\uc138\uadf8\uba3c\ud2b8 ID \uc0dd\uc131: \uc785\ub825\uc774 \uc5ec\ub7ec \uac1c\uc758 \ubb38\uc7a5\uc73c\ub85c \uad6c\uc131\ub41c \uacbd\uc6b0, \uac01 \ubb38\uc7a5\uc774 \uc5b4\ub5a4 \uc138\uadf8\uba3c\ud2b8\uc5d0
\n \uc18d\ud558\ub294\uc9c0\ub97c \ub098\ud0c0\ub0b4\ub294 ID\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n3.2. BERT \ud1a0\ud070\ud654 \uc608\uc81c<\/h3>\n
\uc544\ub798\uc758 Python \ucf54\ub4dc\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec BERT \uc785\ub825 \uc2dc\ud000\uc2a4\ub97c
\n \uc804\ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4:<\/p>\n
\nimport torch\nfrom transformers import BertTokenizer\n\n# BERT tokenizer \ucd08\uae30\ud654\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n\n# \uc608\uc81c \ubb38\uc7a5\ntext = \"\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\uc774\ub2e4.\"\n\n# \ud14d\uc2a4\ud2b8 \ud1a0\ud070\ud654\ninputs = tokenizer(text, return_tensors=\"pt\")\n\n# \uacb0\uacfc \ucd9c\ub825\nprint(\"Input IDs:\", inputs['input_ids'])\nprint(\"Attention Mask:\", inputs['attention_mask'])\n <\/code><\/pre>\n4. [CLS] \ud1a0\ud070\uc744 \uc0ac\uc6a9\ud55c \ub3c4\ud050\uba3c\ud2b8 \ubca1\ud130 \ud45c\ud604<\/h2>\n
BERT\uc758 \ucd9c\ub825\uc5d0\uc11c [CLS] \ud1a0\ud070\uc758 \ubca1\ud130 \ud45c\ud604\uc740 \uc785\ub825 \ubb38\uc11c\uc758 \uace0\uc218\uc900 \uc758\ubbf8\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.
\n \uc774 \ubca1\ud130\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \ubb38\uc11c \ubd84\ub958, \uac10\uc815 \ubd84\uc11d \ub4f1\uc758 \uc791\uc5c5\uc5d0\uc11c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. [CLS] \ud1a0\ud070\uc758
\n \ubca1\ud130\ub97c \ud1b5\ud574 \uc804\uccb4 \ubb38\uc11c\uc758 \uc774\ud574\ub97c \ubc14\ud0d5\uc73c\ub85c \uc608\uce21\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1. BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud55c \uc608\uc81c<\/h3>\n
\ub2e4\uc74c\uc740 BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec [CLS] \ud1a0\ud070\uc758 \ubca1\ud130 \ud45c\ud604\uc744 \ucd94\ucd9c\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4:<\/p>\n
\nfrom transformers import BertModel\n\n# BERT \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = BertModel.from_pretrained('bert-base-uncased')\n\n# \ubaa8\ub378\uc5d0 \uc785\ub825 \ub370\uc774\ud130 \uc804\ub2ec\nwith torch.no_grad():\n outputs = model(**inputs)\n\n# [CLS] \ud1a0\ud070\uc758 \ubca1\ud130 \ucd94\ucd9c\ncls_vector = outputs.last_hidden_state[0][0]\n\n# \uacb0\uacfc \ucd9c\ub825\nprint(\"CLS Vector:\", cls_vector)\n <\/code><\/pre>\n5. \uc804\uccb4 \ucf54\ub4dc \uc608\uc81c<\/h2>\n
\uc804\uccb4 \ucf54\ub4dc \uc608\uc81c\ub97c \ud1b5\ud574 BERT\uc758 \uc804\ucc98\ub9ac\uc640 [CLS] \ud1a0\ud070\uc758 \ubca1\ud130 \ud45c\ud604\uc744 \ucd94\ucd9c\ud558\ub294 \uacfc\uc815\uc744
\n \ud1b5\ud569\uc801\uc73c\ub85c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n
\nimport torch\nfrom transformers import BertTokenizer, BertModel\n\n# BERT tokenizer\uc640 \ubaa8\ub378 \ucd08\uae30\ud654\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertModel.from_pretrained('bert-base-uncased')\n\n# \uc608\uc81c \ubb38\uc7a5\ntext = \"\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\uc774\ub2e4.\"\n\n# \ud14d\uc2a4\ud2b8 \ud1a0\ud070\ud654\ninputs = tokenizer(text, return_tensors=\"pt\")\n\n# \ubaa8\ub378\uc5d0 \uc804\ub2ec\ud558\uc5ec \uc608\uce21 \uc218\ud589\nwith torch.no_grad():\n outputs = model(**inputs)\n\n# [CLS] \ud1a0\ud070\uc758 \ubca1\ud130 \ucd94\ucd9c\ncls_vector = outputs.last_hidden_state[0][0]\n\nprint(\"Input IDs:\", inputs['input_ids'])\nprint(\"Attention Mask:\", inputs['attention_mask'])\nprint(\"CLS Vector:\", cls_vector)\n <\/code><\/pre>\n6. \ub9c8\ubb34\ub9ac<\/h2>\n
\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec BERT\uc758 \uc804\ucc98\ub9ac \uacfc\uc815\uacfc [CLS] \ud1a0\ud070\uc758
\n \ubca1\ud130 \ud45c\ud604\uc744 \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. BERT\ub97c \ud65c\uc6a9\ud558\uba74 \ubb38\uc11c\uc758 \uace0\uc218\uc900 \uc758\ubbf8\ub97c
\n \ud6a8\uacfc\uc801\uc73c\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub97c \ud1b5\ud574 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc791\uc5c5\uc5d0\uc11c \uacbd\uc7c1\ub825 \uc788\ub294 \uc131\ub2a5\uc744
\n \ubc1c\ud718\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c BERT\ub97c \ud65c\uc6a9\ud55c \ub354 \ub9ce\uc740 \uc2e4\uc2b5\uacfc \uc751\uc6a9\uc744 \ud1b5\ud574 \uc2e4\ub825\uc744 \ud0a4\uc6cc\ubcf4\uc2dc\uae30\ub97c
\n \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n