9c8\ub2e4 \ucd1d \uc190\uc2e4\uc744 \uacc4\uc0b0\ud558\uace0 \ucd9c\ub825\ud569\ub2c8\ub2e4. \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 3\ud68c \ud6c8\ub828\ud558\uc600\uc2b5\ub2c8\ub2e4.\n<\/p>\n
5. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
\n    \ubaa8\ub378\uc774 \ud6c8\ub828\ub41c \ud6c4\uc5d0\ub294 \uac80\uc99d \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc131\ub2a5\uc744 \ud3c9\uac00\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc774 \uc5bc\ub9c8\ub098 \uc798 \uc77c\ubc18\ud654\ub418\ub294\uc9c0\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
5.1 \ubaa8\ub378 \ud3c9\uac00 \ud568\uc218 \uc815\uc758<\/h3>\n
python\nfrom sklearn.metrics import accuracy_score\n\ndef evaluate_model(model, val_loader):\n    model.eval()\n    all_labels = []\n    all_preds = []\n\n    with torch.no_grad():\n        for batch in val_loader:\n            outputs = model(**batch)\n            preds = outputs.logits.argmax(dim=-1)\n            all_labels.extend(batch['labels'].tolist())\n            all_preds.extend(preds.tolist())\n\n    accuracy = accuracy_score(all_labels, all_preds)\n    return accuracy\n\naccuracy = evaluate_model(model, val_loader)\nprint(\"\uac80\uc99d \uc815\ud655\ub3c4:\", accuracy)\n<\/code><\/pre>\n
\nevaluate_model<\/code> \ud568\uc218\ub97c \uc815\uc758\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uac80\uc99d \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc815\ud655\ub3c4\ub97c \ucd9c\ub825\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud30c\uc545\ud569\ub2c8\ub2e4.\n<\/p>\n
6. \uacb0\ub860<\/h2>\n
\n    \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 Hugging Face\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec IMDb \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b\uc744 \ub2e4\ub8e8\ub294 \ubc29\ubc95\uc744 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc744 \ubd84\ub9ac\ud558\uace0, \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0, \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \uacfc\uc815\uae4c\uc9c0 \uc804\ubc18\uc801\uc73c\ub85c \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uacfc\uc815\uc744 \ud1b5\ud574 NLP \ubd84\uc57c\uc758 \uae30\ucd08\uc801\uc778 \uc774\ud574\ub97c \ud560 \uc218 \uc788\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\uc220\uc740 \ub2e4\uc591\ud55c \uc5b8\uc5b4 \ubaa8\ub378\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub97c \ud1b5\ud574 \ub354 \ub098\uc740 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
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