{"id":25429,"date":"2024-10-26T09:55:02","date_gmt":"2024-10-26T09:55:02","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=25429"},"modified":"2024-11-26T08:01:47","modified_gmt":"2024-11-26T08:01:47","slug":"%eb%94%a5-%eb%9f%ac%eb%8b%9d%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c-%ec%9e%90%ec%97%b0%ec%96%b4-%ec%b2%98%eb%a6%ac-tfbertforsequenceclassification%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c-%eb%b6%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/25429\/","title":{"rendered":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac: TFBertForSequenceClassification\uc744 \uc774\uc6a9\ud55c \ubd84\ub958"},"content":{"rendered":"

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\uc18c\uac1c<\/h2>\n

\n \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uae30\uacc4\uac00 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ud574\uc11d\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ucd5c\uadfc\uc5d0\ub294 \ub525 \ub7ec\ub2dd \uae30\uc220\uc774 \ubc1c\uc804\ud558\uba74\uc11c \ub9ce\uc740 NLP \uacfc\uc81c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \uacb0\uacfc\ub97c \uc5bb\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 BERT(Bidirectional Encoder Representations from Transformers)\ub77c\ub294 \ubaa8\ub378\uc740 \ub2e4\uc591\ud55c NLP \uc791\uc5c5\uc5d0\uc11c \ub180\ub77c\uc6b4 \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 BERT\uc758 \ubcc0\ud615 \uc911 \ud558\ub098\uc778 TFBertForSequenceClassification<\/code>\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8 \ubd84\ub958 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

BERT\ub780?<\/h2>\n

\n BERT\ub294 Google\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc0ac\uc804 \ud559\uc2b5 \ubaa8\ub378\ub85c, \ubb38\ub9e5\uc744 \uc774\ud574\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. BERT\ub294 \uc591\ubc29\ud5a5Transformer \uc778\ucf54\ub354 \uad6c\uc870\ub97c \uae30\ubc18\uc73c\ub85c \ud558\uc5ec, \uc785\ub825\ub41c \ubb38\uc7a5\uc744 \uc88c\uc6b0\ub85c \ub3d9\uc2dc\uc5d0 \uace0\ub824\ud569\ub2c8\ub2e4. \uae30\uc874\uc758 \ub2e8\ubc29\ud5a5 \ubaa8\ub378\uacfc \ub2ec\ub9ac BERT\uc758 \uc591\ubc29\ud5a5\uc131 \ub355\ubd84\uc5d0 \ubb38\ub9e5\uc744 \ub354 \uc798 \uc774\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

TFBertForSequenceClassification\ub780?<\/h2>\n

\nTFBertForSequenceClassification<\/code>\ub294 BERT \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc8fc\uc5b4\uc9c4 \uc785\ub825 \ud14d\uc2a4\ud2b8\ub97c \ud2b9\uc815 \uce74\ud14c\uace0\ub9ac\ub098 \ud074\ub798\uc2a4\ub85c \ubd84\ub958\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. TensorFlow\uc758 Hugging Face Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\uc5d0\uc11c \uc81c\uacf5\ud558\uba70, \uc190\uc27d\uac8c NLP \ud0dc\uc2a4\ud06c\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

\ubaa8\ub378 \uc124\uce58\uc640 \ud658\uacbd \uc124\uc815<\/h2>\n

\n TFBertForSequenceClassification\uc744 \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 TensorFlow\uc640 Hugging Face Transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n <\/p>\n

pip install tensorflow transformers<\/code><\/pre>\n
\ub370\uc774\ud130\uc14b \uc900\ube44<\/h2>\n
\n        \uc6b0\ub9ac\ub294 IMDB \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uae0d\uc815 \ub610\ub294 \ubd80\uc815 \ub9ac\ubdf0\ub97c \ubd84\ub958\ud560 \uac83\uc785\ub2c8\ub2e4. TensorFlow Datasets\ub97c \uc774\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\nimport tensorflow_datasets as tfds\n\ndataset, info = tfds.load('imdb', with_info=True, as_supervised=True)\ntrain_data, test_data = dataset['train'], dataset['test']\n<\/code><\/pre>\n
\ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\n        \ub85c\ub4dc\ud55c \ub370\uc774\ud130\uc14b\uc744 \ubaa8\ub378\uc5d0 \ub9de\uac8c \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\ub294 \ud14d\uc2a4\ud2b8 \ud1a0\ud070\ud654, \uc2dc\ud000\uc2a4 \uae38\uc774 \ud1b5\uc77c, \ub808\uc774\ube14 \uc778\ucf54\ub529 \ub4f1\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4. BERT \ubaa8\ub378\uc5d0 \uc801\ud569\ud55c \uc785\ub825 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud558\uae30 \uc704\ud574 Hugging Face\uc758 Tokenizer<\/code>\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n    <\/p>\n
\nfrom transformers import BertTokenizer\n\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n\ndef encode_texts(texts):\n    return tokenizer(texts.numpy().tolist(), padding='max_length', truncation=True, max_length=512, return_tensors='tf')\n\n# \ub370\uc774\ud130\uc14b\uc744 \uc804\ucc98\ub9ac\ud558\ub294 \uc608\uc2dc\ntrain_data = train_data.map(lambda x, y: (encode_texts(x), y))\n<\/code><\/pre>\n
\ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\n        BERT \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \ud558\ub294 TFBertForSequenceClassification<\/code> \ubaa8\ub378\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4. \ubbf8\ub9ac \ud559\uc2b5\ub41c BERT \ubaa8\ub378\uc744 \ud30c\uc778\ud29c\ub2dd\ud574\uc11c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n    <\/p>\n
\nfrom transformers import TFBertForSequenceClassification\n\nmodel = TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n<\/code><\/pre>\n
\ubaa8\ub378 \ucef4\ud30c\uc77c \ubc0f \ud559\uc2b5<\/h2>\n
\n        \ubaa8\ub378\uc744 \ucef4\ud30c\uc77c\ud558\uace0 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574 \uc635\ud2f0\ub9c8\uc774\uc800\uc640 \uc190\uc2e4 \ud568\uc218\ub97c \uc124\uc815\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c Adam \uc635\ud2f0\ub9c8\uc774\uc800\uc640 Sparse Categorical Crossentropy \uc190\uc2e4 \ud568\uc218\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n    <\/p>\n
\noptimizer = tf.keras.optimizers.Adam(learning_rate=2e-5)\n\nmodel.compile(optimizer=optimizer, loss=model.compute_loss, metrics=['accuracy'])\nmodel.fit(train_dataset.shuffle(1000).batch(16), epochs=3, validation_data=test_dataset.batch(16))\n<\/code><\/pre>\n
\ubaa8\ub378 \ud3c9\uac00<\/h2>\n
\n        \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc5d0 \ub300\ud55c \uc9c0\ud45c\ub85c \uc815\ud655\ub3c4\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n    <\/p>\n
\nloss, accuracy = model.evaluate(test_dataset.batch(16))\nprint(f'Accuracy: {accuracy}')\n<\/code><\/pre>\n
\uacb0\ub860<\/h2>\n
\n        \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 TFBertForSequenceClassification\uc744 \uc774\uc6a9\ud558\uc5ec \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0\uc11c \ud14d\uc2a4\ud2b8 \ubd84\ub958\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. BERT \ubaa8\ub378\uc740 \ub192\uc740 \uc131\ub2a5\uc744 \uc790\ub791\ud558\uba70, \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uacfc\uc81c\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 \ub354 \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc14b\uacfc \ud30c\uc778\ud29c\ub2dd \uae30\ubc95\uc744 \ud1b5\ud574 \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc744 \ud0d0\uad6c\ud558\uae38 \ubc14\ub78d\ub2c8\ub2e4. \ub525 \ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uacb0\ud569\uc740 \uc55e\uc73c\ub85c\ub3c4 \ud070 \uac00\ub2a5\uc131\uc744 \uc9c0\ub2d9\ub2c8\ub2e4.\n    <\/p>\n
\ucc38\uace0 \uc790\ub8cc<\/h2>\n