\ud604\ub300 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubd84\uc57c\uc5d0\uc11c BERT\ub294 \ud601\uc2e0\uc801\uc778 \ubaa8\ub378\ub85c \uc790\ub9ac\uc7a1\uc558\uc2b5\ub2c8\ub2e4. BERT\ub294 Bidirectional Encoder Representations from Transformers\uc758 \uc57d\uc790\ub85c, \uc591\ubc29\ud5a5 \ucee8\ud14d\uc2a4\ud2b8\ub97c \uc774\ud574\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ub2a5\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub525\ub7ec\ub2dd \uae30\ubc18\uc758 NLP \ubaa8\ub378\uc744 \uac1c\ubc1c\ud568\uc5d0 \uc788\uc5b4, BERT\uc758 \ud65c\uc6a9\uc740 \ud544\uc218\uc801\uc774\uba70, \ud2b9\ud788 \uc559\uc0c1\ube14 \ud559\uc2b5\uacfc \ub370\uc774\ud130 \uc99d\uac15 \uae30\ubc95\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud55c\uce35 \ub04c\uc5b4\uc62c\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 Hugging Face\uc758 Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uc559\uc0c1\ube14 \ud559\uc2b5 \ubc29\ubc95\uacfc \ub370\uc774\ud130 \uc99d\uac15 \uae30\ubc95\uc73c\ub85c \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
BERT\ub294 \ub2e8\uc5b4\uc758 \ub9e5\ub77d\uc744 \uc774\ud574\ud558\uae30 \uc704\ud574 Transformer\ub77c\ub294 \uc544\ud0a4\ud14d\ucc98\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. BERT\uc758 \uac00\uc7a5 \ud070 \ud2b9\uc9d5\uc740 \ud1a0\ud070 \uac04\uc758 \uad00\uacc4\ub97c \ud30c\uc545\ud558\ub294 \ub370 \uc788\uc5b4 \uc591\ubc29\ud5a5\uc131\uc744 \uac16\uace0 \uc788\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 RNN \uae30\ubc18 \ubaa8\ub378\uc740 \uc21c\ucc28\uc801\uc73c\ub85c \ub2e8\uc5b4\ub97c \ucc98\ub9ac\ud558\ub294 \ubc18\uba74, BERT\ub294 \ubb38\uc7a5 \ub0b4 \ubaa8\ub4e0 \ub2e8\uc5b4\uc758 \ub9e5\ub77d\uc744 \ub3d9\uc2dc\uc5d0 \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
Hugging Face Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ub2e4\uc591\ud55c Transformer \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ub9cc\ub4e4\uc5b4\uc9c4 \ud30c\uc774\uc36c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. BERT\ubfd0\ub9cc \uc544\ub2c8\ub77c, GPT, T5 \ub4f1 \ub2e4\ub978 \ucd5c\uc2e0 \ubaa8\ub378\ub3c4 \uc9c0\uc6d0\ud569\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \uc6b0\ub9ac\ub294 \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \ub85c\ub4dc\ud558\uace0, \uc6b0\ub9ac\uc758 \ub370\uc774\ud130\uc5d0 \ub9de\uac8c fine-tuning\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub370\uc774\ud130 \uc99d\uac15(Data Augmentation)\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc131\ub2a5\uc744 \ub192\uc774\ub294 \ub370 \uc788\uc5b4 \ub9e4\uc6b0 \uc911\uc694\ud55c \uae30\uc220\uc785\ub2c8\ub2e4. \ud2b9\ud788 NLP\uc5d0\uc11c\ub294 \ub370\uc774\ud130\uac00 \ubd80\uc871\ud55c \uacbd\uc6b0, \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uac70\ub098 \uae30\uc874 \ub370\uc774\ud130\ub97c \ubcc0\ud615\ud568\uc73c\ub85c\uc368 \ubaa8\ub378\uc758 \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc99d\uac15\uc758 \ubc29\ubc95\uc5d0\ub294 \ub2e4\uc591\ud55c \uae30\ubc95\uc774 \uc788\uc73c\uba70, \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud2b9\ud788 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc99d\uac15\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uc559\uc0c1\ube14 \ud559\uc2b5(Ensemble Learning)\uc740 \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \uc870\ud569\ud558\uc5ec \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc5ec\ub7ec \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uacb0\ud569\ud558\uc5ec \ucd5c\uc885 \uacb0\uacfc\ub97c \ub3c4\ucd9c\ud569\ub2c8\ub2e4. BERT \uc559\uc0c1\ube14 \ud559\uc2b5\uc5d0\uc11c\ub294 \uc11c\ub85c \ub2e4\ub978 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub85c \ud559\uc2b5\ub41c \uc5ec\ub7ec BERT \ubaa8\ub378\uc758 \ucd9c\ub825\uc744 \uacb0\ud569\ud558\uc5ec \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
!pip install transformers torch<\/code><\/pre>\n\uc704 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 Hugging Face Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 PyTorch\ub97c \uc124\uce58\ud569\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub4e4\uc740 BERT \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0, \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \ub3c4\uc640\uc90d\ub2c8\ub2e4.<\/p>\n
7. \ub370\uc774\ud130 \uc900\ube44 \ubc0f \uc804\ucc98\ub9ac<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uac04\ub2e8\ud55c \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubb38\uc81c\ub97c \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\ub294 \ub2e4\uc74c\uacfc \uac19\ub2e4\uace0 \uac00\uc815\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ndata = {\n 'text': ['\uc774 \uc601\ud654\ub294 \uc815\ub9d0\ub85c \uc88b\uc2b5\ub2c8\ub2e4.', '\ucd5c\uc545\uc758 \uc601\ud654\uc600\uc2b5\ub2c8\ub2e4.', '\uc815\ub9d0 \uc7ac\ubbf8\uc788\ub294 \uc601\ud654\uc785\ub2c8\ub2e4.', '\uc774 \uc601\ud654\ub294 \uc9c0\ub8e8\ud569\ub2c8\ub2e4.'],\n 'label': [1, 0, 1, 0] # 1: \uae0d\uc815, 0: \ubd80\uc815\n}\n <\/code><\/pre>\n8. \ub370\uc774\ud130 \uc99d\uac15 \uae30\ubc95<\/h2>\n
\uc5ec\ub7ec \uac00\uc9c0 \ub370\uc774\ud130 \uc99d\uac15 \uae30\ubc95 \uc911\uc5d0\uc11c \uc6b0\ub9ac\uac00 \uc0ac\uc6a9\ud560 \uae30\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uac83\ub4e4\uc785\ub2c8\ub2e4:<\/p>\n
\n- \uc8fc\uc694 \ub2e8\uc5b4 \uce58\ud658:<\/strong> \ud2b9\uc815 \ub2e8\uc5b4\ub97c \ub3d9\uc758\uc5b4\ub85c \uce58\ud658\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ubb38\uc7a5\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n
- \ubb34\uc791\uc704 \uc0bd\uc785:<\/strong> \ubb34\uc791\uc704\ub85c \uc120\ud0dd\ub41c \ub2e8\uc5b4\ub97c \uae30\uc874 \ubb38\uc7a5\uc5d0 \uc0bd\uc785\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ubb38\uc7a5\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n
- \ubb34\uc791\uc704 \uc0ad\uc81c:<\/strong> \ud2b9\uc815 \ub2e8\uc5b4\ub97c \ubb34\uc791\uc704\ub85c \uc0ad\uc81c\ud558\uc5ec \ubb38\uc7a5\uc744 \ubcc0\ud615\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
8.1 \uc8fc\uc694 \ub2e8\uc5b4 \uce58\ud658 \uc608\uc81c<\/h3>\n\nimport random\nfrom nltk.corpus import wordnet\n\ndef synonym_replacement(text):\n words = text.split()\n new_words = words.copy()\n random_word_idx = random.randint(0, len(words)-1)\n word = words[random_word_idx]\n \n synonyms = wordnet.synsets(word)\n if synonyms:\n synonym = synonyms[0].lemmas()[0].name()\n new_words[random_word_idx] = synonym.replace('_', ' ')\n \n return ' '.join(new_words)\n <\/code><\/pre>\n8.2 \ubb34\uc791\uc704 \uc0bd\uc785 \uc608\uc81c<\/h3>\n\ndef random_insertion(text):\n words = text.split()\n new_words = words.copy()\n random_word = random.choice(words)\n new_words.insert(random.randint(0, len(new_words)-1), random_word)\n return ' '.join(new_words)\n <\/code><\/pre>\n8.3 \ubb34\uc791\uc704 \uc0ad\uc81c \uc608\uc81c<\/h3>\n\ndef random_deletion(text, p=0.5):\n words = text.split()\n if len(words) == 1: # only one word, it's better not to drop it\n return text\n \n remaining = list(filter(lambda x: random.random() > p, words))\n return ' '.join(remaining) if len(remaining) > 0 else ' '.join(random.sample(words, 1))\n <\/code><\/pre>\n9. \ub370\uc774\ud130 \uc99d\uac15 \uc801\uc6a9\ud558\uae30<\/h2>\n
\uc774\uc81c \uc218\uc9d1\ub41c \ub370\uc774\ud130\uc5d0 \ub370\uc774\ud130 \uc99d\uac15\uc744 \uc801\uc6a9\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\naugmented_texts = []\naugmented_labels = []\n\nfor index, row in enumerate(data['text']):\n augmented_texts.append(row) # \uc6d0\ubcf8 \ub370\uc774\ud130 \ucd94\uac00\n augmented_labels.append(data['label'][index]) # \ud574\ub2f9 label \ucd94\uac00\n \n # \ub370\uc774\ud130 \uc99d\uac15\n augmented_texts.append(synonym_replacement(row))\n augmented_labels.append(data['label'][index])\n \n augmented_texts.append(random_insertion(row))\n augmented_labels.append(data['label'][index])\n \n augmented_texts.append(random_deletion(row))\n augmented_labels.append(data['label'][index])\n\nprint(\"\uc99d\uac15\ub41c \ub370\uc774\ud130 \uc218:\", len(augmented_texts))\n <\/code><\/pre>\n10. BERT \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\ub370\uc774\ud130 \uc99d\uac15\uc774 \uc644\ub8cc\ub418\uba74, \uc774\uc81c BERT \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ucf1c\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \ud1b5\ud574 BERT \ubaa8\ub378\uc744 \ubd88\ub7ec\uc624\uace0 \ud559\uc2b5\uc744 \uc2dc\uc791\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom transformers import BertTokenizer, BertForSequenceClassification\nfrom transformers import Trainer, TrainingArguments\nimport torch\n\n# \ud1a0\ud06c\ub098\uc774\uc800\uc640 \ubaa8\ub378 \ub85c\ub4dc\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n\n# \ub370\uc774\ud130 \ud1a0\ud06c\ub098\uc774\uc988\ntrain_encodings = tokenizer(augmented_texts, truncation=True, padding=True)\ntrain_labels = augmented_labels\n\n# \ub370\uc774\ud130\uc14b \uc815\uc758\nclass AugmentedDataset(torch.utils.data.Dataset):\n def __init__(self, encodings, labels):\n self.encodings = encodings\n self.labels = labels\n \n def __getitem__(self, idx):\n item = {k: torch.tensor(v[idx]) for k, v 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 = AugmentedDataset(train_encodings, train_labels)\n\n# \ud559\uc2b5 \uc778\uc790 \uc124\uc815\ntraining_args = TrainingArguments(\n per_device_train_batch_size=4,\n num_train_epochs=3,\n logging_dir='.\/logs',\n logging_steps=10,\n)\n\n# Trainer \ucd08\uae30\ud654 \ubc0f \ud559\uc2b5\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n)\n\ntrainer.train()\n <\/code><\/pre>\n11. \uc559\uc0c1\ube14 \ud559\uc2b5<\/h2>\n
\uc774\uc81c \uc5ec\ub7ec \ub2e4\ub978 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub85c BERT \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0 \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uc801\uc6a9\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ndef create_and_train_model(learning_rate, epochs):\n model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n training_args = TrainingArguments(\n per_device_train_batch_size=4,\n num_train_epochs=epochs,\n learning_rate=learning_rate,\n logging_dir='.\/logs',\n logging_steps=10,\n )\n\n trainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n )\n \n trainer.train()\n return model\n\nmodels = []\nfor lr in [5e-5, 2e-5]:\n for epoch in [3, 4]:\n models.append(create_and_train_model(lr, epoch))\n <\/code><\/pre>\n12. \uc559\uc0c1\ube14 \uc608\uce21<\/h2>\n
\ucd5c\uc885 \ubaa8\ub378\uc758 \uc608\uce21\uc740 \uc77c\ubc18\uc801\uc73c\ub85c \uc5ec\ub7ec \ubaa8\ub378\uc758 \uc608\uce21\uac12\uc744 \ud3c9\uade0\ud558\uc5ec \uc0dd\uc131\ud569\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud574 \uc559\uc0c1\ube14 \uc608\uce21\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ndef ensemble_predict(models, texts):\n predictions = []\n \n for model in models:\n model_predictions = trainer.predict(texts)\n predictions.append(model_predictions.predictions)\n \n predictions = sum(predictions) \/ len(predictions)\n return predictions\n\nensemble_results = ensemble_predict(models, test_data) # test_data\ub294 \ubcc4\ub3c4\uc758 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\n <\/code><\/pre>\n13. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 BERT \ubaa8\ub378\uc744 \uc559\uc0c1\ube14 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uacfc \ub370\uc774\ud130 \uc99d\uac15 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. BERT\ub294 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \uc81c\uacf5\ud558\uc9c0\ub9cc, \ub370\uc774\ud130\uac00 \ubd80\uc871\ud558\uac70\ub098 \ud3b8\ud5a5\ub41c \uacbd\uc6b0 \uc131\ub2a5\uc774 \uc800\ud558\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc99d\uac15 \ubc0f \uc559\uc0c1\ube14 \uae30\ubc95\uc740 \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \uc720\uc6a9\ud55c \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n
14. \ucc38\uace0\uc790\ub8cc<\/h2>\n\n- Hugging Face Transformers Documentation: https:\/\/transformers.huggingface.co\/<\/a><\/li>\n
- Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding<\/li>\n
- Natural Language Processing with Transformers (Book)<\/li>\n<\/ul>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"