E17/E17_BERT_KorQuAD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "grateful-struggle",
   "metadata": {},
   "source": [
    "# Exploration 17\n",
    "\n",
    "\n",
    "## KorQuAD Task를 BERT 모델을 활용하여 학습"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proper-correction",
   "metadata": {},
   "source": [
    "### Table of contents\n",
    "\n",
    "1. [KorQuAD 데이터 불러오기]()   \n",
    "2. [KorQuAD 데이터 전처리]()   \n",
    "3. [BERT 모델 학습]()   \n",
    "  \n",
    "  3.1 Pretraining 없이 학습 진행   \n",
    "  3.2 Pretraining Model 활용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "attractive-shelf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#한국어 폰트 설치 확인\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "fontpath = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf'\n",
    "font = fm.FontProperties(fname=fontpath, size=9)\n",
    "plt.rc('font', family='NanumBarunGothic') \n",
    "mpl.font_manager._rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moved-economics",
   "metadata": {},
   "source": [
    "## 1. KorQuAD 데이터 불러오기\n",
    "- KorQuAD(The Korean Question Answering Dataset, 한국어 질의응답 데이터셋)\n",
    "- 한국어 MRC를 위해 LG CNS에서 구축한 대규모 질의응답 데이터셋.\n",
    "- 한국어 MRC 모델을 학습하고 그 성능을 평가하는 일에 사용되고 있음."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "animated-saturn",
   "metadata": {},
   "outputs": [],
   "source": [
    "#필요한 모듈\n",
    "\n",
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "import tensorflow_addons as tfa\n",
    "\n",
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import random\n",
    "import collections\n",
    "import json\n",
    "from datetime import datetime\n",
    "\n",
    "import sentencepiece as spm\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "random_seed = 1234\n",
    "random.seed(random_seed)\n",
    "np.random.seed(random_seed)\n",
    "tf.random.set_seed(random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "pleasant-archive",
   "metadata": {},
   "outputs": [],
   "source": [
    "#다운 받은 KorQuAD 데이터 확인\n",
    "\n",
    "def print_json_tree(data, indent=\"\"):\n",
    "    for key, value in data.items():\n",
    "        if type(value) == list:     # list 형태의 item은 첫번째 item만 출력\n",
    "            print(f'{indent}- {key}: [{len(value)}]')\n",
    "            \n",
    "            #KorQuAD 데이터처럼 json 포맷으로 이루어진 데이터에서 리스트의 첫번째 아이템의 실제 내용을 간단히 확인하는데 유용함.\n",
    "            print_json_tree(value[0], indent + \"  \")\n",
    "            \n",
    "        else:\n",
    "            print(f'{indent}- {key}: {value}')\n",
    "            \n",
    "\n",
    "data_dir = os.getenv('HOME')+'/aiffel/bert_qna/data'\n",
    "model_dir = os.getenv('HOME')+'/aiffel/bert_qna/models'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "opposed-detroit",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- version: KorQuAD_v1.0_train\n",
      "- data: [1420]\n",
      "  - paragraphs: [3]\n",
      "    - qas: [8]\n",
      "      - answers: [1]\n",
      "        - text: 교향곡\n",
      "        - answer_start: 54\n",
      "      - id: 6566495-0-0\n",
      "      - question: 바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가?\n",
      "    - context: 1839년 바그너는 괴테의 파우스트을 처음 읽고 그 내용에 마음이 끌려 이를 소재로 해서 하나의 교향곡을 쓰려는 뜻을 갖는다. 이 시기 바그너는 1838년에 빛 독촉으로 산전수전을 다 걲은 상황이라 좌절과 실망에 가득했으며 메피스토펠레스를 만나는 파우스트의 심경에 공감했다고 한다. 또한 파리에서 아브네크의 지휘로 파리 음악원 관현악단이 연주하는 베토벤의 교향곡 9번을 듣고 깊은 감명을 받았는데, 이것이 이듬해 1월에 파우스트의 서곡으로 쓰여진 이 작품에 조금이라도 영향을 끼쳤으리라는 것은 의심할 여지가 없다. 여기의 라단조 조성의 경우에도 그의 전기에 적혀 있는 것처럼 단순한 정신적 피로나 실의가 반영된 것이 아니라 베토벤의 합창교향곡 조성의 영향을 받은 것을 볼 수 있다. 그렇게 교향곡 작곡을 1839년부터 40년에 걸쳐 파리에서 착수했으나 1악장을 쓴 뒤에 중단했다. 또한 작품의 완성과 동시에 그는 이 서곡(1악장)을 파리 음악원의 연주회에서 연주할 파트보까지 준비하였으나, 실제로는 이루어지지는 않았다. 결국 초연은 4년 반이 지난 후에 드레스덴에서 연주되었고 재연도 이루어졌지만, 이후에 그대로 방치되고 말았다. 그 사이에 그는 리엔치와 방황하는 네덜란드인을 완성하고 탄호이저에도 착수하는 등 분주한 시간을 보냈는데, 그런 바쁜 생활이 이 곡을 잊게 한 것이 아닌가 하는 의견도 있다.\n",
      "  - title: 파우스트_서곡\n"
     ]
    }
   ],
   "source": [
    "#훈련데이터\n",
    "train_json_path = data_dir + '/KorQuAD_v1.0_train.json'\n",
    "with open(train_json_path) as f:\n",
    "    train_json = json.load(f)\n",
    "    print_json_tree(train_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "legal-peace",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- version: KorQuAD_v1.0_dev\n",
      "- data: [140]\n",
      "  - paragraphs: [2]\n",
      "    - qas: [7]\n",
      "      - answers: [1]\n",
      "        - text: 1989년 2월 15일\n",
      "        - answer_start: 0\n",
      "      - id: 6548850-0-0\n",
      "      - question: 임종석이 여의도 농민 폭력 시위를 주도한 혐의로 지명수배 된 날은?\n",
      "    - context: 1989년 2월 15일 여의도 농민 폭력 시위를 주도한 혐의(폭력행위등처벌에관한법률위반)으로 지명수배되었다. 1989년 3월 12일 서울지방검찰청 공안부는 임종석의 사전구속영장을 발부받았다. 같은 해 6월 30일 평양축전에 임수경을 대표로 파견하여 국가보안법위반 혐의가 추가되었다. 경찰은 12월 18일~20일 사이 서울 경희대학교에서 임종석이 성명 발표를 추진하고 있다는 첩보를 입수했고, 12월 18일 오전 7시 40분 경 가스총과 전자봉으로 무장한 특공조 및 대공과 직원 12명 등 22명의 사복 경찰을 승용차 8대에 나누어 경희대학교에 투입했다. 1989년 12월 18일 오전 8시 15분 경 서울청량리경찰서는 호위 학생 5명과 함께 경희대학교 학생회관 건물 계단을 내려오는 임종석을 발견, 검거해 구속을 집행했다. 임종석은 청량리경찰서에서 약 1시간 동안 조사를 받은 뒤 오전 9시 50분 경 서울 장안동의 서울지방경찰청 공안분실로 인계되었다.\n",
      "  - title: 임종석\n"
     ]
    }
   ],
   "source": [
    "#검증데이터\n",
    "dev_json_path = data_dir + '/KorQuAD_v1.0_dev.json'\n",
    "with open(dev_json_path) as f:\n",
    "    dev_json = json.load(f)\n",
    "    print_json_tree(dev_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "third-knight",
   "metadata": {},
   "outputs": [],
   "source": [
    "#json 데이터 실제 형태 확인\n",
    "#print(json.dumps(train_json[\"data\"][0], indent=2, ensure_ascii=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charming-identity",
   "metadata": {},
   "source": [
    "## 2. KorQuAD 데이터 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subject-attraction",
   "metadata": {},
   "source": [
    "### 2.1 띄어쓰기 단위 정보관리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "closing-vermont",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _is_whitespace(c):\n",
    "    if c == \" \" or c == \"\\t\" or c == \"\\r\" or c == \"\\n\" or ord(c) == 0x202F:\n",
    "        return True\n",
    "    \n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "possible-zambia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('파우스트', '파우스트')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#whitespace가 2개인 경우를 처리해야 함\n",
    "\n",
    "string1 = '1839년 파우스트를 읽었다.'\n",
    "string2 = '1839년  파우스트를 읽었다.'\n",
    "string1[6:10], string2[7:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "surrounded-literacy",
   "metadata": {},
   "outputs": [],
   "source": [
    "#띄어쓰기 단위 정보 관리 기능 함수\n",
    "def _tokenize_whitespace(string):\n",
    "    word_tokens = []\n",
    "    char_to_word = []\n",
    "    prev_is_whitespace = True\n",
    "\n",
    "    for c in string:\n",
    "        if _is_whitespace(c):\n",
    "            prev_is_whitespace = True\n",
    "        else:\n",
    "            if prev_is_whitespace:\n",
    "                word_tokens.append(c)\n",
    "            else:\n",
    "                word_tokens[-1] += c\n",
    "            prev_is_whitespace = False    \n",
    "        char_to_word.append(len(word_tokens) - 1)\n",
    "    \n",
    "    return word_tokens, char_to_word"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excited-elite",
   "metadata": {},
   "source": [
    "- 띄어쓰기 단위로 token을 정리한 후, word token 영역별로 유니크한 숫자(어절 번호)를 부여함.\n",
    "- 글자 혹은 subword 단위로 token이 분리되는 것에 대비해서 원래 데이터가 띄어쓰기 단위로 어떠했었는지 word token 영역별로 추가 정보를 관리하면 도움이 됨."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "electric-manhattan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'1' : 0\n",
      "'8' : 0\n",
      "'3' : 0\n",
      "'9' : 0\n",
      "'년' : 0\n",
      "' ' : 0\n",
      "'파' : 1\n",
      "'우' : 1\n",
      "'스' : 1\n",
      "'트' : 1\n",
      "'를' : 1\n",
      "' ' : 1\n",
      "'읽' : 2\n",
      "'었' : 2\n",
      "'다' : 2\n",
      "'.' : 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(['1839년', '파우스트를', '읽었다.'], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#글자별로 word_token 영역을 표시해 주는 char_to_word list를 관리해 둠.\n",
    "#이 값은 현재 글자가 몇 번째 어절에 포함된 것이었는지를 말해 줌.\n",
    "\n",
    "#첫번째 문장(string1)에 대해 띄어쓰기 영역 정보를 표시\n",
    "word_tokens, char_to_word = _tokenize_whitespace(string1)\n",
    "for c, i in zip(list(string1), char_to_word):\n",
    "    print(f'\\'{c}\\' : {i}')\n",
    "\n",
    "word_tokens, char_to_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "collected-boring",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'1' : 0\n",
      "'8' : 0\n",
      "'3' : 0\n",
      "'9' : 0\n",
      "'년' : 0\n",
      "' ' : 0\n",
      "' ' : 0\n",
      "'파' : 1\n",
      "'우' : 1\n",
      "'스' : 1\n",
      "'트' : 1\n",
      "'를' : 1\n",
      "' ' : 1\n",
      "'읽' : 2\n",
      "'었' : 2\n",
      "'다' : 2\n",
      "'.' : 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(['1839년', '파우스트를', '읽었다.'],\n",
       " [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#두번째 문장(string2)에 대해 띄어쓰기 영역 정보를 표시\n",
    "word_tokens, char_to_word = _tokenize_whitespace(string2)\n",
    "for c, i in zip(list(string2), char_to_word):\n",
    "    print(f'\\'{c}\\' : {i}')\n",
    "\n",
    "word_tokens, char_to_word"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dressed-retirement",
   "metadata": {},
   "source": [
    "### 2.2 Tokenize by Vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dress-lancaster",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['▁1839', '년', '▁', '파우스트', '를', '▁읽', '었다', '.'], [0, 2, 5])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#vocab loading\n",
    "vocab = spm.SentencePieceProcessor()\n",
    "vocab.load(f\"{model_dir}/ko_32000.model\")\n",
    "\n",
    "#word를 subword로 변경하면서 index 저장\n",
    "word_to_token = []\n",
    "context_tokens = []\n",
    "for (i, word) in enumerate(word_tokens):\n",
    "    word_to_token.append(len(context_tokens))\n",
    "    tokens = vocab.encode_as_pieces(word)  #SentencePiece를 사용해 Subword로 쪼갬.\n",
    "    for token in tokens:\n",
    "        context_tokens.append(token)\n",
    "\n",
    "context_tokens, word_to_token"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qualified-boost",
   "metadata": {},
   "source": [
    "- word_to_token의 [0, 2, 5]란 context_tokens에 쪼개져 담긴 0번, 2번, 5번 토큰인 '▁1839', '▁', '▁읽' 이 어절단위의 첫번째 토큰이 된다는 정보를 담아둔 것."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "affecting-console",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sentencepice\n",
    "def _tokenize_vocab(vocab, context_words):\n",
    "    word_to_token = []\n",
    "    context_tokens = []\n",
    "    for (i, word) in enumerate(context_words):\n",
    "        word_to_token.append(len(context_tokens))\n",
    "        tokens = vocab.encode_as_pieces(word)\n",
    "        for token in tokens:\n",
    "            context_tokens.append(token)\n",
    "    return context_tokens, word_to_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cubic-shore",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1839년', '파우스트를', '읽었다.']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(['▁1839', '년', '▁', '파우스트', '를', '▁읽', '었다', '.'], [0, 2, 5])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#처리해야 할 word 단위 입력\n",
    "print(word_tokens)  \n",
    "\n",
    "context_tokens, word_to_token = _tokenize_vocab(vocab, word_tokens)\n",
    "\n",
    "#Subword 단위로 토큰화한 결과\n",
    "context_tokens, word_to_token"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grateful-barbados",
   "metadata": {},
   "source": [
    "### 2.3 Improve Span\n",
    "- KorQuAD 데이터셋은 질문(question)과 지문(context)을 주고, 지문 영역에서 정답(answer)을 찾도록 구성되어 있기 때문에 정답에 해당하는 지문 영역을 정확히 찾아내는 것이 전처리의 핵심적인 작업이 됨."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "proved-magnet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[context]  1839년 바그너는 괴테의 파우스트을 처음 읽고 그 내용에 마음이 끌려 이를 소재로 해서 하나의 교향곡을 쓰려는 뜻을 갖는다. 이 시기 바그너는 1838년에 빛 독촉으로 산전수전을 다 걲은 상황이라 좌절과 실망에 가득했으며 메피스토펠레스를 만나는 파우스트의 심경에 공감했다고 한다. 또한 파리에서 아브네크의 지휘로 파리 음악원 관현악단이 연주하는 베토벤의 교향곡 9번을 듣고 깊은 감명을 받았는데, 이것이 이듬해 1월에 파우스트의 서곡으로 쓰여진 이 작품에 조금이라도 영향을 끼쳤으리라는 것은 의심할 여지가 없다. 여기의 라단조 조성의 경우에도 그의 전기에 적혀 있는 것처럼 단순한 정신적 피로나 실의가 반영된 것이 아니라 베토벤의 합창교향곡 조성의 영향을 받은 것을 볼 수 있다. 그렇게 교향곡 작곡을 1839년부터 40년에 걸쳐 파리에서 착수했으나 1악장을 쓴 뒤에 중단했다. 또한 작품의 완성과 동시에 그는 이 서곡(1악장)을 파리 음악원의 연주회에서 연주할 파트보까지 준비하였으나, 실제로는 이루어지지는 않았다. 결국 초연은 4년 반이 지난 후에 드레스덴에서 연주되었고 재연도 이루어졌지만, 이후에 그대로 방치되고 말았다. 그 사이에 그는 리엔치와 방황하는 네덜란드인을 완성하고 탄호이저에도 착수하는 등 분주한 시간을 보냈는데, 그런 바쁜 생활이 이 곡을 잊게 한 것이 아닌가 하는 의견도 있다.\n",
      "\n",
      "\n",
      "[question]  바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가?\n",
      "\n",
      "\n",
      "[answer]  교향곡\n",
      "\n",
      "\n",
      "[answer_start] index:  54 character:  교\n",
      "\n",
      "\n",
      "[answer_end]index:  56 character:  곡\n"
     ]
    }
   ],
   "source": [
    "context = train_json['data'][0]['paragraphs'][0]['context']\n",
    "question = train_json['data'][0]['paragraphs'][0]['qas'][0]['question']\n",
    "answer_text = train_json['data'][0]['paragraphs'][0]['qas'][0]['answers'][0]['text']\n",
    "answer_start = train_json['data'][0]['paragraphs'][0]['qas'][0]['answers'][0]['answer_start']\n",
    "answer_end = answer_start + len(answer_text) - 1\n",
    "\n",
    "print('[context] ', context)\n",
    "print('\\n')\n",
    "print('[question] ', question)\n",
    "print('\\n')\n",
    "print('[answer] ', answer_text)\n",
    "print('\\n')\n",
    "print('[answer_start] index: ', answer_start, 'character: ', context[answer_start])\n",
    "print('\\n')\n",
    "print('[answer_end]index: ', answer_end, 'character: ', context[answer_end])\n",
    "\n",
    "#answer_text에 해당하는 context 영역을 정확히 찾아내야 함. \n",
    "assert context[answer_start:answer_end + 1] == answer_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "prepared-limit",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1839년', '바그너는', '괴테의', '파우스트을', '처음', '읽고', '그', '내용에', '마음이', '끌려', '이를', '소재로', '해서', '하나의', '교향곡을', '쓰려는', '뜻을', '갖는다.', '이', '시기']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3],\n",
       " '1839년 바그너는 괴테의 파우스트을')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#context를 띄어쓰기(word) 단위로 토큰화한 결과\n",
    "\n",
    "word_tokens, char_to_word = _tokenize_whitespace(context)\n",
    "\n",
    "print( word_tokens[:20])\n",
    "\n",
    "char_to_word[:20], context[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "civic-marathon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ['▁1839', '년']\n",
      "2 ['▁바그너', '는']\n",
      "4 ['▁괴테', '의']\n",
      "6 ['▁', '파우스트', '을']\n",
      "9 ['▁처음']\n",
      "10 ['▁읽고']\n",
      "11 ['▁그']\n",
      "12 ['▁내용에']\n",
      "13 ['▁마음이']\n",
      "14 ['▁끌려']\n",
      "15 ['▁이를']\n",
      "16 ['▁소재로']\n",
      "17 ['▁해서']\n",
      "18 ['▁하나의']\n",
      "19 ['▁교향곡', '을']\n",
      "21 ['▁쓰', '려는']\n",
      "23 ['▁뜻을']\n",
      "24 ['▁갖는다', '.']\n",
      "26 ['▁이']\n",
      "27 ['▁시기']\n"
     ]
    }
   ],
   "source": [
    "#띄어쓰기(word) 단위로 쪼개진 context(word_tokens)를 Subword로 토큰화한 결과\n",
    "\n",
    "context_tokens, word_to_token = _tokenize_vocab(vocab, word_tokens)\n",
    "\n",
    "for i in range(min(20, len(word_to_token) - 1)):\n",
    "    print(word_to_token[i], context_tokens[word_to_token[i]:word_to_token[i + 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "italian-samba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14, '교향곡', ['교향곡을'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#answer_start와 answer_end로부터 word_start와 word_end 구함.\n",
    "\n",
    "word_start = char_to_word[answer_start]\n",
    "word_end = char_to_word[answer_end]\n",
    "\n",
    "word_start, word_end, answer_text, word_tokens[word_start:word_end + 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "patent-grave",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 20, ['▁교향곡', '을'])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#word_start로부터 word_end까지의 context를 Subword 단위로 토큰화한 결과\n",
    "\n",
    "token_start = word_to_token[word_start]\n",
    "if word_end < len(word_to_token) - 1:\n",
    "    token_end = word_to_token[word_end + 1] - 1\n",
    "else:\n",
    "    token_end = len(context_tokens) - 1\n",
    "    \n",
    "token_start, token_end, context_tokens[token_start:token_end + 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "modified-difficulty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'▁교향곡'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#실제 정답인 answer_text도 Subword 기준으로 토큰화\n",
    "\n",
    "token_answer = \" \".join(vocab.encode_as_pieces(answer_text))\n",
    "\n",
    "token_answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "partial-madness",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X >> (19, 20) ▁교향곡 을\n",
      "O >> (19, 19) ▁교향곡\n",
      "X >> (20, 20) 을\n"
     ]
    }
   ],
   "source": [
    "#좀더 일반적인 방법으로 정답 토큰 범위를 찾는 코드\n",
    "\n",
    "#정답이 될수 있는 new_start와 new_end의 경우를 순회탐색\n",
    "\n",
    "for new_start in range(token_start, token_end + 1):\n",
    "    for new_end in range(token_end, new_start - 1, -1):\n",
    "        text_span = \" \".join(context_tokens[new_start : (new_end + 1)])\n",
    "        if text_span == token_answer:   #정답과 일치할 때\n",
    "            print(\"O >>\", (new_start, new_end), text_span)\n",
    "        else:\n",
    "            print(\"X >>\", (new_start, new_end), text_span)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "relevant-genealogy",
   "metadata": {},
   "outputs": [],
   "source": [
    "#context에서 answer의 위치를 토큰화된 상태에서 찾는 함수\n",
    "#context_tokens에서 char_answer의 위치를 찾아 리턴함.\n",
    "\n",
    "def _improve_span(vocab, context_tokens, token_start, token_end, char_answer):\n",
    "    token_answer = \" \".join(vocab.encode_as_pieces(char_answer))\n",
    "    for new_start in range(token_start, token_end + 1):\n",
    "        for new_end in range(token_end, new_start - 1, -1):\n",
    "            text_span = \" \".join(context_tokens[new_start : (new_end + 1)])\n",
    "            if text_span == token_answer:\n",
    "                return (new_start, new_end)\n",
    "    return (token_start, token_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "liked-yugoslavia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "token_start: 19  token_end: 19\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['▁교향곡']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_start, token_end = _improve_span(vocab, context_tokens, token_start, token_end, answer_text)\n",
    "\n",
    "print('token_start:', token_start, ' token_end:', token_end)\n",
    "\n",
    "context_tokens[token_start:token_end + 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reduced-halloween",
   "metadata": {},
   "source": [
    "### 2.4 데이터셋 분리 - train/dev\n",
    "- _improve_span() 함수 이용하여 전처리 후 파일로 저장."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "departmental-transport",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dump_korquad(vocab, json_data, out_file):\n",
    "    with open(out_file, \"w\") as f:\n",
    "        for data in tqdm(json_data[\"data\"]):\n",
    "            title = data[\"title\"]\n",
    "            for paragraph in data[\"paragraphs\"]:\n",
    "                context = paragraph[\"context\"]\n",
    "                context_words, char_to_word = _tokenize_whitespace(context)\n",
    "\n",
    "                for qa in paragraph[\"qas\"]:\n",
    "                    assert len(qa[\"answers\"]) == 1\n",
    "                    qa_id = qa[\"id\"]\n",
    "                    question = qa[\"question\"]\n",
    "                    answer_text = qa[\"answers\"][0][\"text\"]\n",
    "                    answer_start = qa[\"answers\"][0][\"answer_start\"]\n",
    "                    answer_end = answer_start + len(answer_text) - 1\n",
    "\n",
    "                    assert answer_text == context[answer_start:answer_end + 1]\n",
    "\n",
    "                    word_start = char_to_word[answer_start]\n",
    "                    word_end = char_to_word[answer_end]\n",
    "\n",
    "                    word_answer = \" \".join(context_words[word_start:word_end + 1])\n",
    "                    char_answer = \" \".join(answer_text.strip().split())\n",
    "                    assert char_answer in word_answer\n",
    "\n",
    "                    context_tokens, word_to_token = _tokenize_vocab(vocab, context_words)\n",
    "\n",
    "                    token_start = word_to_token[word_start]\n",
    "                    if word_end < len(word_to_token) - 1:\n",
    "                        token_end = word_to_token[word_end + 1] - 1\n",
    "                    else:\n",
    "                        token_end = len(context_tokens) - 1\n",
    "\n",
    "                    token_start, token_end = _improve_span(vocab, context_tokens, token_start, token_end, char_answer)\n",
    "\n",
    "                    data = {\"qa_id\": qa_id, \"title\": title, \"question\": vocab.encode_as_pieces(question), \"context\": context_tokens, \"answer\": char_answer, \"token_start\": token_start, \"token_end\":token_end}\n",
    "                    f.write(json.dumps(data, ensure_ascii=False))\n",
    "                    f.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "shared-resource",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3a8049a87094ed2b6eb8aff04af2b8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1420 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e090b51b7af04df2af143201f6a2c794",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/140 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#전처리후 파일로 저장\n",
    "\n",
    "dump_korquad(vocab, train_json, f\"{data_dir}/korquad_train.json\")\n",
    "dump_korquad(vocab, dev_json, f\"{data_dir}/korquad_dev.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "communist-appeal",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_file(filename, count=10):\n",
    "    \"\"\"\n",
    "    파일 내용 출력\n",
    "    :param filename: 파일 이름\n",
    "    :param count: 출력 라인 수\n",
    "    \"\"\"\n",
    "    with open(filename) as f:\n",
    "        for i, line in enumerate(f):\n",
    "            if count <= i:\n",
    "                break\n",
    "            print(line.strip())\n",
    "\n",
    "#print_file(f\"{data_dir}/korquad_train.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "civic-fellowship",
   "metadata": {},
   "source": [
    "### 2.5 데이터 분석 : Question "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "crude-cursor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 ['▁바그너', '는', '▁괴테', '의', '▁', '파우스트', '를', '▁읽고', '▁무엇을', '▁쓰고', '자', '▁', '했', '는', '가', '?']\n",
      "168 ['▁바그너', '는', '▁교향곡', '▁작곡', '을', '▁어디', '까지', '▁쓴', '▁뒤에', '▁중단', '했', '는', '가', '?']\n",
      "80 ['▁바그너', '가', '▁', '파우스트', '▁서', '곡을', '▁쓸', '▁때', '▁어떤', '▁곡', '의', '▁영향을', '▁받았', '는', '가', '?']\n",
      "6 ['▁1839', '년', '▁바그너', '가', '▁교향곡', '의', '▁소재로', '▁쓰', '려고', '▁했던', '▁책은', '?']\n",
      "143 ['▁', '파우스트', '▁서', '곡', '의', '▁라', '단', '조', '▁조성', '이', '▁영향을', '▁받은', '▁베토벤', '의', '▁곡은', '?']\n",
      "0 ['▁바그너', '가', '▁', '파우스트', '를', '▁처음으로', '▁읽', '은', '▁', '년', '도', '는', '?']\n",
      "165 ['▁바그너', '가', '▁처음', '▁교향곡', '▁작곡', '을', '▁한', '▁장소', '는', '?']\n",
      "216 ['▁바그너', '의', '▁1', '악장', '의', '▁초연', '은', '▁어디서', '▁연주', '되었', '는', '가', '?']\n",
      "164 ['▁바그너', '의', '▁작품을', '▁시인', '의', '▁피로', '▁쓰여', '졌다', '고', '▁극찬', '한', '▁것은', '▁누구', '인', '가', '?']\n",
      "7 ['▁잊', '혀', '져', '▁있는', '▁', '파우스트', '▁서', '곡', '▁1', '악장', '을', '▁부활', '시킨', '▁것은', '▁누구', '인', '가', '?']\n"
     ]
    }
   ],
   "source": [
    "#전체 데이터에서 question 항목의 길이 분포를 조사\n",
    "\n",
    "questions = []\n",
    "contexts = []\n",
    "token_starts = []\n",
    "with open(f\"{data_dir}/korquad_train.json\") as f:\n",
    "    for i, line in enumerate(f):\n",
    "        data = json.loads(line)\n",
    "        questions.append(data[\"question\"])\n",
    "        contexts.append(data[\"context\"])\n",
    "        token_starts.append(data[\"token_start\"])\n",
    "        if i < 10:\n",
    "            print(data[\"token_start\"], data[\"question\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "tutorial-winter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16, 14, 16, 12, 16, 13, 10, 13, 16, 18]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#token count\n",
    "train_question_counts = [len(question) for question in questions]\n",
    "train_question_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "realistic-pathology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.hist(train_question_counts, bins=100, range=[0, 100], facecolor='b', label='train')\n",
    "plt.title('Count of question')\n",
    "plt.xlabel('Number of question')\n",
    "plt.ylabel('Count of question')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "destroyed-retention",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "question 길이 최대:      58\n",
      "question 길이 최소:       3\n",
      "question 길이 평균:      15.25\n",
      "question 길이 표준편차:    5.50\n",
      "question 25/100분위:    11.00\n",
      "question 50/100분위:    14.00\n",
      "question 75/100분위:    18.00\n",
      "question IQR:           7.00\n",
      "question MAX/100분위:   28.50\n"
     ]
    }
   ],
   "source": [
    "#데이터 길이\n",
    "print(f\"question 길이 최대:    {np.max(train_question_counts):4d}\")\n",
    "print(f\"question 길이 최소:    {np.min(train_question_counts):4d}\")\n",
    "print(f\"question 길이 평균:    {np.mean(train_question_counts):7.2f}\")\n",
    "print(f\"question 길이 표준편차: {np.std(train_question_counts):7.2f}\")\n",
    "\n",
    "\"\"\"\n",
    "https://ko.wikipedia.org/wiki/%EB%B0%B1%EB%B6%84%EC%9C%84%EC%88%98\n",
    "백분위수(Percentile)는 크기가 있는 값들로 이뤄진 자료를 순서대로 나열했을 때 백분율로 나타낸 특정 위치의 값을 이르는 용어이다.\n",
    "일반적으로 크기가 작은 것부터 나열하여 가장 작은 것을 0, 가장 큰 것을 100으로 한다.\n",
    "100개의 값을 가진 어떤 자료의 20 백분위수는 그 자료의 값들 중 20번째로 작은 값을 뜻한다. 50 백분위수는 중앙값과 같다.\n",
    "\"\"\"\n",
    "percentile25 = np.percentile(train_question_counts, 25)\n",
    "percentile50 = np.percentile(train_question_counts, 50)\n",
    "percentile75 = np.percentile(train_question_counts, 75)\n",
    "percentileIQR = percentile75 - percentile25\n",
    "percentileMAX = percentile75 + percentileIQR * 1.5\n",
    "\n",
    "print(f\"question 25/100분위:  {percentile25:7.2f}\")\n",
    "print(f\"question 50/100분위:  {percentile50:7.2f}\")\n",
    "print(f\"question 75/100분위:  {percentile75:7.2f}\")\n",
    "print(f\"question IQR:        {percentileIQR:7.2f}\")\n",
    "print(f\"question MAX/100분위: {percentileMAX:7.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "pursuant-selling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 6))\n",
    "\n",
    "# 박스플롯 생성\n",
    "# 첫번째 파라메터: 여러 분포에 대한 데이터 리스트를\n",
    "# labels: 입력한 데이터에 대한 라벨\n",
    "# showmeans: 평균값을 표현\n",
    "\n",
    "plt.boxplot(train_question_counts, labels=['token counts'], showmeans=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tribal-death",
   "metadata": {},
   "source": [
    "### 2.6 데이터 분석 : Context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "adjustable-glance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[278, 278, 278, 278, 278, 278, 278, 278, 209, 209]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#token count\n",
    "train_context_counts = [len(context) for context in contexts]\n",
    "\n",
    "train_context_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "knowing-processor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.hist(train_context_counts, bins=900, range=[100, 1000], facecolor='r', label='train')\n",
    "plt.title('Count of context')\n",
    "plt.xlabel('Number of context')\n",
    "plt.ylabel('Count of context')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "tired-draft",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "context 길이 최대:    4816\n",
      "context 길이 최소:     108\n",
      "context 길이 평균:     222.84\n",
      "context 길이 표준편차:   97.68\n",
      "context 25/100분위:   169.00\n",
      "context 50/100분위:   199.00\n",
      "context 75/100분위:   248.00\n",
      "context IQR:          79.00\n",
      "context MAX/100분위:  366.50\n"
     ]
    }
   ],
   "source": [
    "# 데이터 길이\n",
    "print(f\"context 길이 최대:    {np.max(train_context_counts):4d}\")\n",
    "print(f\"context 길이 최소:    {np.min(train_context_counts):4d}\")\n",
    "print(f\"context 길이 평균:    {np.mean(train_context_counts):7.2f}\")\n",
    "print(f\"context 길이 표준편차: {np.std(train_context_counts):7.2f}\")\n",
    "\n",
    "percentile25 = np.percentile(train_context_counts, 25)\n",
    "percentile50 = np.percentile(train_context_counts, 50)\n",
    "percentile75 = np.percentile(train_context_counts, 75)\n",
    "percentileIQR = percentile75 - percentile25\n",
    "percentileMAX = percentile75 + percentileIQR * 1.5\n",
    "\n",
    "print(f\"context 25/100분위:  {percentile25:7.2f}\")\n",
    "print(f\"context 50/100분위:  {percentile50:7.2f}\")\n",
    "print(f\"context 75/100분위:  {percentile75:7.2f}\")\n",
    "print(f\"context IQR:        {percentileIQR:7.2f}\")\n",
    "print(f\"context MAX/100분위: {percentileMAX:7.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "compliant-primary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 6))\n",
    "plt.boxplot(train_context_counts, labels=['token counts'], showmeans=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "concerned-mongolia",
   "metadata": {},
   "source": [
    "### 2.7 데이터 분석 : Answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "grand-thing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[19, 168, 80, 6, 143, 0, 165, 216, 164, 7]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#token count\n",
    "train_answer_starts = token_starts\n",
    "train_answer_starts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "center-hormone",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.hist(train_answer_starts, bins=500, range=[0, 500], facecolor='g', label='train')\n",
    "\n",
    "plt.title('Count of answer')\n",
    "plt.xlabel('Number of answer')\n",
    "plt.ylabel('Count of answer')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "underlying-tonight",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "answer 위치 최대:    1124\n",
      "answer 위치 최소:       0\n",
      "answer 위치 평균:      89.01\n",
      "answer 위치 표준편차:   78.21\n",
      "answer 25/100분위:    25.00\n",
      "answer 50/100분위:    74.00\n",
      "answer 75/100분위:   134.00\n",
      "answer IQR:         109.00\n",
      "answer MAX/100분위:  297.50\n"
     ]
    }
   ],
   "source": [
    "#데이터 길이\n",
    "print(f\"answer 위치 최대:    {np.max(train_answer_starts):4d}\")\n",
    "print(f\"answer 위치 최소:    {np.min(train_answer_starts):4d}\")\n",
    "print(f\"answer 위치 평균:    {np.mean(train_answer_starts):7.2f}\")\n",
    "print(f\"answer 위치 표준편차: {np.std(train_answer_starts):7.2f}\")\n",
    "\n",
    "percentile25 = np.percentile(train_answer_starts, 25)\n",
    "percentile50 = np.percentile(train_answer_starts, 50)\n",
    "percentile75 = np.percentile(train_answer_starts, 75)\n",
    "percentileIQR = percentile75 - percentile25\n",
    "percentileMAX = percentile75 + percentileIQR * 1.5\n",
    "\n",
    "print(f\"answer 25/100분위:  {percentile25:7.2f}\")\n",
    "print(f\"answer 50/100분위:  {percentile50:7.2f}\")\n",
    "print(f\"answer 75/100분위:  {percentile75:7.2f}\")\n",
    "print(f\"answer IQR:        {percentileIQR:7.2f}\")\n",
    "print(f\"answer MAX/100분위: {percentileMAX:7.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "following-brooks",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 6))\n",
    "plt.boxplot(train_answer_starts, labels=['token counts'], showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comparative-southeast",
   "metadata": {},
   "source": [
    "### 2.8 데이터 분석 : Word Cloud\n",
    "- 워드 클라우드(Word Cloud)란 자료의 빈도수를 시각화해서 나타내는 방법.\n",
    "- 문서의 핵심 단어를 한눈에 파악하고 데이터의 특징을 도출할 수 있음.\n",
    "- 빈도수가 높은 단어일수록 글씨 크기가 큰 특징이 있음."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "sunrise-ghana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "312b03ee023f4541b6a5d467d23eaf02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1420 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['파우스트_서곡',\n",
       " '1839년 바그너는 괴테의 파우스트을 처음 읽고 그 내용에 마음이 끌려 이를 소재로 해서 하나의 교향곡을 쓰려는 뜻을 갖는다. 이 시기 바그너는 1838년에 빛 독촉으로 산전수전을 다 걲은 상황이라 좌절과 실망에 가득했으며 메피스토펠레스를 만나는 파우스트의 심경에 공감했다고 한다. 또한 파리에서 아브네크의 지휘로 파리 음악원 관현악단이 연주하는 베토벤의 교향곡 9번을 듣고 깊은 감명을 받았는데, 이것이 이듬해 1월에 파우스트의 서곡으로 쓰여진 이 작품에 조금이라도 영향을 끼쳤으리라는 것은 의심할 여지가 없다. 여기의 라단조 조성의 경우에도 그의 전기에 적혀 있는 것처럼 단순한 정신적 피로나 실의가 반영된 것이 아니라 베토벤의 합창교향곡 조성의 영향을 받은 것을 볼 수 있다. 그렇게 교향곡 작곡을 1839년부터 40년에 걸쳐 파리에서 착수했으나 1악장을 쓴 뒤에 중단했다. 또한 작품의 완성과 동시에 그는 이 서곡(1악장)을 파리 음악원의 연주회에서 연주할 파트보까지 준비하였으나, 실제로는 이루어지지는 않았다. 결국 초연은 4년 반이 지난 후에 드레스덴에서 연주되었고 재연도 이루어졌지만, 이후에 그대로 방치되고 말았다. 그 사이에 그는 리엔치와 방황하는 네덜란드인을 완성하고 탄호이저에도 착수하는 등 분주한 시간을 보냈는데, 그런 바쁜 생활이 이 곡을 잊게 한 것이 아닌가 하는 의견도 있다.',\n",
       " '바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가?',\n",
       " '바그너는 교향곡 작곡을 어디까지 쓴 뒤에 중단했는가?',\n",
       " '바그너가 파우스트 서곡을 쓸 때 어떤 곡의 영향을 받았는가?']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train documents\n",
    "documents = []\n",
    "\n",
    "#전체 데이터에서 title, context, question 문장을 모두 추출\n",
    "for data in tqdm(train_json[\"data\"]):\n",
    "    title = data[\"title\"]\n",
    "    documents.append(title)\n",
    "    for paragraph in data[\"paragraphs\"]:\n",
    "        context = paragraph[\"context\"]\n",
    "        documents.append(context)\n",
    "\n",
    "        for qa in paragraph[\"qas\"]:\n",
    "            assert len(qa[\"answers\"]) == 1\n",
    "            question = qa[\"question\"]\n",
    "            documents.append(question)\n",
    "\n",
    "documents[:5]   #앞 5개만 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "monetary-token",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'파우스트_서곡 1839년 바그너는 괴테의 파우스트을 처음 읽고 그 내용에 마음이 끌려 이를 소재로 해서 하나의 교향곡을 쓰려는 뜻을 갖는다. 이 시기 바그너는 1838년에 빛 독촉으로 산전수전을 다 걲은 상황이라 좌절과 실망에 가득했으며 메피스토펠레스를 만나는 파우스트의 심경에 공감했다고 한다. 또한 파리에서 아브네크의 지휘로 파리 음악원 관현악단이 연주하는 베토벤의 교향곡 9번을 듣고 깊은 감명을 받았는데, 이것이 이듬해 1월에 파우스트의 서곡으로 쓰여진 이 작품에 조금이라도 영향을 끼쳤으리라는 것은 의심할 여지가 없다. 여기의 라단조 조성의 경우에도 그의 전기에 적혀 있는 것처럼 단순한 정신적 피로나 실의가 반영된 것이 아니라 베토벤의 합창교향곡 조성의 영향을 받은 것을 볼 수 있다. 그렇게 교향곡 작곡을 1839년부터 40년에 걸쳐 파리에서 착수했으나 1악장을 쓴 뒤에 중단했다. 또한 작품의 완성과 동시에 그는 이 서곡(1악장)을 파리 음악원의 연주회에서 연주할 파트보까지 준비하였으나, 실제로는 이루어지지는 않았다. 결국 초연은 4년 반이 지난 후에 드레스덴에서 연주되었고 재연도 이루어졌지만, 이후에 그대로 방치되고 말았다. 그 사이에 그는 리엔치와 방황하는 네덜란드인을 완성하고 탄호이저에도 착수하는 등 분주한 시간을 보냈는데, 그런 바쁜 생활이 이 곡을 잊게 한 것이 아닌가 하는 의견도 있다. 바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가? 바그너는 교향곡 작곡을 어디까지 쓴 뒤에 중단했는가? 바그너가 파우스트 서곡을 쓸 때 어떤 곡의 영향을 받았는가?'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#documents를 전부 이어 하나의 문장으로 만들기\n",
    "\" \".join(documents[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "hazardous-variance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#WordCloud로 \" \".join(documents)를 처리\n",
    "wordcloud = WordCloud(width=800, height=800, font_path='/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf').generate(\" \".join(documents))\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')   #bilinear 보간법 사용\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deluxe-speed",
   "metadata": {},
   "source": [
    "### 2.9 데이터 로드\n",
    "- 지금까지 만든 데이터셋을 메모리에 로드."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "standard-allen",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_json = os.path.join(data_dir, \"korquad_train.json\")\n",
    "dev_json = os.path.join(data_dir, \"korquad_dev.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "interracial-comment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_seq_length': 384, 'max_query_length': 64}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Config(dict):\n",
    "    \"\"\"\n",
    "    json을 config 형태로 사용하기 위한 Class\n",
    "    :param dict: config dictionary\n",
    "    \"\"\"\n",
    "    __getattr__ = dict.__getitem__\n",
    "    __setattr__ = dict.__setitem__\n",
    "\n",
    "\n",
    "args = Config({\n",
    "    'max_seq_length': 384,\n",
    "    'max_query_length': 64,\n",
    "})\n",
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "parallel-poland",
   "metadata": {},
   "outputs": [],
   "source": [
    "#생성한 데이터셋 파일을 메모리에 로딩하는 함수\n",
    "def load_data(args, filename):\n",
    "    inputs, segments, labels_start, labels_end = [], [], [], []\n",
    "\n",
    "    n_discard = 0\n",
    "    with open(filename, \"r\") as f:\n",
    "        for i, line in enumerate(tqdm(f, desc=f\"Loading ...\")):\n",
    "            data = json.loads(line)\n",
    "            token_start = data.get(\"token_start\")\n",
    "            token_end = data.get(\"token_end\")\n",
    "            question = data[\"question\"][:args.max_query_length]\n",
    "            context = data[\"context\"]\n",
    "            answer_tokens = \" \".join(context[token_start:token_end + 1])\n",
    "            context_len = args.max_seq_length - len(question) - 3\n",
    "\n",
    "            if token_end >= context_len:\n",
    "                #최대 길이내에 token이 들어가지 않은 경우 처리하지 않음.\n",
    "                n_discard += 1\n",
    "                continue\n",
    "            context = context[:context_len]\n",
    "            assert len(question) + len(context) <= args.max_seq_length - 3\n",
    "\n",
    "            tokens = ['[CLS]'] + question + ['[SEP]'] + context + ['[SEP]']\n",
    "            ids = [vocab.piece_to_id(token) for token in tokens]\n",
    "            ids += [0] * (args.max_seq_length - len(ids))\n",
    "            inputs.append(ids)\n",
    "            segs = [0] * (len(question) + 2) + [1] * (len(context) + 1)\n",
    "            segs += [0] * (args.max_seq_length - len(segs))\n",
    "            segments.append(segs)\n",
    "            token_start += (len(question) + 2)\n",
    "            labels_start.append(token_start)\n",
    "            token_end += (len(question) + 2)\n",
    "            labels_end.append(token_end)\n",
    "    print(f'n_discard: {n_discard}')\n",
    "\n",
    "    return (np.array(inputs), np.array(segments)), (np.array(labels_start), np.array(labels_end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "modular-rental",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4bc5afa11c7e49d6840ab9bbde799b0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading ...: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_discard: 430\n",
      "train_inputs: (59977, 384)\n",
      "train_inputs: (59977, 384)\n",
      "train_labels: (59977,)\n",
      "train_labels: (59977,)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "858d49f6fd6c40d2b953d21a703014b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading ...: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_discard: 78\n",
      "dev_inputs: (5696, 384)\n",
      "dev_inputs: (5696, 384)\n",
      "dev_labels: (5696,)\n",
      "dev_labels: (5696,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((array([[    5, 15798,    10, ...,     0,     0,     0],\n",
       "         [    5, 15798,    10, ...,     0,     0,     0],\n",
       "         [    5, 15798,    19, ...,     0,     0,     0],\n",
       "         ...,\n",
       "         [    5, 21666,    19, ...,     0,     0,     0],\n",
       "         [    5,   964, 16865, ...,     0,     0,     0],\n",
       "         [    5,   365,    15, ...,     0,     0,     0]]),\n",
       "  array([[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]])),\n",
       " (array([ 37, 184,  98, ...,  74, 190,  35]),\n",
       "  array([ 37, 185, 102, ...,  75, 191,  44])))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train data\n",
    "train_inputs, train_labels = load_data(args, train_json)\n",
    "print(f\"train_inputs: {train_inputs[0].shape}\")\n",
    "print(f\"train_inputs: {train_inputs[1].shape}\")\n",
    "print(f\"train_labels: {train_labels[0].shape}\")\n",
    "print(f\"train_labels: {train_labels[1].shape}\")\n",
    "\n",
    "#dev data\n",
    "dev_inputs, dev_labels = load_data(args, dev_json)\n",
    "print(f\"dev_inputs: {dev_inputs[0].shape}\")\n",
    "print(f\"dev_inputs: {dev_inputs[1].shape}\")\n",
    "print(f\"dev_labels: {dev_labels[0].shape}\")\n",
    "print(f\"dev_labels: {dev_labels[1].shape}\")\n",
    "\n",
    "train_inputs[:10], train_labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "determined-istanbul",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[    5 15798    10 28935     9    11 29566    20 14604 20424  3904    70\n",
      "    11  4648    10    19  1910     4 22070    15 15798    10 28935     9\n",
      "    11 29566    16   626 14604    38 14028 11773 13829   384  8376  3021\n",
      "  1239  6874    16  1687  5958  2694  5061     7    30  1613 15798    10\n",
      " 28065    75  4415  1816  4978    27   347   145   107  2703   263    11\n",
      "     1    18  5853    99  9677    24 11969    13  7595   437  1019  5907\n",
      "   257  3794  1972    20 11278    11 29566     9   612 12631 13214  1732\n",
      "    76     7   110  8802 17581   354  9648  2060    21  1682 22110 18164\n",
      "    17 21076 14980     9  6874    81 11325  4239  3597  1010  1035 17670\n",
      "     8  2447  1306    35   443    11 29566     9   315 12729 14457    30\n",
      "  7938  3742 10766   634  9971 17590 19424    10   285  4080    61 17573\n",
      "   483     7  7588     9   473   338   147  1924     9 11016   136  1034\n",
      "    13 11672    40  3436  5217  7898 11684    57   830     9    19  3319\n",
      "    86   220   464 14980     9 20515   412   991   684  1924     9   634\n",
      "   920   144   430    34    25     7  4210  6874  2150    16 22070   298\n",
      "  1159    75  1098  8802  7490   805    35 18678    16  1657  1970  2272\n",
      "    53     7   110  6559  2178    24   756    82    30   315   684  3772\n",
      " 18678    12    16  1682 22110     9 22469    22  1757    61  8817   194\n",
      "   164  1693   749     8  6739 12202    10   494     7   502 12181    18\n",
      "    46    15   374    17  1680   708 26344    22  1757   432   465   351\n",
      "    32 18563   710     8  2585  1384 16071   265  3360     7    38   747\n",
      "    82   383   678   200    26   590  1281    41  1172    31    16  2178\n",
      "    43  3044   156    17   647   468  7490    41    84   758    92    33\n",
      "  3401   369 18319     8  2582 29798  1102    17    30  4573 11170   139\n",
      "    58   220   773    19   211 23824    25     7     4     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0]\n",
      "\n",
      "\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "\n",
      "\n",
      "37 37\n"
     ]
    }
   ],
   "source": [
    "###만든 데이터셋 최종 결과###\n",
    "\n",
    "#Question과 Context가 포함된 입력데이터 1번째\n",
    "print(train_inputs[0][0])\n",
    "print('\\n')\n",
    "\n",
    "#Question을 0으로, Context를 1로 구분해 준 Segment 데이터 1번째\n",
    "print(train_inputs[1][0])\n",
    "print('\\n')\n",
    "\n",
    "#Answer위치의 시작점과 끝점 라벨 1번째\n",
    "print(train_labels[0][0], train_labels[1][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portable-costa",
   "metadata": {},
   "source": [
    "## 3. BERT 모델 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "balanced-tobacco",
   "metadata": {},
   "outputs": [],
   "source": [
    "#유틸리티 함수들\n",
    "\n",
    "def get_pad_mask(tokens, i_pad=0):\n",
    "    \"\"\"\n",
    "    pad mask 계산하는 함수\n",
    "    :param tokens: tokens (bs, n_seq)\n",
    "    :param i_pad: id of pad\n",
    "    :return mask: pad mask (pad: 1, other: 0)\n",
    "    \"\"\"\n",
    "    mask = tf.cast(tf.math.equal(tokens, i_pad), tf.float32)\n",
    "    mask = tf.expand_dims(mask, axis=1)\n",
    "    return mask\n",
    "\n",
    "\n",
    "def get_ahead_mask(tokens, i_pad=0):\n",
    "    \"\"\"\n",
    "    ahead mask 계산하는 함수\n",
    "    :param tokens: tokens (bs, n_seq)\n",
    "    :param i_pad: id of pad\n",
    "    :return mask: ahead and pad mask (ahead or pad: 1, other: 0)\n",
    "    \"\"\"\n",
    "    n_seq = tf.shape(tokens)[1]\n",
    "    ahead_mask = 1 - tf.linalg.band_part(tf.ones((n_seq, n_seq)), -1, 0)\n",
    "    ahead_mask = tf.expand_dims(ahead_mask, axis=0)\n",
    "    pad_mask = get_pad_mask(tokens, i_pad)\n",
    "    mask = tf.maximum(ahead_mask, pad_mask)\n",
    "    return mask\n",
    "\n",
    "\n",
    "@tf.function(experimental_relax_shapes=True)\n",
    "def gelu(x):\n",
    "    \"\"\"\n",
    "    gelu activation 함수\n",
    "    :param x: 입력 값\n",
    "    :return: gelu activation result\n",
    "    \"\"\"\n",
    "    return 0.5 * x * (1 + K.tanh(x * 0.7978845608 * (1 + 0.044715 * x * x)))\n",
    "\n",
    "\n",
    "def kernel_initializer(stddev=0.02):\n",
    "    \"\"\"\n",
    "    parameter initializer 생성\n",
    "    :param stddev: 생성할 랜덤 변수의 표준편차\n",
    "    \"\"\"\n",
    "    return tf.keras.initializers.TruncatedNormal(stddev=stddev)\n",
    "\n",
    "\n",
    "def bias_initializer():\n",
    "    \"\"\"\n",
    "    bias initializer 생성\n",
    "    \"\"\"\n",
    "    return tf.zeros_initializer\n",
    "\n",
    "\n",
    "class Config(dict):\n",
    "    \"\"\"\n",
    "    json을 config 형태로 사용하기 위한 Class\n",
    "    :param dict: config dictionary\n",
    "    \"\"\"\n",
    "    __getattr__ = dict.__getitem__\n",
    "    __setattr__ = dict.__setitem__\n",
    "\n",
    "    @classmethod\n",
    "    def load(cls, file):\n",
    "        \"\"\"\n",
    "        file에서 Config를 생성 함\n",
    "        :param file: filename\n",
    "        \"\"\"\n",
    "        with open(file, 'r') as f:\n",
    "            config = json.loads(f.read())\n",
    "            return Config(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "homeless-software",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mode == \"embedding\" 일 경우 Token Embedding Layer 로 사용되는 layer 클래스. \n",
    "\n",
    "class SharedEmbedding(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Weighed Shared Embedding Class\n",
    "    \"\"\"\n",
    "    def __init__(self, config, name=\"weight_shared_embedding\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param config: Config 객체\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "\n",
    "        self.n_vocab = config.n_vocab\n",
    "        self.d_model = config.d_model\n",
    "    \n",
    "    def build(self, input_shape):\n",
    "        \"\"\"\n",
    "        shared weight 생성\n",
    "        :param input_shape: Tensor Shape (not used)\n",
    "        \"\"\"\n",
    "        with tf.name_scope(\"shared_embedding_weight\"):\n",
    "            self.shared_weights = self.add_weight(\n",
    "                \"weights\",\n",
    "                shape=[self.n_vocab, self.d_model],\n",
    "                initializer=kernel_initializer()\n",
    "            )\n",
    "\n",
    "    def call(self, inputs, mode=\"embedding\"):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param inputs: 입력\n",
    "        :param mode: 실행 모드\n",
    "        :return: embedding or linear 실행 결과\n",
    "        \"\"\"\n",
    "        # mode가 embedding일 경우 embedding lookup 실행\n",
    "        if mode == \"embedding\":\n",
    "            return self._embedding(inputs)\n",
    "        # mode가 linear일 경우 linear 실행\n",
    "        elif mode == \"linear\":\n",
    "            return self._linear(inputs)\n",
    "        # mode가 기타일 경우 오류 발생\n",
    "        else:\n",
    "            raise ValueError(f\"mode {mode} is not valid.\")\n",
    "    \n",
    "    def _embedding(self, inputs):\n",
    "        \"\"\"\n",
    "        embedding lookup\n",
    "        :param inputs: 입력\n",
    "        \"\"\"\n",
    "        embed = tf.gather(self.shared_weights, tf.cast(inputs, tf.int32))\n",
    "        return embed\n",
    "\n",
    "    def _linear(self, inputs):  # (bs, n_seq, d_model)\n",
    "        \"\"\"\n",
    "        linear 실행\n",
    "        :param inputs: 입력\n",
    "        \"\"\"\n",
    "        n_batch = tf.shape(inputs)[0]\n",
    "        n_seq = tf.shape(inputs)[1]\n",
    "        inputs = tf.reshape(inputs, [-1, self.d_model])  # (bs * n_seq, d_model)\n",
    "        outputs = tf.matmul(inputs, self.shared_weights, transpose_b=True)\n",
    "        outputs = tf.reshape(outputs, [n_batch, n_seq, self.n_vocab])  # (bs, n_seq, n_vocab)\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "printable-therapy",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionalEmbedding(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Positional Embedding Class\n",
    "    \"\"\"\n",
    "    def __init__(self, config, name=\"position_embedding\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param config: Config 객체\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "        \n",
    "        self.embedding = tf.keras.layers.Embedding(config.n_seq, config.d_model, embeddings_initializer=kernel_initializer())\n",
    "\n",
    "    def call(self, inputs):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param inputs: 입력\n",
    "        :return embed: positional embedding lookup 결과\n",
    "        \"\"\"\n",
    "        position = tf.cast(tf.math.cumsum(tf.ones_like(inputs), axis=1, exclusive=True), tf.int32)\n",
    "        embed = self.embedding(position)\n",
    "        return embed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "western-circuit",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ScaleDotProductAttention(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Scale Dot Product Attention Class\n",
    "    \"\"\"\n",
    "    def __init__(self, name=\"scale_dot_product_attention\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "\n",
    "    def call(self, Q, K, V, attn_mask):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param Q: Q value\n",
    "        :param K: K value\n",
    "        :param V: V value\n",
    "        :param attn_mask: 실행 모드\n",
    "        :return attn_out: attention 실행 결과\n",
    "        \"\"\"\n",
    "        attn_score = tf.matmul(Q, K, transpose_b=True)\n",
    "        scale = tf.math.sqrt(tf.cast(tf.shape(K)[-1], tf.float32))\n",
    "        attn_scale = tf.math.divide(attn_score, scale)\n",
    "        attn_scale -= 1.e9 * attn_mask\n",
    "        attn_prob = tf.nn.softmax(attn_scale, axis=-1)\n",
    "        attn_out = tf.matmul(attn_prob, V)\n",
    "        return attn_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "strategic-competition",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttention(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Multi Head Attention Class\n",
    "    \"\"\"\n",
    "    def __init__(self, config, name=\"multi_head_attention\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param config: Config 객체\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "\n",
    "        self.d_model = config.d_model\n",
    "        self.n_head = config.n_head\n",
    "        self.d_head = config.d_head\n",
    "\n",
    "        # Q, K, V input dense layer\n",
    "        self.W_Q = tf.keras.layers.Dense(config.n_head * config.d_head, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "        self.W_K = tf.keras.layers.Dense(config.n_head * config.d_head, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "        self.W_V = tf.keras.layers.Dense(config.n_head * config.d_head, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "        # Scale Dot Product Attention class\n",
    "        self.attention = ScaleDotProductAttention(name=\"self_attention\")\n",
    "        # output dense layer\n",
    "        self.W_O = tf.keras.layers.Dense(config.d_model, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "\n",
    "    def call(self, Q, K, V, attn_mask):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param Q: Q value\n",
    "        :param K: K value\n",
    "        :param V: V value\n",
    "        :param attn_mask: 실행 모드\n",
    "        :return attn_out: attention 실행 결과\n",
    "        \"\"\"\n",
    "        # reshape Q, K, V, attn_mask\n",
    "        batch_size = tf.shape(Q)[0]\n",
    "        Q_m = tf.transpose(tf.reshape(self.W_Q(Q), [batch_size, -1, self.n_head, self.d_head]), [0, 2, 1, 3])  # (bs, n_head, Q_len, d_head)\n",
    "        K_m = tf.transpose(tf.reshape(self.W_K(K), [batch_size, -1, self.n_head, self.d_head]), [0, 2, 1, 3])  # (bs, n_head, K_len, d_head)\n",
    "        V_m = tf.transpose(tf.reshape(self.W_V(V), [batch_size, -1, self.n_head, self.d_head]), [0, 2, 1, 3])  # (bs, n_head, K_len, d_head)\n",
    "        attn_mask_m = tf.expand_dims(attn_mask, axis=1)\n",
    "        # Scale Dot Product Attention with multi head Q, K, V, attn_mask\n",
    "        attn_out = self.attention(Q_m, K_m, V_m, attn_mask_m)  # (bs, n_head, Q_len, d_head)\n",
    "        # transpose and liner\n",
    "        attn_out_m = tf.transpose(attn_out, perm=[0, 2, 1, 3])  # (bs, Q_len, n_head, d_head)\n",
    "        attn_out = tf.reshape(attn_out_m, [batch_size, -1, config.n_head * config.d_head])  # (bs, Q_len, d_model)\n",
    "        attn_out = self.W_O(attn_out) # (bs, Q_len, d_model)\n",
    "\n",
    "        return attn_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "featured-relation",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionWiseFeedForward(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Position Wise Feed Forward Class\n",
    "    \"\"\"\n",
    "    def __init__(self, config, name=\"feed_forward\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param config: Config 객체\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "\n",
    "        self.W_1 = tf.keras.layers.Dense(config.d_ff, activation=gelu, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "        self.W_2 = tf.keras.layers.Dense(config.d_model, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "\n",
    "    def call(self, inputs):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param inputs: inputs\n",
    "        :return ff_val: feed forward 실행 결과\n",
    "        \"\"\"\n",
    "        ff_val = self.W_2(self.W_1(inputs))\n",
    "        return ff_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "widespread-chess",
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderLayer(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Encoder Layer Class\n",
    "    \"\"\"\n",
    "    def __init__(self, config, name=\"encoder_layer\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param config: Config 객체\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "\n",
    "        self.self_attention = MultiHeadAttention(config)\n",
    "        self.norm1 = tf.keras.layers.LayerNormalization(epsilon=config.layernorm_epsilon)\n",
    "\n",
    "        self.ffn = PositionWiseFeedForward(config)\n",
    "        self.norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layernorm_epsilon)\n",
    "\n",
    "        self.dropout = tf.keras.layers.Dropout(config.dropout)\n",
    " \n",
    "    def call(self, enc_embed, self_mask):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param enc_embed: enc_embed 또는 이전 EncoderLayer의 출력\n",
    "        :param self_mask: enc_tokens의 pad mask\n",
    "        :return enc_out: EncoderLayer 실행 결과\n",
    "        \"\"\"\n",
    "        self_attn_val = self.self_attention(enc_embed, enc_embed, enc_embed, self_mask)\n",
    "        norm1_val = self.norm1(enc_embed + self.dropout(self_attn_val))\n",
    "\n",
    "        ffn_val = self.ffn(norm1_val)\n",
    "        enc_out = self.norm2(norm1_val + self.dropout(ffn_val))\n",
    "\n",
    "        return enc_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "acoustic-bible",
   "metadata": {},
   "outputs": [],
   "source": [
    "#BERT 모델 구현\n",
    "\n",
    "class BERT(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    BERT Class\n",
    "    \"\"\"\n",
    "    def __init__(self, config, name=\"bert\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param config: Config 객체\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "\n",
    "        self.i_pad = config.i_pad\n",
    "        self.embedding = SharedEmbedding(config)\n",
    "        self.position = PositionalEmbedding(config)\n",
    "        self.segment = tf.keras.layers.Embedding(2, config.d_model, embeddings_initializer=kernel_initializer())\n",
    "        self.norm = tf.keras.layers.LayerNormalization(epsilon=config.layernorm_epsilon)\n",
    "        \n",
    "        self.encoder_layers = [EncoderLayer(config, name=f\"encoder_layer_{i}\") for i in range(config.n_layer)]\n",
    "\n",
    "        self.dropout = tf.keras.layers.Dropout(config.dropout)\n",
    "\n",
    "    def call(self, enc_tokens, segments):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param enc_tokens: encoder tokens\n",
    "        :param segments: token segments\n",
    "        :return logits_cls: CLS 결과 logits\n",
    "        :return logits_lm: LM 결과 logits\n",
    "        \"\"\"\n",
    "        enc_self_mask = get_pad_mask(enc_tokens, self.i_pad)\n",
    "\n",
    "        enc_embed = self.get_embedding(enc_tokens, segments)\n",
    "\n",
    "        enc_out = self.dropout(enc_embed)\n",
    "        for encoder_layer in self.encoder_layers:\n",
    "            enc_out = encoder_layer(enc_out, enc_self_mask)\n",
    "\n",
    "        logits_cls = enc_out[:,0]\n",
    "        logits_lm = enc_out\n",
    "        return logits_cls, logits_lm\n",
    "    \n",
    "    def get_embedding(self, tokens, segments):\n",
    "        \"\"\"\n",
    "        token embedding, position embedding lookup\n",
    "        :param tokens: 입력 tokens\n",
    "        :param segments: 입력 segments\n",
    "        :return embed: embedding 결과\n",
    "        \"\"\"\n",
    "        embed = self.embedding(tokens) + self.position(tokens) + self.segment(segments)\n",
    "        embed = self.norm(embed)\n",
    "        return embed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "guilty-password",
   "metadata": {},
   "source": [
    "### 3.1 Pretraining 없이 학습 진행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "common-fortune",
   "metadata": {},
   "outputs": [],
   "source": [
    "#BERT 레이어에 Fully Connected layer를 붙어 KorQuAD용으로 finetune하기 위한 모델 클래스\n",
    "#LSTM과 같은 Tokenizer로 구성\n",
    "\n",
    "class BERT4KorQuAD(tf.keras.Model):\n",
    "    def __init__(self, config):\n",
    "        super().__init__(name='BERT4KorQuAD')\n",
    "\n",
    "        self.bert = BERT(config)\n",
    "        self.dense = tf.keras.layers.Dense(2)\n",
    "    \n",
    "    def call(self, enc_tokens, segments):\n",
    "        logits_cls, logits_lm = self.bert(enc_tokens, segments)\n",
    "\n",
    "        hidden = self.dense(logits_lm) # (bs, n_seq, 2)\n",
    "        start_logits, end_logits = tf.split(hidden, 2, axis=-1)  # (bs, n_seq, 1), (bs, n_seq, 1)\n",
    "\n",
    "        start_logits = tf.squeeze(start_logits, axis=-1)\n",
    "        start_outputs = tf.keras.layers.Softmax(name=\"start\")(start_logits)\n",
    "\n",
    "        end_logits = tf.squeeze(end_logits, axis=-1)\n",
    "        end_outputs = tf.keras.layers.Softmax(name=\"end\")(end_logits)\n",
    "\n",
    "        return start_outputs, end_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "resistant-reform",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'d_model': 512,\n",
       " 'n_head': 8,\n",
       " 'd_head': 64,\n",
       " 'dropout': 0.1,\n",
       " 'd_ff': 1024,\n",
       " 'layernorm_epsilon': 0.001,\n",
       " 'n_layer': 6,\n",
       " 'n_seq': 384,\n",
       " 'n_vocab': 32007,\n",
       " 'i_pad': 0}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = Config({\"d_model\": 512, \"n_head\": 8, \"d_head\": 64, \"dropout\": 0.1, \"d_ff\": 1024, \"layernorm_epsilon\": 0.001, \"n_layer\": 6, \"n_seq\": 384, \"n_vocab\": 0, \"i_pad\": 0})\n",
    "config.n_vocab = len(vocab)\n",
    "config.i_pad = vocab.pad_id()\n",
    "\n",
    "#Config을 통해 실제로 우리가 사용할 모델 사이즈를 조정함.\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "sustained-warrant",
   "metadata": {},
   "outputs": [],
   "source": [
    "bert_batch_size = 32\n",
    "\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((train_inputs, train_labels)).shuffle(10000).batch(bert_batch_size)\n",
    "dev_dataset = tf.data.Dataset.from_tensor_slices((dev_inputs, dev_labels)).batch(bert_batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "major-spencer",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = BERT4KorQuAD(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "enclosed-lawsuit",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_epoch(model, dataset, loss_fn, acc_fn, optimizer):\n",
    "    metric_start_loss = tf.keras.metrics.Mean(name='start_loss')\n",
    "    metric_end_loss = tf.keras.metrics.Mean(name='end_loss')\n",
    "    metric_start_acc = tf.keras.metrics.Mean(name='start_acc')\n",
    "    metric_end_acc = tf.keras.metrics.Mean(name='end_acc')\n",
    "\n",
    "    p_bar = tqdm(dataset)\n",
    "    for batch, ((enc_tokens, segments), (start_labels, end_labels)) in enumerate(p_bar):\n",
    "        with tf.GradientTape() as tape:\n",
    "            start_outputs, end_outputs = model(enc_tokens, segments)\n",
    "\n",
    "            start_loss = loss_fn(start_labels, start_outputs)\n",
    "            end_loss = loss_fn(end_labels, end_outputs)\n",
    "            loss = start_loss + end_loss\n",
    "\n",
    "            start_acc = acc_fn(start_labels, start_outputs)\n",
    "            end_acc = acc_fn(end_labels, end_outputs)\n",
    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "        metric_start_loss(start_loss)\n",
    "        metric_end_loss(end_loss)\n",
    "        metric_start_acc(start_acc)\n",
    "        metric_end_acc(end_acc)\n",
    "        if batch % 10 == 9:\n",
    "            p_bar.set_description(f'loss: {metric_start_loss.result():0.4f}, {metric_end_loss.result():0.4f}, acc: {metric_start_acc.result():0.4f}, {metric_end_acc.result():0.4f}')\n",
    "    p_bar.close()\n",
    "\n",
    "    return metric_start_loss.result(), metric_end_loss.result(), metric_start_acc.result(), metric_end_acc.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "complete-house",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_epoch(model, dataset, loss_fn, acc_fn):\n",
    "    metric_start_loss = tf.keras.metrics.Mean(name='start_loss')\n",
    "    metric_end_loss = tf.keras.metrics.Mean(name='end_loss')\n",
    "    metric_start_acc = tf.keras.metrics.Mean(name='start_acc')\n",
    "    metric_end_acc = tf.keras.metrics.Mean(name='end_acc')\n",
    "\n",
    "    for batch, ((enc_tokens, segments), (start_labels, end_labels)) in enumerate(dataset):\n",
    "        start_outputs, end_outputs = model(enc_tokens, segments)\n",
    "\n",
    "        start_loss = loss_fn(start_labels, start_outputs)\n",
    "        end_loss = loss_fn(end_labels, end_outputs)\n",
    "\n",
    "        start_acc = acc_fn(start_labels, start_outputs)\n",
    "        end_acc = acc_fn(end_labels, end_outputs)\n",
    "\n",
    "        metric_start_loss(start_loss)\n",
    "        metric_end_loss(end_loss)\n",
    "        metric_start_acc(start_acc)\n",
    "        metric_end_acc(end_acc)\n",
    "\n",
    "    return metric_start_loss.result(), metric_end_loss.result(), metric_start_acc.result(), metric_end_acc.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "played-airline",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6496b638282748428bce901d9af5172c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1875 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 0 >> loss: 5.9489, 5.9428, acc: 0.0026, 0.0054, \n",
      "    Total loss: 11.8918, Total acc: 0.0081\n",
      "save best model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d53837eca1774cf28d96f5b342c32c69",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1875 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 1 >> loss: 5.9472, 5.9427, acc: 0.0047, 0.0060, \n",
      "    Total loss: 11.8899, Total acc: 0.0107\n",
      "save best model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1994b94b672f4a62a09bbecceee6020a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1875 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 2 >> loss: 5.9457, 5.9366, acc: 0.0058, 0.0046, \n",
      "    Total loss: 11.8823, Total acc: 0.0104\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c01fb7131818403f861f12491574cd59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1875 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 3 >> loss: 5.9475, 5.9370, acc: 0.0051, 0.0049, \n",
      "    Total loss: 11.8845, Total acc: 0.0100\n",
      "early stopping\n"
     ]
    }
   ],
   "source": [
    "loss_fn = tf.keras.losses.sparse_categorical_crossentropy\n",
    "acc_fn = tf.keras.metrics.sparse_categorical_accuracy\n",
    "\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=5e-4)\n",
    "\n",
    "best_acc = .0\n",
    "patience = 0\n",
    "\n",
    "train_loss_lst, train_acc_lst, val_loss_lst, val_acc_lst = [], [], [], []\n",
    "\n",
    "for epoch in range(20):\n",
    "    s_l, e_l, s_acc, e_acc = train_epoch(model, train_dataset, loss_fn, acc_fn, optimizer)\n",
    "    start_loss, end_loss, start_acc, end_acc = eval_epoch(model, dev_dataset, loss_fn, acc_fn)\n",
    "    print(f'eval {epoch} >> loss: {start_loss:0.4f}, {end_loss:0.4f}, acc: {start_acc:0.4f}, {end_acc:0.4f}, \\n    Total loss: {(start_loss+end_loss):0.4f}, Total acc: {(start_acc+end_acc):0.4f}')\n",
    "    train_loss = s_l + e_l\n",
    "    train_acc = s_acc + e_acc\n",
    "    val_acc = start_acc + end_acc\n",
    "    val_loss = start_loss + end_loss\n",
    "    \n",
    "    train_loss_lst.append(train_loss)\n",
    "    train_acc_lst.append(train_acc)\n",
    "    val_loss_lst.append(val_loss)\n",
    "    val_acc_lst.append(val_acc)\n",
    "    if best_acc < val_acc:\n",
    "        patience = 0\n",
    "        best_acc = val_acc\n",
    "        model.save_weights(os.path.join(data_dir, \"korquad_bert_none_pretrain.hdf5\"))\n",
    "        print(f'save best model')\n",
    "    else:\n",
    "        patience += 1\n",
    "    if 2 <= patience:\n",
    "        print(f'early stopping')\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "talented-medicine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#training result\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_loss_lst, 'b-', label='train_loss')\n",
    "plt.plot(val_loss_lst, 'r--', label='val_loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.title(f'Min valid loss : {min(val_loss_lst):.4f}', fontsize=15)\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_acc_lst, 'g-', label='train_accuracy')\n",
    "plt.plot(val_acc_lst, 'k--', label='val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.title(f'Max valid acc : {max(val_acc_lst):.4f}', fontsize=15)\n",
    "plt.legend()\n",
    "plt.savefig('./result1.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "internal-multimedia",
   "metadata": {},
   "source": [
    "### 3.1.1. Non pretrained model inference 수행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "protected-contribution",
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_predict(model, question, context):\n",
    "    \"\"\"\n",
    "    입력에 대한 답변 생성하는 함수\n",
    "    :param model: model\n",
    "    :param question: 입력 문자열\n",
    "    :param context: 입력 문자열\n",
    "    \"\"\"\n",
    "    q_tokens = vocab.encode_as_pieces(question)[:args.max_query_length]\n",
    "    c_tokens = vocab.encode_as_pieces(context)[:args.max_seq_length - len(q_tokens) - 3]\n",
    "    tokens = ['[CLS]'] + q_tokens + ['[SEP]'] + c_tokens + ['[SEP]']\n",
    "    token_ids = [vocab.piece_to_id(token) for token in tokens]\n",
    "    segments = [0] * (len(q_tokens) + 2) + [1] * (len(c_tokens) + 1)\n",
    "\n",
    "    y_start, y_end = model(np.array([token_ids]), np.array([segments]))\n",
    "    # print(y_start, y_end)\n",
    "    y_start_idx = K.argmax(y_start, axis=-1)[0].numpy()\n",
    "    y_end_idx = K.argmax(y_end, axis=-1)[0].numpy()\n",
    "    answer_tokens = tokens[y_start_idx:y_end_idx + 1]\n",
    "\n",
    "    return vocab.decode_pieces(answer_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "surprised-product",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [Answer Failed!!!]\n",
      "\n",
      "1\n",
      "[Question]  1989년 6월 30일 평양축전에 대표로 파견 된 인물은?\n",
      "[Context]  1989년 2월 15일 여의도 농민 폭력 시위를 주도한 혐의(폭력행위등처벌에관한법률위반)으로 지명수배되었다. 1989년 3월 12일 서울지방검찰청 공안부는 임종석의 사전구속영장을 발부받았다. 같은 해 6월 30일 평양축전에 임수경을 대표로 파견하여 국가보안법위반 혐의가 추가되었다. 경찰은 12월 18일~20일 사이 서울 경희대학교에서 임종석이 성명 발표를 추진하고 있다는 첩보를 입수했고, 12월 18일 오전 7시 40분 경 가스총과 전자봉으로 무장한 특공조 및 대공과 직원 12명 등 22명의 사복 경찰을 승용차 8대에 나누어 경희대학교에 투입했다. 1989년 12월 18일 오전 8시 15분 경 서울청량리경찰서는 호위 학생 5명과 함께 경희대학교 학생회관 건물 계단을 내려오는 임종석을 발견, 검거해 구속을 집행했다. 임종석은 청량리경찰서에서 약 1시간 동안 조사를 받은 뒤 오전 9시 50분 경 서울 장안동의 서울지방경찰청 공안분실로 인계되었다.\n",
      "[Answer]  임수경\n",
      "[Predict]  위반)으로 지명수배되었다. 1989년 3월 12일 서울지방검찰청 공안부는 임종석의 사전구속영장을 발부받았다. 같은 해 6월 30일 평양축전에 임수경을 대표로 파견하여 국가보안법위반 혐의가 추가되었다. 경찰은 12월 18일~20일 사이 서울 경희대학교에서 임종석이 성명 발표를 추진하고 있다는 첩보를 입수했고, 12월 18일 오전 7시 40분 경 가스총과 전자봉으로 무장한 특공조 및 대공과 직원 12명 등 22명의 사복 경찰을 승용차 8대에 나누어 경희대학교에 투입했다. 1989년 12월 18일 오전 8시 15분 경 서울청량리경찰서는 호위 학생 5명과 함께 경희대학교 학생회관 건물 계단을 내려오는 임종석을 발견, 검거해 구속을 집행했다. 임종석은 청량리경찰서에서 약 1시간 동안 조사를 받은 뒤 오전 9시 50분 경 서울 장안동의 \n",
      "\n",
      "2 [Answer Failed!!!]\n",
      "\n",
      "3 [Answer Failed!!!]\n",
      "\n",
      "4 [Answer Failed!!!]\n",
      "\n",
      "5 [Answer Failed!!!]\n",
      "\n",
      "6\n",
      "[Question]  임종석이 1989년 2월 15일에 지명수배 받은 혐의는 어떤 시위를 주도했다는 것인가?\n",
      "[Context]  1989년 2월 15일 여의도 농민 폭력 시위를 주도한 혐의(폭력행위등처벌에관한법률위반)으로 지명수배되었다. 1989년 3월 12일 서울지방검찰청 공안부는 임종석의 사전구속영장을 발부받았다. 같은 해 6월 30일 평양축전에 임수경을 대표로 파견하여 국가보안법위반 혐의가 추가되었다. 경찰은 12월 18일~20일 사이 서울 경희대학교에서 임종석이 성명 발표를 추진하고 있다는 첩보를 입수했고, 12월 18일 오전 7시 40분 경 가스총과 전자봉으로 무장한 특공조 및 대공과 직원 12명 등 22명의 사복 경찰을 승용차 8대에 나누어 경희대학교에 투입했다. 1989년 12월 18일 오전 8시 15분 경 서울청량리경찰서는 호위 학생 5명과 함께 경희대학교 학생회관 건물 계단을 내려오는 임종석을 발견, 검거해 구속을 집행했다. 임종석은 청량리경찰서에서 약 1시간 동안 조사를 받은 뒤 오전 9시 50분 경 서울 장안동의 서울지방경찰청 공안분실로 인계되었다.\n",
      "[Answer]  여의도 농민 폭력 시위\n",
      "[Predict]  여의도 농민 폭력 시위를 주도한 혐의(폭력행위등처벌에관한법률위반)으로 지명수배되었다. 1989년 3월 12일 서울지방검찰청 공안부는 임종 \n",
      "\n",
      "7 [Answer Failed!!!]\n",
      "\n",
      "8 [Answer Failed!!!]\n",
      "\n",
      "9 [Answer Failed!!!]\n",
      "\n",
      "10 [Answer Failed!!!]\n",
      "\n",
      "11 [Answer Failed!!!]\n",
      "\n",
      "[Total result] :  12  Questions, 10  Answers Failed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dev_json = os.path.join(data_dir, \"korquad_dev.json\")\n",
    "\n",
    "answer_fail_cnt = 0\n",
    "total_cnt = 0\n",
    "\n",
    "with open(dev_json) as f:\n",
    "    for i, line in enumerate(f):\n",
    "        data = json.loads(line)\n",
    "        question = vocab.decode_pieces(data['question'])\n",
    "        context = vocab.decode_pieces(data['context'])\n",
    "        answer = data['answer']\n",
    "        answer_predict = do_predict(model, question, context)\n",
    "        total_cnt += 1\n",
    "        \n",
    "        if answer in answer_predict:\n",
    "            print(i)\n",
    "            print(\"[Question] \", question)\n",
    "            print(\"[Context] \", context)\n",
    "            print(\"[Answer] \", answer)\n",
    "            print(\"[Predict] \", answer_predict, \"\\n\")\n",
    "        else :\n",
    "            print(i, \"[Answer Failed!!!]\\n\")\n",
    "            answer_fail_cnt += 1\n",
    "            \n",
    "        if 10 < i:\n",
    "            break\n",
    "            \n",
    "    print(\"[Total result] : \", total_cnt, \" Questions,\", answer_fail_cnt, \" Answers Failed\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chubby-inflation",
   "metadata": {},
   "source": [
    "### 3.2 Pretrained Model 활용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "complicated-snake",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"BERT4KorQuAD\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bert (BERT)                  multiple                  29202944  \n",
      "_________________________________________________________________\n",
      "dense_73 (Dense)             multiple                  1026      \n",
      "=================================================================\n",
      "Total params: 29,203,970\n",
      "Trainable params: 29,203,970\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#pretrained model load\n",
    "\n",
    "checkpoint_file = os.path.join(model_dir, 'bert_pretrain_32000.hdf5')\n",
    "\n",
    "model = BERT4KorQuAD(config)\n",
    "\n",
    "if os.path.exists(checkpoint_file):\n",
    "    #pretrained model 을 로드하기 위해 먼저 모델이 생성되어 있어야 함.\n",
    "    enc_tokens = np.random.randint(0, len(vocab), (4, 10))\n",
    "    segments = np.random.randint(0, 2, (4, 10))\n",
    "    model(enc_tokens, segments)\n",
    "    \n",
    "    #checkpoint 파일에서 필요한 layer를 불러옴. \n",
    "    model.load_weights(os.path.join(model_dir, \"bert_pretrain_32000.hdf5\"), by_name=True)\n",
    "\n",
    "    model.summary()\n",
    "else:\n",
    "    print('NO Pretrained Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "vital-freeware",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0fab6d26a7c64ba1a738c87f839da19f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1875 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 0 >> loss: 1.7861, 2.0486, acc: 0.5388, 0.5007, \n",
      "    Total loss: 3.8347, Total acc: 1.0395\n",
      "save best model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6da65eaffecf4553b29fc1cf60008575",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1875 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 1 >> loss: 1.7718, 1.9637, acc: 0.5569, 0.5255, \n",
      "    Total loss: 3.7356, Total acc: 1.0823\n",
      "save best model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "078af01cd50c4feaa949ca28ef9bafd6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1875 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 2 >> loss: 1.8602, 2.0961, acc: 0.5697, 0.5346, \n",
      "    Total loss: 3.9563, Total acc: 1.1043\n",
      "save best model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb608ac802364b7aa0f622297d2df640",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1875 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 3 >> loss: 1.9006, 2.3845, acc: 0.5392, 0.5074, \n",
      "    Total loss: 4.2851, Total acc: 1.0465\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "790b237ad2e34c2eb0009fb6a7347eb0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1875 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 4 >> loss: 2.3647, 2.5200, acc: 0.5437, 0.5060, \n",
      "    Total loss: 4.8847, Total acc: 1.0497\n",
      "early stopping\n"
     ]
    }
   ],
   "source": [
    "#pretrained model finetune\n",
    "\n",
    "loss_fn = tf.keras.losses.sparse_categorical_crossentropy\n",
    "acc_fn = tf.keras.metrics.sparse_categorical_accuracy\n",
    "\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=5e-4)\n",
    "\n",
    "best_acc = .0\n",
    "patience = 0\n",
    "\n",
    "train_loss_lst1, train_acc_lst1, val_loss_lst1, val_acc_lst1 = [], [], [], []\n",
    "\n",
    "for epoch in range(20):\n",
    "    s_l, e_l, s_acc, e_acc = train_epoch(model, train_dataset, loss_fn, acc_fn, optimizer)\n",
    "    start_loss, end_loss, start_acc, end_acc = eval_epoch(model, dev_dataset, loss_fn, acc_fn)\n",
    "    print(f'eval {epoch} >> loss: {start_loss:0.4f}, {end_loss:0.4f}, acc: {start_acc:0.4f}, {end_acc:0.4f}, \\n    Total loss: {(start_loss+end_loss):0.4f}, Total acc: {(start_acc+end_acc):0.4f}')\n",
    "    train_loss = s_l + e_l\n",
    "    train_acc = s_acc + e_acc\n",
    "    val_acc = start_acc + end_acc\n",
    "    val_loss = start_loss + end_loss\n",
    "    \n",
    "    train_loss_lst1.append(train_loss)\n",
    "    train_acc_lst1.append(train_acc)\n",
    "    val_loss_lst1.append(val_loss)\n",
    "    val_acc_lst1.append(val_acc)\n",
    "    if best_acc < val_acc:\n",
    "        patience = 0\n",
    "        best_acc = val_acc\n",
    "        model.save_weights(os.path.join(data_dir, \"korquad_bert_none_pretrain.hdf5\"))\n",
    "        print(f'save best model')\n",
    "    else:\n",
    "        patience += 1\n",
    "    if 2 <= patience:\n",
    "        print(f'early stopping')\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "corporate-garbage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training result\n",
    "plt.figure(figsize=(16, 8))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_loss_lst1, 'b-', label='train_loss')\n",
    "plt.plot(val_loss_lst1, 'r--', label='val_loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.title(f'Min valid loss : {min(val_loss_lst):.4f}', fontsize=15)\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_acc_lst1, 'g-', label='train_accuracy')\n",
    "plt.plot(val_acc_lst1, 'k--', label='val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.title(f'Max valid acc : {max(val_acc_lst):.4f}', fontsize=15)\n",
    "plt.legend()\n",
    "plt.savefig('./result1.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electrical-charm",
   "metadata": {},
   "source": [
    "### 3.2.1. Inferfence 수행\n",
    "- finetune 학습이 완료된 model 활용해 퀴즈 풀이 결과 확인."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "atlantic-colors",
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_predict(model, question, context):\n",
    "    \"\"\"\n",
    "    입력에 대한 답변 생성하는 함수\n",
    "    :param model: model\n",
    "    :param question: 입력 문자열\n",
    "    :param context: 입력 문자열\n",
    "    \"\"\"\n",
    "    q_tokens = vocab.encode_as_pieces(question)[:args.max_query_length]\n",
    "    c_tokens = vocab.encode_as_pieces(context)[:args.max_seq_length - len(q_tokens) - 3]\n",
    "    tokens = ['[CLS]'] + q_tokens + ['[SEP]'] + c_tokens + ['[SEP]']\n",
    "    token_ids = [vocab.piece_to_id(token) for token in tokens]\n",
    "    segments = [0] * (len(q_tokens) + 2) + [1] * (len(c_tokens) + 1)\n",
    "\n",
    "    y_start, y_end = model(np.array([token_ids]), np.array([segments]))\n",
    "    # print(y_start, y_end)\n",
    "    y_start_idx = K.argmax(y_start, axis=-1)[0].numpy()\n",
    "    y_end_idx = K.argmax(y_end, axis=-1)[0].numpy()\n",
    "    answer_tokens = tokens[y_start_idx:y_end_idx + 1]\n",
    "\n",
    "    return vocab.decode_pieces(answer_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "polyphonic-domestic",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "[Question]  임종석이 여의도 농민 폭력 시위를 주도한 혐의로 지명수배 된 날은?\n",
      "[Context]  1989년 2월 15일 여의도 농민 폭력 시위를 주도한 혐의(폭력행위등처벌에관한법률위반)으로 지명수배되었다. 1989년 3월 12일 서울지방검찰청 공안부는 임종석의 사전구속영장을 발부받았다. 같은 해 6월 30일 평양축전에 임수경을 대표로 파견하여 국가보안법위반 혐의가 추가되었다. 경찰은 12월 18일~20일 사이 서울 경희대학교에서 임종석이 성명 발표를 추진하고 있다는 첩보를 입수했고, 12월 18일 오전 7시 40분 경 가스총과 전자봉으로 무장한 특공조 및 대공과 직원 12명 등 22명의 사복 경찰을 승용차 8대에 나누어 경희대학교에 투입했다. 1989년 12월 18일 오전 8시 15분 경 서울청량리경찰서는 호위 학생 5명과 함께 경희대학교 학생회관 건물 계단을 내려오는 임종석을 발견, 검거해 구속을 집행했다. 임종석은 청량리경찰서에서 약 1시간 동안 조사를 받은 뒤 오전 9시 50분 경 서울 장안동의 서울지방경찰청 공안분실로 인계되었다.\n",
      "[Answer]  1989년 2월 15일\n",
      "[Predict]  1989년 2월 15일 \n",
      "\n",
      "1\n",
      "[Question]  1989년 6월 30일 평양축전에 대표로 파견 된 인물은?\n",
      "[Context]  1989년 2월 15일 여의도 농민 폭력 시위를 주도한 혐의(폭력행위등처벌에관한법률위반)으로 지명수배되었다. 1989년 3월 12일 서울지방검찰청 공안부는 임종석의 사전구속영장을 발부받았다. 같은 해 6월 30일 평양축전에 임수경을 대표로 파견하여 국가보안법위반 혐의가 추가되었다. 경찰은 12월 18일~20일 사이 서울 경희대학교에서 임종석이 성명 발표를 추진하고 있다는 첩보를 입수했고, 12월 18일 오전 7시 40분 경 가스총과 전자봉으로 무장한 특공조 및 대공과 직원 12명 등 22명의 사복 경찰을 승용차 8대에 나누어 경희대학교에 투입했다. 1989년 12월 18일 오전 8시 15분 경 서울청량리경찰서는 호위 학생 5명과 함께 경희대학교 학생회관 건물 계단을 내려오는 임종석을 발견, 검거해 구속을 집행했다. 임종석은 청량리경찰서에서 약 1시간 동안 조사를 받은 뒤 오전 9시 50분 경 서울 장안동의 서울지방경찰청 공안분실로 인계되었다.\n",
      "[Answer]  임수경\n",
      "[Predict]  임수경을 \n",
      "\n",
      "2\n",
      "[Question]  임종석이 여의도 농민 폭력 시위를 주도한 혐의로 지명수배된 연도는?\n",
      "[Context]  1989년 2월 15일 여의도 농민 폭력 시위를 주도한 혐의(폭력행위등처벌에관한법률위반)으로 지명수배되었다. 1989년 3월 12일 서울지방검찰청 공안부는 임종석의 사전구속영장을 발부받았다. 같은 해 6월 30일 평양축전에 임수경을 대표로 파견하여 국가보안법위반 혐의가 추가되었다. 경찰은 12월 18일~20일 사이 서울 경희대학교에서 임종석이 성명 발표를 추진하고 있다는 첩보를 입수했고, 12월 18일 오전 7시 40분 경 가스총과 전자봉으로 무장한 특공조 및 대공과 직원 12명 등 22명의 사복 경찰을 승용차 8대에 나누어 경희대학교에 투입했다. 1989년 12월 18일 오전 8시 15분 경 서울청량리경찰서는 호위 학생 5명과 함께 경희대학교 학생회관 건물 계단을 내려오는 임종석을 발견, 검거해 구속을 집행했다. 임종석은 청량리경찰서에서 약 1시간 동안 조사를 받은 뒤 오전 9시 50분 경 서울 장안동의 서울지방경찰청 공안분실로 인계되었다.\n",
      "[Answer]  1989년\n",
      "[Predict]  1989년 \n",
      "\n",
      "3 [Answer Failed!!!]\n",
      "\n",
      "4\n",
      "[Question]  임종석이 조사를 받은 뒤 인계된 곳은 어딘가?\n",
      "[Context]  1989년 2월 15일 여의도 농민 폭력 시위를 주도한 혐의(폭력행위등처벌에관한법률위반)으로 지명수배되었다. 1989년 3월 12일 서울지방검찰청 공안부는 임종석의 사전구속영장을 발부받았다. 같은 해 6월 30일 평양축전에 임수경을 대표로 파견하여 국가보안법위반 혐의가 추가되었다. 경찰은 12월 18일~20일 사이 서울 경희대학교에서 임종석이 성명 발표를 추진하고 있다는 첩보를 입수했고, 12월 18일 오전 7시 40분 경 가스총과 전자봉으로 무장한 특공조 및 대공과 직원 12명 등 22명의 사복 경찰을 승용차 8대에 나누어 경희대학교에 투입했다. 1989년 12월 18일 오전 8시 15분 경 서울청량리경찰서는 호위 학생 5명과 함께 경희대학교 학생회관 건물 계단을 내려오는 임종석을 발견, 검거해 구속을 집행했다. 임종석은 청량리경찰서에서 약 1시간 동안 조사를 받은 뒤 오전 9시 50분 경 서울 장안동의 서울지방경찰청 공안분실로 인계되었다.\n",
      "[Answer]  서울지방경찰청 공안분실\n",
      "[Predict]  서울지방경찰청 공안분실로 \n",
      "\n",
      "5 [Answer Failed!!!]\n",
      "\n",
      "6 [Answer Failed!!!]\n",
      "\n",
      "7\n",
      "[Question]  정부의 헌법개정안 준비 과정에 대해서 청와대 비서실이 아니라 국무회의 중심으로 이뤄졌어야 했다고 지적한 원로 헌법학자는?\n",
      "[Context]  \"내각과 장관들이 소외되고 대통령비서실의 권한이 너무 크다\", \"행보가 비서 본연의 역할을 벗어난다\"는 의견이 제기되었다. 대표적인 예가 10차 개헌안 발표이다. 원로 헌법학자인 허영 경희대 석좌교수는 정부의 헌법개정안 준비 과정에 대해 \"청와대 비서실이 아닌 국무회의 중심으로 이뤄졌어야 했다\"고 지적했다. '국무회의의 심의를 거쳐야 한다'(제89조)는 헌법 규정에 충실하지 않았다는 것이다. 그러면서 \"법무부 장관을 제쳐놓고 민정수석이 개정안을 설명하는 게 이해가 안 된다\"고 지적했다. 민정수석은 국회의원에 대해 책임지는 법무부 장관도 아니고, 국민에 대해 책임지는 사람도 아니기 때문에 정당성이 없고, 단지 대통령의 신임이 있을 뿐이라는 것이다. 또한 국무총리 선출 방식에 대한 기자의 질문에 \"문 대통령도 취임 전에 국무총리에게 실질적 권한을 주겠다고 했지만 그러지 못하고 있다. 대통령비서실장만도 못한 권한을 행사하고 있다.\"고 답변했다.\n",
      "[Answer]  허영\n",
      "[Predict]  허영 경희대 석좌교수 \n",
      "\n",
      "8 [Answer Failed!!!]\n",
      "\n",
      "9 [Answer Failed!!!]\n",
      "\n",
      "10 [Answer Failed!!!]\n",
      "\n",
      "11\n",
      "[Question]  미국 군대 내 두번째로 높은 직위는 무엇인가?\n",
      "[Context]  알렉산더 메이그스 헤이그 2세(영어: Alexander Meigs Haig, Jr., 1924년 12월 2일 ~ 2010년 2월 20일)는 미국의 국무 장관을 지낸 미국의 군인, 관료 및 정치인이다. 로널드 레이건 대통령 밑에서 국무장관을 지냈으며, 리처드 닉슨과 제럴드 포드 대통령 밑에서 백악관 비서실장을 지냈다. 또한 그는 미국 군대에서 2번째로 높은 직위인 미국 육군 부참모 총장과 나토 및 미국 군대의 유럽연합군 최고사령관이었다. 한국 전쟁 시절 더글러스 맥아더 유엔군 사령관의 참모로 직접 참전하였으며, 로널드 레이건 정부 출범당시 초대 국무장관직을 맡아 1980년대 대한민국과 미국의 관계를 조율해 왔다. 저서로 회고록 《경고:현실주의, 레이건과 외교 정책》(1984년 발간)이 있다.\n",
      "[Answer]  미국 육군 부참모 총장\n",
      "[Predict]  미국 육군 부참모 총장 \n",
      "\n",
      "12\n",
      "[Question]  로널드 레이건 정부 출범 당시 알렉산더 헤이그는 어떤 직책을 맡았는가?\n",
      "[Context]  알렉산더 메이그스 헤이그 2세(영어: Alexander Meigs Haig, Jr., 1924년 12월 2일 ~ 2010년 2월 20일)는 미국의 국무 장관을 지낸 미국의 군인, 관료 및 정치인이다. 로널드 레이건 대통령 밑에서 국무장관을 지냈으며, 리처드 닉슨과 제럴드 포드 대통령 밑에서 백악관 비서실장을 지냈다. 또한 그는 미국 군대에서 2번째로 높은 직위인 미국 육군 부참모 총장과 나토 및 미국 군대의 유럽연합군 최고사령관이었다. 한국 전쟁 시절 더글러스 맥아더 유엔군 사령관의 참모로 직접 참전하였으며, 로널드 레이건 정부 출범당시 초대 국무장관직을 맡아 1980년대 대한민국과 미국의 관계를 조율해 왔다. 저서로 회고록 《경고:현실주의, 레이건과 외교 정책》(1984년 발간)이 있다.\n",
      "[Answer]  초대 국무장관직\n",
      "[Predict]  초대 국무장관직을 \n",
      "\n",
      "13\n",
      "[Question]  알렉산더 헤이그는 어느 대통령의 밑에서 국무장관을 지냈는가?\n",
      "[Context]  알렉산더 메이그스 헤이그 2세(영어: Alexander Meigs Haig, Jr., 1924년 12월 2일 ~ 2010년 2월 20일)는 미국의 국무 장관을 지낸 미국의 군인, 관료 및 정치인이다. 로널드 레이건 대통령 밑에서 국무장관을 지냈으며, 리처드 닉슨과 제럴드 포드 대통령 밑에서 백악관 비서실장을 지냈다. 또한 그는 미국 군대에서 2번째로 높은 직위인 미국 육군 부참모 총장과 나토 및 미국 군대의 유럽연합군 최고사령관이었다. 한국 전쟁 시절 더글러스 맥아더 유엔군 사령관의 참모로 직접 참전하였으며, 로널드 레이건 정부 출범당시 초대 국무장관직을 맡아 1980년대 대한민국과 미국의 관계를 조율해 왔다. 저서로 회고록 《경고:현실주의, 레이건과 외교 정책》(1984년 발간)이 있다.\n",
      "[Answer]  로널드 레이건 대통령\n",
      "[Predict]  로널드 레이건 대통령 \n",
      "\n",
      "14 [Answer Failed!!!]\n",
      "\n",
      "15 [Answer Failed!!!]\n",
      "\n",
      "16\n",
      "[Question]  알렉산더 메이그스 헤이그의 생년월일은?\n",
      "[Context]  알렉산더 메이그스 헤이그 2세(영어: Alexander Meigs Haig, Jr., 1924년 12월 2일 ~ 2010년 2월 20일)는 미국의 국무 장관을 지낸 미국의 군인, 관료 및 정치인이다. 로널드 레이건 대통령 밑에서 국무장관을 지냈으며, 리처드 닉슨과 제럴드 포드 대통령 밑에서 백악관 비서실장을 지냈다. 또한 그는 미국 군대에서 2번째로 높은 직위인 미국 육군 부참모 총장과 나토 및 미국 군대의 유럽연합군 최고사령관이었다. 한국 전쟁 시절 더글러스 맥아더 유엔군 사령관의 참모로 직접 참전하였으며, 로널드 레이건 정부 출범당시 초대 국무장관직을 맡아 1980년대 대한민국과 미국의 관계를 조율해 왔다. 저서로 회고록 《경고:현실주의, 레이건과 외교 정책》(1984년 발간)이 있다.\n",
      "[Answer]  1924년 12월 2일\n",
      "[Predict]  1924년 12월 2일 \n",
      "\n",
      "17\n",
      "[Question]  알렉산더 헤이그가 로널드 레이건 대통령 밑에서 맡은 직책은 무엇이었나?\n",
      "[Context]  알렉산더 메이그스 헤이그 2세(영어: Alexander Meigs Haig, Jr., 1924년 12월 2일 ~ 2010년 2월 20일)는 미국의 국무 장관을 지낸 미국의 군인, 관료 및 정치인이다. 로널드 레이건 대통령 밑에서 국무장관을 지냈으며, 리처드 닉슨과 제럴드 포드 대통령 밑에서 백악관 비서실장을 지냈다. 또한 그는 미국 군대에서 2번째로 높은 직위인 미국 육군 부참모 총장과 나토 및 미국 군대의 유럽연합군 최고사령관이었다. 한국 전쟁 시절 더글러스 맥아더 유엔군 사령관의 참모로 직접 참전하였으며, 로널드 레이건 정부 출범당시 초대 국무장관직을 맡아 1980년대 대한민국과 미국의 관계를 조율해 왔다. 저서로 회고록 《경고:현실주의, 레이건과 외교 정책》(1984년 발간)이 있다.\n",
      "[Answer]  국무장관\n",
      "[Predict]  국무장관을 지냈으며, 리처드 닉슨과 제럴드 포드 대통령 \n",
      "\n",
      "18\n",
      "[Question]  알렉산더 헤이그가 1984년 발간한 회고록의 제목은 무엇인가?\n",
      "[Context]  알렉산더 메이그스 헤이그 2세(영어: Alexander Meigs Haig, Jr., 1924년 12월 2일 ~ 2010년 2월 20일)는 미국의 국무 장관을 지낸 미국의 군인, 관료 및 정치인이다. 로널드 레이건 대통령 밑에서 국무장관을 지냈으며, 리처드 닉슨과 제럴드 포드 대통령 밑에서 백악관 비서실장을 지냈다. 또한 그는 미국 군대에서 2번째로 높은 직위인 미국 육군 부참모 총장과 나토 및 미국 군대의 유럽연합군 최고사령관이었다. 한국 전쟁 시절 더글러스 맥아더 유엔군 사령관의 참모로 직접 참전하였으며, 로널드 레이건 정부 출범당시 초대 국무장관직을 맡아 1980년대 대한민국과 미국의 관계를 조율해 왔다. 저서로 회고록 《경고:현실주의, 레이건과 외교 정책》(1984년 발간)이 있다.\n",
      "[Answer]  경고:현실주의, 레이건과 외교 정책\n",
      "[Predict]  《경고:현실주의, 레이건과 외교 정책》(1984년 발간)이 \n",
      "\n",
      "19 [Answer Failed!!!]\n",
      "\n",
      "20\n",
      "[Question]  알렉산더 헤이그가 미국 육군사관학교로 임명받은 해는 언제인가?\n",
      "[Context]  노터데임 대학교에서 2년간 합리적으로 심각한 공부를 한 후 헤이그는 1944년 미국 육군사관학교로 임명을 획득하여 자신의 어린 시절을 군사 경력의 야망으로 알아챘다. 그 경력은 헤이그의 학문적 경연이 암시하려고 한것보다 더욱 극적이었으며 그는 1947년 310의 동기병에서 217번째 사관으로서 졸업하였다. 22세의 소위로 헤이그는 처음에 캔자스 주 포트라일리에서 정통 제병 연합부대로, 그러고나서 켄터키 주 포트녹스에 있는 기갑 훈련소로 갔다. 그후에 그는 제1 기병 사단으로 선임되고 그러고나서 일본에서 점령군의 임무와 기력이 없는 훈련을 하였다. 그는 1950년 5월 한번 자신의 사령관 알론조 폭스 장군의 딸 퍼트리샤 앤토이넷 폭스와 결혼하여 슬하 3명의 자식을 두었다.\n",
      "[Answer]  1944년\n",
      "[Predict]  1944년 \n",
      "\n",
      "21\n",
      "[Question]  알렉산더 헤이그는 퍼트리샤 앤토이넷 폭스와 결혼해 몇 명의 자녀를 두었는가?\n",
      "[Context]  노터데임 대학교에서 2년간 합리적으로 심각한 공부를 한 후 헤이그는 1944년 미국 육군사관학교로 임명을 획득하여 자신의 어린 시절을 군사 경력의 야망으로 알아챘다. 그 경력은 헤이그의 학문적 경연이 암시하려고 한것보다 더욱 극적이었으며 그는 1947년 310의 동기병에서 217번째 사관으로서 졸업하였다. 22세의 소위로 헤이그는 처음에 캔자스 주 포트라일리에서 정통 제병 연합부대로, 그러고나서 켄터키 주 포트녹스에 있는 기갑 훈련소로 갔다. 그후에 그는 제1 기병 사단으로 선임되고 그러고나서 일본에서 점령군의 임무와 기력이 없는 훈련을 하였다. 그는 1950년 5월 한번 자신의 사령관 알론조 폭스 장군의 딸 퍼트리샤 앤토이넷 폭스와 결혼하여 슬하 3명의 자식을 두었다.\n",
      "[Answer]  3명\n",
      "[Predict]  3명의 \n",
      "\n",
      "22 [Answer Failed!!!]\n",
      "\n",
      "23 [Answer Failed!!!]\n",
      "\n",
      "24 [Answer Failed!!!]\n",
      "\n",
      "25 [Answer Failed!!!]\n",
      "\n",
      "26 [Answer Failed!!!]\n",
      "\n",
      "27\n",
      "[Question]  헤이그가 군에서 퇴역한 해는 언제인가?\n",
      "[Context]  헤이그는 닉슨 대통령이 그를 사성 장군과 육군 부참모로 진급시킬 때 집중 광선과 논쟁으로 들어갔다. 헤이그를 군사의 최상으로 밀어넣은 닉슨의 행동은 대통령의 남자들을 다양한 연방 대리법에서 권한의 직우들로 놓은 노력과 함께 일치였다. 하지만 그는 곧 백악관으로 돌아가 1973년부터 1974년까지 대통령 특별 보좌관을 지냈다. 워터게이트 사건이 일어난지 한달 후, 헤이그는 포위된 닉슨 대통령을 위한 치명적 역할을 하였다. 그일은 8월 닉슨의 사임과 제럴드 포드의 대통령으로 계승으로 이끈 협상들에서 헤이그가 수단이었던 우연이 아니었다. 곧 후에 헤이그는 미국 유럽 연합군 최고사령부의 최고 사령관으로 임명되었다. 그는 나토에서 다음 5년을 보내고 1979년 군에서 퇴역하여 미국 기술 주식 회사의 우두머리가 되었다.\n",
      "[Answer]  1979년\n",
      "[Predict]  5년을 보내고 1979년 \n",
      "\n",
      "28 [Answer Failed!!!]\n",
      "\n",
      "29\n",
      "[Question]  헤이그는 나토에서 얼마나 있었습니까?\n",
      "[Context]  헤이그는 닉슨 대통령이 그를 사성 장군과 육군 부참모로 진급시킬 때 집중 광선과 논쟁으로 들어갔다. 헤이그를 군사의 최상으로 밀어넣은 닉슨의 행동은 대통령의 남자들을 다양한 연방 대리법에서 권한의 직우들로 놓은 노력과 함께 일치였다. 하지만 그는 곧 백악관으로 돌아가 1973년부터 1974년까지 대통령 특별 보좌관을 지냈다. 워터게이트 사건이 일어난지 한달 후, 헤이그는 포위된 닉슨 대통령을 위한 치명적 역할을 하였다. 그일은 8월 닉슨의 사임과 제럴드 포드의 대통령으로 계승으로 이끈 협상들에서 헤이그가 수단이었던 우연이 아니었다. 곧 후에 헤이그는 미국 유럽 연합군 최고사령부의 최고 사령관으로 임명되었다. 그는 나토에서 다음 5년을 보내고 1979년 군에서 퇴역하여 미국 기술 주식 회사의 우두머리가 되었다.\n",
      "[Answer]  5년\n",
      "[Predict]  5년 \n",
      "\n",
      "30\n",
      "[Question]  헤이그가 군에서 퇴역한 년도는 몇년도입니까?\n",
      "[Context]  헤이그는 닉슨 대통령이 그를 사성 장군과 육군 부참모로 진급시킬 때 집중 광선과 논쟁으로 들어갔다. 헤이그를 군사의 최상으로 밀어넣은 닉슨의 행동은 대통령의 남자들을 다양한 연방 대리법에서 권한의 직우들로 놓은 노력과 함께 일치였다. 하지만 그는 곧 백악관으로 돌아가 1973년부터 1974년까지 대통령 특별 보좌관을 지냈다. 워터게이트 사건이 일어난지 한달 후, 헤이그는 포위된 닉슨 대통령을 위한 치명적 역할을 하였다. 그일은 8월 닉슨의 사임과 제럴드 포드의 대통령으로 계승으로 이끈 협상들에서 헤이그가 수단이었던 우연이 아니었다. 곧 후에 헤이그는 미국 유럽 연합군 최고사령부의 최고 사령관으로 임명되었다. 그는 나토에서 다음 5년을 보내고 1979년 군에서 퇴역하여 미국 기술 주식 회사의 우두머리가 되었다.\n",
      "[Answer]  1979년\n",
      "[Predict]  1979년 \n",
      "\n",
      "31\n",
      "[Question]  제럴드 포드 대통령 시기 헤이그가 최고사령부의 최고 사령관으로 임명된 곳은 어디인가?\n",
      "[Context]  헤이그는 닉슨 대통령이 그를 사성 장군과 육군 부참모로 진급시킬 때 집중 광선과 논쟁으로 들어갔다. 헤이그를 군사의 최상으로 밀어넣은 닉슨의 행동은 대통령의 남자들을 다양한 연방 대리법에서 권한의 직우들로 놓은 노력과 함께 일치였다. 하지만 그는 곧 백악관으로 돌아가 1973년부터 1974년까지 대통령 특별 보좌관을 지냈다. 워터게이트 사건이 일어난지 한달 후, 헤이그는 포위된 닉슨 대통령을 위한 치명적 역할을 하였다. 그일은 8월 닉슨의 사임과 제럴드 포드의 대통령으로 계승으로 이끈 협상들에서 헤이그가 수단이었던 우연이 아니었다. 곧 후에 헤이그는 미국 유럽 연합군 최고사령부의 최고 사령관으로 임명되었다. 그는 나토에서 다음 5년을 보내고 1979년 군에서 퇴역하여 미국 기술 주식 회사의 우두머리가 되었다.\n",
      "[Answer]  미국 유럽 연합군\n",
      "[Predict]  미국 유럽 연합군 최고사령부 \n",
      "\n",
      "32\n",
      "[Question]  헤이그가 군대에서 퇴역한 년도는?\n",
      "[Context]  헤이그는 닉슨 대통령이 그를 사성 장군과 육군 부참모로 진급시킬 때 집중 광선과 논쟁으로 들어갔다. 헤이그를 군사의 최상으로 밀어넣은 닉슨의 행동은 대통령의 남자들을 다양한 연방 대리법에서 권한의 직우들로 놓은 노력과 함께 일치였다. 하지만 그는 곧 백악관으로 돌아가 1973년부터 1974년까지 대통령 특별 보좌관을 지냈다. 워터게이트 사건이 일어난지 한달 후, 헤이그는 포위된 닉슨 대통령을 위한 치명적 역할을 하였다. 그일은 8월 닉슨의 사임과 제럴드 포드의 대통령으로 계승으로 이끈 협상들에서 헤이그가 수단이었던 우연이 아니었다. 곧 후에 헤이그는 미국 유럽 연합군 최고사령부의 최고 사령관으로 임명되었다. 그는 나토에서 다음 5년을 보내고 1979년 군에서 퇴역하여 미국 기술 주식 회사의 우두머리가 되었다.\n",
      "[Answer]  1979년\n",
      "[Predict]  1979년 \n",
      "\n",
      "33 [Answer Failed!!!]\n",
      "\n",
      "34\n",
      "[Question]  알렉산더 헤이그는 레이건의 조언자들을 무엇이라고 묘사하였는가?\n",
      "[Context]  그의 편에 헤이그는 지구촌의 논점들의 국내적 정치 노력들에 관해서만 근심한 레이건의 가까운 조언자들을 \"외교 정책의 아마추어\"로 묘사하였다. 1982년 6월 25일 결국적으로 온 그의 국무장관으로서 사임은 불가능한 상황이 된 것을 끝냈다. 헤이그는 개인적 생활로 돌아갔다가 1988년 대통령 선거를 위한 공화당 후보직을 안정시키는 시도를 하는 데 충분하게 정계로 돌아갔으나 후보직을 이기는 데 성원을 가지지 않았다. 그는 외교 정책 논쟁들에 연설자로서 활동적으로 남아있었으나 그의 전념은 정치에서 개인적 생활로 옮겨졌다. 그는 Worldwide Associates Inc.의 국제적 상담 회사에 의하여 기용되었고, 그 기구의 의장과 회장이 되었다.\n",
      "[Answer]  외교 정책의 아마추어\n",
      "[Predict]  \"외교 정책의 아마추어\"로 \n",
      "\n",
      "35\n",
      "[Question]  헤이그가 사적생활을 하다가 정계로 돌아갔던 해는 언제인가?\n",
      "[Context]  그의 편에 헤이그는 지구촌의 논점들의 국내적 정치 노력들에 관해서만 근심한 레이건의 가까운 조언자들을 \"외교 정책의 아마추어\"로 묘사하였다. 1982년 6월 25일 결국적으로 온 그의 국무장관으로서 사임은 불가능한 상황이 된 것을 끝냈다. 헤이그는 개인적 생활로 돌아갔다가 1988년 대통령 선거를 위한 공화당 후보직을 안정시키는 시도를 하는 데 충분하게 정계로 돌아갔으나 후보직을 이기는 데 성원을 가지지 않았다. 그는 외교 정책 논쟁들에 연설자로서 활동적으로 남아있었으나 그의 전념은 정치에서 개인적 생활로 옮겨졌다. 그는 Worldwide Associates Inc.의 국제적 상담 회사에 의하여 기용되었고, 그 기구의 의장과 회장이 되었다.\n",
      "[Answer]  1988년\n",
      "[Predict]  1988년 \n",
      "\n",
      "36 [Answer Failed!!!]\n",
      "\n",
      "37\n",
      "[Question]  헤이그가 정계로 돌아간 년도는 몇년도입니까?\n",
      "[Context]  그의 편에 헤이그는 지구촌의 논점들의 국내적 정치 노력들에 관해서만 근심한 레이건의 가까운 조언자들을 \"외교 정책의 아마추어\"로 묘사하였다. 1982년 6월 25일 결국적으로 온 그의 국무장관으로서 사임은 불가능한 상황이 된 것을 끝냈다. 헤이그는 개인적 생활로 돌아갔다가 1988년 대통령 선거를 위한 공화당 후보직을 안정시키는 시도를 하는 데 충분하게 정계로 돌아갔으나 후보직을 이기는 데 성원을 가지지 않았다. 그는 외교 정책 논쟁들에 연설자로서 활동적으로 남아있었으나 그의 전념은 정치에서 개인적 생활로 옮겨졌다. 그는 Worldwide Associates Inc.의 국제적 상담 회사에 의하여 기용되었고, 그 기구의 의장과 회장이 되었다.\n",
      "[Answer]  1988년\n",
      "[Predict]  1988년 \n",
      "\n",
      "38\n",
      "[Question]  레이건 대통령의 조언자들을 헤이그는 무엇이라고 묘사하였나?\n",
      "[Context]  그의 편에 헤이그는 지구촌의 논점들의 국내적 정치 노력들에 관해서만 근심한 레이건의 가까운 조언자들을 \"외교 정책의 아마추어\"로 묘사하였다. 1982년 6월 25일 결국적으로 온 그의 국무장관으로서 사임은 불가능한 상황이 된 것을 끝냈다. 헤이그는 개인적 생활로 돌아갔다가 1988년 대통령 선거를 위한 공화당 후보직을 안정시키는 시도를 하는 데 충분하게 정계로 돌아갔으나 후보직을 이기는 데 성원을 가지지 않았다. 그는 외교 정책 논쟁들에 연설자로서 활동적으로 남아있었으나 그의 전념은 정치에서 개인적 생활로 옮겨졌다. 그는 Worldwide Associates Inc.의 국제적 상담 회사에 의하여 기용되었고, 그 기구의 의장과 회장이 되었다.\n",
      "[Answer]  외교 정책의 아마추어\n",
      "[Predict]  \"외교 정책의 아마추어\"로 \n",
      "\n",
      "39\n",
      "[Question]  헤이그가 정계로 다시 돌아간 년도는?\n",
      "[Context]  그의 편에 헤이그는 지구촌의 논점들의 국내적 정치 노력들에 관해서만 근심한 레이건의 가까운 조언자들을 \"외교 정책의 아마추어\"로 묘사하였다. 1982년 6월 25일 결국적으로 온 그의 국무장관으로서 사임은 불가능한 상황이 된 것을 끝냈다. 헤이그는 개인적 생활로 돌아갔다가 1988년 대통령 선거를 위한 공화당 후보직을 안정시키는 시도를 하는 데 충분하게 정계로 돌아갔으나 후보직을 이기는 데 성원을 가지지 않았다. 그는 외교 정책 논쟁들에 연설자로서 활동적으로 남아있었으나 그의 전념은 정치에서 개인적 생활로 옮겨졌다. 그는 Worldwide Associates Inc.의 국제적 상담 회사에 의하여 기용되었고, 그 기구의 의장과 회장이 되었다.\n",
      "[Answer]  1988년\n",
      "[Predict]  1988년 \n",
      "\n",
      "40\n",
      "[Question]  하나님의 명령에 배를 만들고 가족과 짐승들을 배에 태워 홍수를 피한 사람은 누구인가?\n",
      "[Context]  노아는 하나님의 명령에 따라 배를 만들고 가족과 정결한 짐승 암수 일곱 마리씩, 부정한 짐승 암수 한 마리씩(혹은 두 마리씩; 사본에 따라 다름), 그리고 새 암수 일곱 마리씩을 싣고 밀어닥친 홍수를 피하였다. 모든 사람들이 타락한 생활에 빠져 있어 하나님이 홍수로 심판하려 할 때 홀로 바르게 살던 노아는 하나님의 특별한 계시로 홍수가 올 것을 미리 알게 된다. 그는 길이 300 규빗, 너비 50 규빗, 높이 30 규빗(고대의 1규빗은 팔꿈치에서 가운데 손가락끝까지의 길이로 약 45~46cm를 가리킴), 상 ·중 ·하 3층으로 된 방주를 만들어 8명의 가족과, 한 쌍씩의 여러 동물을 데리고 이 방주에 탄다. 대홍수를 만나 모든 생물(물고기 제외)이 전멸하고 말았지만, 이 방주에 탔던 노아의 가족과 동물들은 살아 남았다고 한다.〈창세기〉 6장 14~16절에 보면 길이 300규빗 (약 135m), 폭 50 규빗 (약 22.5m), 높이 30 규빗 (약 13.5m)인 이 배는 지붕과 문을 달고 배 안은 3층으로 만들어져 있었다. 선체(船體)는 고페르나무(잣나무)로 되고 안쪽에는 역청(아스팔트와 비슷한 성분)을 칠하여 굳혔다고 기록하고 있다.\n",
      "[Answer]  노아\n",
      "[Predict]  노아는 \n",
      "\n",
      "41 [Answer Failed!!!]\n",
      "\n",
      "42\n",
      "[Question]  노아의 방주의 선체는 어떠한 나무로 만들었는가?\n",
      "[Context]  노아는 하나님의 명령에 따라 배를 만들고 가족과 정결한 짐승 암수 일곱 마리씩, 부정한 짐승 암수 한 마리씩(혹은 두 마리씩; 사본에 따라 다름), 그리고 새 암수 일곱 마리씩을 싣고 밀어닥친 홍수를 피하였다. 모든 사람들이 타락한 생활에 빠져 있어 하나님이 홍수로 심판하려 할 때 홀로 바르게 살던 노아는 하나님의 특별한 계시로 홍수가 올 것을 미리 알게 된다. 그는 길이 300 규빗, 너비 50 규빗, 높이 30 규빗(고대의 1규빗은 팔꿈치에서 가운데 손가락끝까지의 길이로 약 45~46cm를 가리킴), 상 ·중 ·하 3층으로 된 방주를 만들어 8명의 가족과, 한 쌍씩의 여러 동물을 데리고 이 방주에 탄다. 대홍수를 만나 모든 생물(물고기 제외)이 전멸하고 말았지만, 이 방주에 탔던 노아의 가족과 동물들은 살아 남았다고 한다.〈창세기〉 6장 14~16절에 보면 길이 300규빗 (약 135m), 폭 50 규빗 (약 22.5m), 높이 30 규빗 (약 13.5m)인 이 배는 지붕과 문을 달고 배 안은 3층으로 만들어져 있었다. 선체(船體)는 고페르나무(잣나무)로 되고 안쪽에는 역청(아스팔트와 비슷한 성분)을 칠하여 굳혔다고 기록하고 있다.\n",
      "[Answer]  고페르나무\n",
      "[Predict]  고페르나무 \n",
      "\n",
      "43 [Answer Failed!!!]\n",
      "\n",
      "44\n",
      "[Question]  노아의 방주는 몇층으로 구성되어 있었는가?\n",
      "[Context]  노아는 하나님의 명령에 따라 배를 만들고 가족과 정결한 짐승 암수 일곱 마리씩, 부정한 짐승 암수 한 마리씩(혹은 두 마리씩; 사본에 따라 다름), 그리고 새 암수 일곱 마리씩을 싣고 밀어닥친 홍수를 피하였다. 모든 사람들이 타락한 생활에 빠져 있어 하나님이 홍수로 심판하려 할 때 홀로 바르게 살던 노아는 하나님의 특별한 계시로 홍수가 올 것을 미리 알게 된다. 그는 길이 300 규빗, 너비 50 규빗, 높이 30 규빗(고대의 1규빗은 팔꿈치에서 가운데 손가락끝까지의 길이로 약 45~46cm를 가리킴), 상 ·중 ·하 3층으로 된 방주를 만들어 8명의 가족과, 한 쌍씩의 여러 동물을 데리고 이 방주에 탄다. 대홍수를 만나 모든 생물(물고기 제외)이 전멸하고 말았지만, 이 방주에 탔던 노아의 가족과 동물들은 살아 남았다고 한다.〈창세기〉 6장 14~16절에 보면 길이 300규빗 (약 135m), 폭 50 규빗 (약 22.5m), 높이 30 규빗 (약 13.5m)인 이 배는 지붕과 문을 달고 배 안은 3층으로 만들어져 있었다. 선체(船體)는 고페르나무(잣나무)로 되고 안쪽에는 역청(아스팔트와 비슷한 성분)을 칠하여 굳혔다고 기록하고 있다.\n",
      "[Answer]  3층\n",
      "[Predict]  3층으로 \n",
      "\n",
      "45 [Answer Failed!!!]\n",
      "\n",
      "46 [Answer Failed!!!]\n",
      "\n",
      "47\n",
      "[Question]  노아의 방주는 총 몇층으로 되어 있었는가?\n",
      "[Context]  노아는 하나님의 명령에 따라 배를 만들고 가족과 정결한 짐승 암수 일곱 마리씩, 부정한 짐승 암수 한 마리씩(혹은 두 마리씩; 사본에 따라 다름), 그리고 새 암수 일곱 마리씩을 싣고 밀어닥친 홍수를 피하였다. 모든 사람들이 타락한 생활에 빠져 있어 하나님이 홍수로 심판하려 할 때 홀로 바르게 살던 노아는 하나님의 특별한 계시로 홍수가 올 것을 미리 알게 된다. 그는 길이 300 규빗, 너비 50 규빗, 높이 30 규빗(고대의 1규빗은 팔꿈치에서 가운데 손가락끝까지의 길이로 약 45~46cm를 가리킴), 상 ·중 ·하 3층으로 된 방주를 만들어 8명의 가족과, 한 쌍씩의 여러 동물을 데리고 이 방주에 탄다. 대홍수를 만나 모든 생물(물고기 제외)이 전멸하고 말았지만, 이 방주에 탔던 노아의 가족과 동물들은 살아 남았다고 한다.〈창세기〉 6장 14~16절에 보면 길이 300규빗 (약 135m), 폭 50 규빗 (약 22.5m), 높이 30 규빗 (약 13.5m)인 이 배는 지붕과 문을 달고 배 안은 3층으로 만들어져 있었다. 선체(船體)는 고페르나무(잣나무)로 되고 안쪽에는 역청(아스팔트와 비슷한 성분)을 칠하여 굳혔다고 기록하고 있다.\n",
      "[Answer]  3층\n",
      "[Predict]  3층으로 \n",
      "\n",
      "48 [Answer Failed!!!]\n",
      "\n",
      "49\n",
      "[Question]  노아의 방주를 상징적 의미로 받아들이는 종교는 무엇인가?\n",
      "[Context]  역사학과 과학이 발달하지 않았던 과거 전통 신학계에서는 근본주의적 시각을 받아들여 노아의 방주를 역사적 사실로 기술하려 했으며, 이러한 관점은 아직도 과학과 역사학에 어두운 보수적 근본주의계열의 개신교에서만 받아들여지고 있다. 하지만 역사학과 과학의 발달로 인해, 노아의 방주의 실존에 대한 의문이 제기가 되고, 세계적 홍수가 존재할 수 없음이 밝혀짐에 따라 현대 신학계에서는 비록 노아의 홍수가 과학적으로 실존하지는 않았지만 그 자체의 의미는 신학적으로 매우 중요하며, 이에 대한 해석은 다양하게 이루어지고 있으며, 대부분의 기독교(가톨릭, 개신교를 포함한 대부분)에서는 노아의 방주는 상징적 의미로 받아들여진다. 그러므로 과학과는 상관없이 신학적으로 노아의 방주 자체의 의미는 중요하게 해석된다고 한다\n",
      "[Answer]  기독교\n",
      "[Predict]  기독교 \n",
      "\n",
      "50\n",
      "[Question]  노아의 방주의 실존에 대한 의문이 제기되고 세계적 홍수가 없었다는 것이 밝혀지게된 이유는?\n",
      "[Context]  역사학과 과학이 발달하지 않았던 과거 전통 신학계에서는 근본주의적 시각을 받아들여 노아의 방주를 역사적 사실로 기술하려 했으며, 이러한 관점은 아직도 과학과 역사학에 어두운 보수적 근본주의계열의 개신교에서만 받아들여지고 있다. 하지만 역사학과 과학의 발달로 인해, 노아의 방주의 실존에 대한 의문이 제기가 되고, 세계적 홍수가 존재할 수 없음이 밝혀짐에 따라 현대 신학계에서는 비록 노아의 홍수가 과학적으로 실존하지는 않았지만 그 자체의 의미는 신학적으로 매우 중요하며, 이에 대한 해석은 다양하게 이루어지고 있으며, 대부분의 기독교(가톨릭, 개신교를 포함한 대부분)에서는 노아의 방주는 상징적 의미로 받아들여진다. 그러므로 과학과는 상관없이 신학적으로 노아의 방주 자체의 의미는 중요하게 해석된다고 한다\n",
      "[Answer]  역사학과 과학의 발달\n",
      "[Predict]  역사학과 과학의 발달 \n",
      "\n",
      "51\n",
      "[Question]  역사학과 과학의 발달이 미비했을 때 전통 신학계에서는 어떠한 시작으로 노아의 방주를 역사적 사실로 기술하였는가?\n",
      "[Context]  역사학과 과학이 발달하지 않았던 과거 전통 신학계에서는 근본주의적 시각을 받아들여 노아의 방주를 역사적 사실로 기술하려 했으며, 이러한 관점은 아직도 과학과 역사학에 어두운 보수적 근본주의계열의 개신교에서만 받아들여지고 있다. 하지만 역사학과 과학의 발달로 인해, 노아의 방주의 실존에 대한 의문이 제기가 되고, 세계적 홍수가 존재할 수 없음이 밝혀짐에 따라 현대 신학계에서는 비록 노아의 홍수가 과학적으로 실존하지는 않았지만 그 자체의 의미는 신학적으로 매우 중요하며, 이에 대한 해석은 다양하게 이루어지고 있으며, 대부분의 기독교(가톨릭, 개신교를 포함한 대부분)에서는 노아의 방주는 상징적 의미로 받아들여진다. 그러므로 과학과는 상관없이 신학적으로 노아의 방주 자체의 의미는 중요하게 해석된다고 한다\n",
      "[Answer]  근본주의적\n",
      "[Predict]  근본주의적 시각 \n",
      "\n",
      "52 [Answer Failed!!!]\n",
      "\n",
      "53 [Answer Failed!!!]\n",
      "\n",
      "54 [Answer Failed!!!]\n",
      "\n",
      "55\n",
      "[Question]  노아의 방주의 실존에 대한 의문을 제기한 학문은?\n",
      "[Context]  역사학과 과학이 발달하지 않았던 과거 전통 신학계에서는 근본주의적 시각을 받아들여 노아의 방주를 역사적 사실로 기술하려 했으며, 이러한 관점은 아직도 과학과 역사학에 어두운 보수적 근본주의계열의 개신교에서만 받아들여지고 있다. 하지만 역사학과 과학의 발달로 인해, 노아의 방주의 실존에 대한 의문이 제기가 되고, 세계적 홍수가 존재할 수 없음이 밝혀짐에 따라 현대 신학계에서는 비록 노아의 홍수가 과학적으로 실존하지는 않았지만 그 자체의 의미는 신학적으로 매우 중요하며, 이에 대한 해석은 다양하게 이루어지고 있으며, 대부분의 기독교(가톨릭, 개신교를 포함한 대부분)에서는 노아의 방주는 상징적 의미로 받아들여진다. 그러므로 과학과는 상관없이 신학적으로 노아의 방주 자체의 의미는 중요하게 해석된다고 한다\n",
      "[Answer]  역사학과 과학\n",
      "[Predict]  역사학과 과학의 발달 \n",
      "\n",
      "56\n",
      "[Question]  제칠일안식교에서 비롯된 의사과학의 한 종류인 유사지질학의 이름은 무엇인가?\n",
      "[Context]  역사학과 과학의 발달이 더뎠던 고대사회에서는, 성경이 단순한 교리적인 부분 뿐 아니라 역사책으로서의 권위도 높았기에 노아의 방주를 역사적인 존재로서 다루고 있었다. 이는 제칠일안식교에서 비롯된 의사과학의 한 종류인 유사지질학인 홍수지질학과 같은 것에 영향을 주었으며, 과거 신학에서는 이러한 근본주의적 해석을 받아들여 역사와 사회적인 모든 부분에 있어 성경을 교과서로 채택할 것을 촉구했다. 이러한 홍수지질학을 주장했던 유사지질학자들은 성경에 나오는 노아의 홍수가 어딘가에 그 흔적이 남아 있을것이라고 주장하며 노아의 방주를 찾기 위한 노력을 했다고 주장한다. 이들은 같은 메소포타미아 지방의 신화인 이슬람교 경전이나 길가메쉬 서사시등의 신화를 들어서 이를 근거라고 주장하기도 했다. 그러나 이러한 전통적 근본주의적 시각은 과거에는 상당히 힘을 얻었으나, 역사학과 과학의 발달에 따라 힘을 잃게 되었고, 홍수지질학은 유사과학으로서 남게 되었다. 현대에는 뒤의 실존논란에서 다루는 것처럼 이러한 근본주의적 해석은 비과학적인 해석으로 여기는 것이 일반적이지만, 남침례교로 대표되는 극보수주의계열 기독교에서는 아직도 이것이 받아들여지고 있다.\n",
      "[Answer]  홍수지질학\n",
      "[Predict]  홍수지질학과 \n",
      "\n",
      "57\n",
      "[Question]  극보수주의계열의 기독교이자 아직도 노아의 홍수가 있었다고 주장하는 곳은 어디인가?\n",
      "[Context]  역사학과 과학의 발달이 더뎠던 고대사회에서는, 성경이 단순한 교리적인 부분 뿐 아니라 역사책으로서의 권위도 높았기에 노아의 방주를 역사적인 존재로서 다루고 있었다. 이는 제칠일안식교에서 비롯된 의사과학의 한 종류인 유사지질학인 홍수지질학과 같은 것에 영향을 주었으며, 과거 신학에서는 이러한 근본주의적 해석을 받아들여 역사와 사회적인 모든 부분에 있어 성경을 교과서로 채택할 것을 촉구했다. 이러한 홍수지질학을 주장했던 유사지질학자들은 성경에 나오는 노아의 홍수가 어딘가에 그 흔적이 남아 있을것이라고 주장하며 노아의 방주를 찾기 위한 노력을 했다고 주장한다. 이들은 같은 메소포타미아 지방의 신화인 이슬람교 경전이나 길가메쉬 서사시등의 신화를 들어서 이를 근거라고 주장하기도 했다. 그러나 이러한 전통적 근본주의적 시각은 과거에는 상당히 힘을 얻었으나, 역사학과 과학의 발달에 따라 힘을 잃게 되었고, 홍수지질학은 유사과학으로서 남게 되었다. 현대에는 뒤의 실존논란에서 다루는 것처럼 이러한 근본주의적 해석은 비과학적인 해석으로 여기는 것이 일반적이지만, 남침례교로 대표되는 극보수주의계열 기독교에서는 아직도 이것이 받아들여지고 있다.\n",
      "[Answer]  남침례교\n",
      "[Predict]  남침례교로 \n",
      "\n",
      "58 [Answer Failed!!!]\n",
      "\n",
      "59\n",
      "[Question]  역사학과 과학의 발달로 홍수지질학은 어떤 과학으로 남게 되었는가?\n",
      "[Context]  역사학과 과학의 발달이 더뎠던 고대사회에서는, 성경이 단순한 교리적인 부분 뿐 아니라 역사책으로서의 권위도 높았기에 노아의 방주를 역사적인 존재로서 다루고 있었다. 이는 제칠일안식교에서 비롯된 의사과학의 한 종류인 유사지질학인 홍수지질학과 같은 것에 영향을 주었으며, 과거 신학에서는 이러한 근본주의적 해석을 받아들여 역사와 사회적인 모든 부분에 있어 성경을 교과서로 채택할 것을 촉구했다. 이러한 홍수지질학을 주장했던 유사지질학자들은 성경에 나오는 노아의 홍수가 어딘가에 그 흔적이 남아 있을것이라고 주장하며 노아의 방주를 찾기 위한 노력을 했다고 주장한다. 이들은 같은 메소포타미아 지방의 신화인 이슬람교 경전이나 길가메쉬 서사시등의 신화를 들어서 이를 근거라고 주장하기도 했다. 그러나 이러한 전통적 근본주의적 시각은 과거에는 상당히 힘을 얻었으나, 역사학과 과학의 발달에 따라 힘을 잃게 되었고, 홍수지질학은 유사과학으로서 남게 되었다. 현대에는 뒤의 실존논란에서 다루는 것처럼 이러한 근본주의적 해석은 비과학적인 해석으로 여기는 것이 일반적이지만, 남침례교로 대표되는 극보수주의계열 기독교에서는 아직도 이것이 받아들여지고 있다.\n",
      "[Answer]  유사과학\n",
      "[Predict]  유사과학 \n",
      "\n",
      "60 [Answer Failed!!!]\n",
      "\n",
      "61 [Answer Failed!!!]\n",
      "\n",
      "62 [Answer Failed!!!]\n",
      "\n",
      "63\n",
      "[Question]  노아의 방주가 역사적으로 실재했다는 주장은 무엇이 존재하지 않아 학계로부터 전혀 인정받지 못하고 있는가?\n",
      "[Context]  물론 노아의 방주가 신학과 신앙에서 중요한 영향을 차지하는 것은 사실이나, 현재 노아의 방주가 역사적으로 실존한다는 주장은 그 증거가 존재하지 않기에 관련 학계로부터 전혀 인정받지 못하고 있으며 그 실존과 안정성에 대한 수많은 논란이 있다. 한국창조과학회 등에서는 제칠일안식교를 기반으로 한 홍수지질학적 주장들을을 내어 놓고 있지만, 사실과 다른 근거들을 바탕으로 주장하므로 신뢰하기 힘든 것들이 전부라 할 수 있다. 그러므로 현재 노아의 방주가 실존한다는 주장은 그 증거가 존재하지 않기에 관련 학계로부터 전혀 인정받지 못하고 있다. 모든 과학관련 학계에서는 노아의 방주의 구조나 재질등이 실제로 존재할 수 없는 설화속 이야기라는 데에 동의하고 있다.\n",
      "[Answer]  증거\n",
      "[Predict]  그 증거가 존재하지 않기 \n",
      "\n",
      "64\n",
      "[Question]  한국에서 홍수지질학적 주장들을 내어 놓고 있는 집단은?\n",
      "[Context]  물론 노아의 방주가 신학과 신앙에서 중요한 영향을 차지하는 것은 사실이나, 현재 노아의 방주가 역사적으로 실존한다는 주장은 그 증거가 존재하지 않기에 관련 학계로부터 전혀 인정받지 못하고 있으며 그 실존과 안정성에 대한 수많은 논란이 있다. 한국창조과학회 등에서는 제칠일안식교를 기반으로 한 홍수지질학적 주장들을을 내어 놓고 있지만, 사실과 다른 근거들을 바탕으로 주장하므로 신뢰하기 힘든 것들이 전부라 할 수 있다. 그러므로 현재 노아의 방주가 실존한다는 주장은 그 증거가 존재하지 않기에 관련 학계로부터 전혀 인정받지 못하고 있다. 모든 과학관련 학계에서는 노아의 방주의 구조나 재질등이 실제로 존재할 수 없는 설화속 이야기라는 데에 동의하고 있다.\n",
      "[Answer]  한국창조과학회\n",
      "[Predict]  한국창조과학회 등에서는 제칠일안식교를 \n",
      "\n",
      "65 [Answer Failed!!!]\n",
      "\n",
      "66\n",
      "[Question]  2012년 중국에서 노아의 방주가 발견되었다는 보도를 한 방송사는 어디인가?\n",
      "[Context]  일반적으로 터키의 아라랏 산의 경우, 실제 성경 속에 등장하는 아라랏 산은 지금 아라랏이라 불리는 하나의 산이 아니라 당시 아라랏이라고 불리던 광대한 지역의 산들을 모두 가리키는 표현이라는 주장도 나와 있으며, 또한 목재로 만들어진 방주가 현재까지 남아있을 수는 없다는 비판도 받고 있다. 예를 들어, 1955년 프랑스의 탐험가인 Fernand Navarra가 발견한 목재 파편의 경우, 스페인의 임업 연구소에서 목재의 특성을 토대로 5000년 전의 것이라고 밝히긴 했으나 그 신빙성에 문제점이 있었고 후에 방사성 동위원소 측정법 등의 첨단 과학의 도움을 받은 5개 연구소에서 모두 기원 이후의 시기로 연대를 측정했다. 2009년 뿐 아니라 거의 수년에 한번씩 어디선가 노아의 방주를 발견했다는 주장들이 제시되었지만, 심지어 같은 창조과학을 주장하는 사람들에게조차 비판받을 정도였다. 노아의 방주가 다른 여러 지방에서 발견되었다는 주장이 있으나 너무나 다양한 지방(중국, 터키, 인도 등)에 걸쳐있고, 그 주장도 각각 제각각이므로 신빙성이 없다. 예를 들자면, 중국 BTV에서는 2012년에 중국에서 노아의 방주가 발견되었다는 보도를 하였는데, 이것은 창조과학회에서 주장하는 장소와는 전혀 다른곳이기도 하며, 화석화가 진행되지 않은 나무의 존재등으로 가짜임이 밝혀졌다. 때때로 일부 \"학자\"라 칭하는 사람들이 이를 찾기 위해 노력한다고 주장하지만, 이는 학계에서 유사지질학으로 평가되고 있다.\n",
      "[Answer]  BTV\n",
      "[Predict]  BTV \n",
      "\n",
      "67 [Answer Failed!!!]\n",
      "\n",
      "68 [Answer Failed!!!]\n",
      "\n",
      "69 [Answer Failed!!!]\n",
      "\n",
      "70 [Answer Failed!!!]\n",
      "\n",
      "71\n",
      "[Question]  현재의 생물다양성은 대략 몇 종 인가?\n",
      "[Context]  기독교 성경 내용에는 모든 종들을 방주에 태운다고 이야기하고 있으나, 어류나 수중 생물에 대해서는 언급하지 않았다. 이것을 신학적 의미로만 받아들이면 괜찮은 문제이나, 이 현상이 실제로 일어났다고 가정할 경우,이는 종 간 생존 환경의 차이에 대해서 간과하고 있다. 수중 생물이라 하더라도 종에 따라 생존할 수 있는 환경은 각각 다른 것이며, 40일 이내에 현존하는 가장 높은 산인 에베레스트 산도 잠기게 할 정도의 폭우로 인해 담수와 염수가 급작스럽게 섞일 경우, 급격한 삼투압 변화로 인해 대부분의 수생생물들이 폐사하게 되며, 결과적으로 육지 뿐 아니라 바다와 강의 모든 생태계가 파괴된다. 이후 5천년이라는 지극히 짧은 세월 동안 지구상의 동식물이 모두 페름기 대멸종 또는 K-T 대멸종에 준하는 대량절멸에 가까운 상태에서부터 시작하여 현재의 대략 870만(±120만)종에 달하는 생물다양성을 획득하려면 모든 생물들이 각 세대마다 종분화가 일어나야 할 만큼 엄청난 속도로 진화 및 번식이 (멸종 없이) 이루어져야만 가능한 일이다. (이와 관련하여 창조과학회 측에서는 북극곰의 예시를 통해 가지고 있던 특성이 없어지는 것이 진화가 아니라고 주장하지만, 통상적으로 알려진 바와 같이 생물학에서는 이미 존재하는 특성이 없어지는 현상, 즉 퇴화 역시 진화의 정의에 포함된다.) 즉, 노아의 홍수가 실재하는 사건이었다면 진화적 종분화가 현재까지 알려진 것과 비교할 수 없이 엄청난 속도로 이루어져야만 현재 지구의 생물다양성을 설명할 수 있다. 게다가 이것은 현재의 생물종 멸종 속도를 전혀 고려하지 않았다. 다시 말해, 노아의 홍수가 실재하는 전지구적인 사건이기 위해서는 최소 캄브리아기 대폭발 수준의 폭발적인 진화적 종분화가 1-2억년이 아니라 최대 3-4천년 이내에 이루어졌어야만 현생 지구의 생물다양성에 대한 설명이 가능해진다. 그보다 더 중요한 것은, 각 동물들이 차지하는 영역과 먹이사슬에서의 위치, 375일 동안 먹이도 없이 밀폐된 공간으로 인해 받을 스트레스 등 생태적 지위에 대한 고려가 전혀 없다는 점이다. 또한 바다에서 생존이 불가능한 생물종까지 숫자에 포함되었다는 점에서 논란이 있다.\n",
      "[Answer]  870만\n",
      "[Predict]  870만 \n",
      "\n",
      "72 [Answer Failed!!!]\n",
      "\n",
      "73\n",
      "[Question]  급작스러운 폭우로 담수와 염수가 섞일 경우 삼투압 변화로 폐사하는 생물류는?\n",
      "[Context]  기독교 성경 내용에는 모든 종들을 방주에 태운다고 이야기하고 있으나, 어류나 수중 생물에 대해서는 언급하지 않았다. 이것을 신학적 의미로만 받아들이면 괜찮은 문제이나, 이 현상이 실제로 일어났다고 가정할 경우,이는 종 간 생존 환경의 차이에 대해서 간과하고 있다. 수중 생물이라 하더라도 종에 따라 생존할 수 있는 환경은 각각 다른 것이며, 40일 이내에 현존하는 가장 높은 산인 에베레스트 산도 잠기게 할 정도의 폭우로 인해 담수와 염수가 급작스럽게 섞일 경우, 급격한 삼투압 변화로 인해 대부분의 수생생물들이 폐사하게 되며, 결과적으로 육지 뿐 아니라 바다와 강의 모든 생태계가 파괴된다. 이후 5천년이라는 지극히 짧은 세월 동안 지구상의 동식물이 모두 페름기 대멸종 또는 K-T 대멸종에 준하는 대량절멸에 가까운 상태에서부터 시작하여 현재의 대략 870만(±120만)종에 달하는 생물다양성을 획득하려면 모든 생물들이 각 세대마다 종분화가 일어나야 할 만큼 엄청난 속도로 진화 및 번식이 (멸종 없이) 이루어져야만 가능한 일이다. (이와 관련하여 창조과학회 측에서는 북극곰의 예시를 통해 가지고 있던 특성이 없어지는 것이 진화가 아니라고 주장하지만, 통상적으로 알려진 바와 같이 생물학에서는 이미 존재하는 특성이 없어지는 현상, 즉 퇴화 역시 진화의 정의에 포함된다.) 즉, 노아의 홍수가 실재하는 사건이었다면 진화적 종분화가 현재까지 알려진 것과 비교할 수 없이 엄청난 속도로 이루어져야만 현재 지구의 생물다양성을 설명할 수 있다. 게다가 이것은 현재의 생물종 멸종 속도를 전혀 고려하지 않았다. 다시 말해, 노아의 홍수가 실재하는 전지구적인 사건이기 위해서는 최소 캄브리아기 대폭발 수준의 폭발적인 진화적 종분화가 1-2억년이 아니라 최대 3-4천년 이내에 이루어졌어야만 현생 지구의 생물다양성에 대한 설명이 가능해진다. 그보다 더 중요한 것은, 각 동물들이 차지하는 영역과 먹이사슬에서의 위치, 375일 동안 먹이도 없이 밀폐된 공간으로 인해 받을 스트레스 등 생태적 지위에 대한 고려가 전혀 없다는 점이다. 또한 바다에서 생존이 불가능한 생물종까지 숫자에 포함되었다는 점에서 논란이 있다.\n",
      "[Answer]  수생생물\n",
      "[Predict]  수생생물들이 \n",
      "\n",
      "74\n",
      "[Question]  가지고 있는 특성이 없어지는 것은 진화가 아니라는 창조과학회의 주장의 예시는?\n",
      "[Context]  기독교 성경 내용에는 모든 종들을 방주에 태운다고 이야기하고 있으나, 어류나 수중 생물에 대해서는 언급하지 않았다. 이것을 신학적 의미로만 받아들이면 괜찮은 문제이나, 이 현상이 실제로 일어났다고 가정할 경우,이는 종 간 생존 환경의 차이에 대해서 간과하고 있다. 수중 생물이라 하더라도 종에 따라 생존할 수 있는 환경은 각각 다른 것이며, 40일 이내에 현존하는 가장 높은 산인 에베레스트 산도 잠기게 할 정도의 폭우로 인해 담수와 염수가 급작스럽게 섞일 경우, 급격한 삼투압 변화로 인해 대부분의 수생생물들이 폐사하게 되며, 결과적으로 육지 뿐 아니라 바다와 강의 모든 생태계가 파괴된다. 이후 5천년이라는 지극히 짧은 세월 동안 지구상의 동식물이 모두 페름기 대멸종 또는 K-T 대멸종에 준하는 대량절멸에 가까운 상태에서부터 시작하여 현재의 대략 870만(±120만)종에 달하는 생물다양성을 획득하려면 모든 생물들이 각 세대마다 종분화가 일어나야 할 만큼 엄청난 속도로 진화 및 번식이 (멸종 없이) 이루어져야만 가능한 일이다. (이와 관련하여 창조과학회 측에서는 북극곰의 예시를 통해 가지고 있던 특성이 없어지는 것이 진화가 아니라고 주장하지만, 통상적으로 알려진 바와 같이 생물학에서는 이미 존재하는 특성이 없어지는 현상, 즉 퇴화 역시 진화의 정의에 포함된다.) 즉, 노아의 홍수가 실재하는 사건이었다면 진화적 종분화가 현재까지 알려진 것과 비교할 수 없이 엄청난 속도로 이루어져야만 현재 지구의 생물다양성을 설명할 수 있다. 게다가 이것은 현재의 생물종 멸종 속도를 전혀 고려하지 않았다. 다시 말해, 노아의 홍수가 실재하는 전지구적인 사건이기 위해서는 최소 캄브리아기 대폭발 수준의 폭발적인 진화적 종분화가 1-2억년이 아니라 최대 3-4천년 이내에 이루어졌어야만 현생 지구의 생물다양성에 대한 설명이 가능해진다. 그보다 더 중요한 것은, 각 동물들이 차지하는 영역과 먹이사슬에서의 위치, 375일 동안 먹이도 없이 밀폐된 공간으로 인해 받을 스트레스 등 생태적 지위에 대한 고려가 전혀 없다는 점이다. 또한 바다에서 생존이 불가능한 생물종까지 숫자에 포함되었다는 점에서 논란이 있다.\n",
      "[Answer]  북극곰\n",
      "[Predict]  북극곰 \n",
      "\n",
      "75 [Answer Failed!!!]\n",
      "\n",
      "76 [Answer Failed!!!]\n",
      "\n",
      "77 [Answer Failed!!!]\n",
      "\n",
      "78\n",
      "[Question]  목재 선박의 배수량의 한계는 얼마인가?\n",
      "[Context]  창조과학회에서는 또한 노아의 방주가 안정적인 구조였다고 주장하지만, 이와는 달리 노아의 방주는 항해가 불가능한 설계에 가깝다. 실제로 창조과학에서 주장하는 방주의 크기와 철제 부품을 사용하지 않은 목재 선박 중에서 가장 큰 수준의 선박들을 비교하면 배수량이 두배 이상 차이난다. 그리고 목재 선박은 강도 상의 문제 때문에 통상 길이 100m, 배수량 2000톤 정도가 한계로 여겨져 왔다. 창조과학회에서는 노아의 방주의 안정성을 실험하기 위한 연구가 있다고 주장하기도 하나, 그 자체의 불합리성에 대한 비판을 받고 있으며, 관련 주요 연구자는 지질학 석사학위, 생물학 학사학위를 가진 초등학교 교사로서, 주류 학계의 학회나 저널 등에 발표한 적이 없으며 또한 정당한 피어 리뷰에 의해 검증받지 않았다.\n",
      "[Answer]  2000톤\n",
      "[Predict]  2000톤 \n",
      "\n",
      "79\n",
      "[Question]  노아의 방주가 안정적인 구조였다고 주장하는 집단은 어디인가?\n",
      "[Context]  창조과학회에서는 또한 노아의 방주가 안정적인 구조였다고 주장하지만, 이와는 달리 노아의 방주는 항해가 불가능한 설계에 가깝다. 실제로 창조과학에서 주장하는 방주의 크기와 철제 부품을 사용하지 않은 목재 선박 중에서 가장 큰 수준의 선박들을 비교하면 배수량이 두배 이상 차이난다. 그리고 목재 선박은 강도 상의 문제 때문에 통상 길이 100m, 배수량 2000톤 정도가 한계로 여겨져 왔다. 창조과학회에서는 노아의 방주의 안정성을 실험하기 위한 연구가 있다고 주장하기도 하나, 그 자체의 불합리성에 대한 비판을 받고 있으며, 관련 주요 연구자는 지질학 석사학위, 생물학 학사학위를 가진 초등학교 교사로서, 주류 학계의 학회나 저널 등에 발표한 적이 없으며 또한 정당한 피어 리뷰에 의해 검증받지 않았다.\n",
      "[Answer]  창조과학회\n",
      "[Predict]  창조과학회 \n",
      "\n",
      "80\n",
      "[Question]  목재 선박은 강도상의 문제로 통상 길이 몇m가 한계인가?\n",
      "[Context]  창조과학회에서는 또한 노아의 방주가 안정적인 구조였다고 주장하지만, 이와는 달리 노아의 방주는 항해가 불가능한 설계에 가깝다. 실제로 창조과학에서 주장하는 방주의 크기와 철제 부품을 사용하지 않은 목재 선박 중에서 가장 큰 수준의 선박들을 비교하면 배수량이 두배 이상 차이난다. 그리고 목재 선박은 강도 상의 문제 때문에 통상 길이 100m, 배수량 2000톤 정도가 한계로 여겨져 왔다. 창조과학회에서는 노아의 방주의 안정성을 실험하기 위한 연구가 있다고 주장하기도 하나, 그 자체의 불합리성에 대한 비판을 받고 있으며, 관련 주요 연구자는 지질학 석사학위, 생물학 학사학위를 가진 초등학교 교사로서, 주류 학계의 학회나 저널 등에 발표한 적이 없으며 또한 정당한 피어 리뷰에 의해 검증받지 않았다.\n",
      "[Answer]  100m\n",
      "[Predict]  100m \n",
      "\n",
      "81\n",
      "[Question]  노아의 방주 안정성을 실험하기 위한 연구가 있다고 주장하는 단체는?\n",
      "[Context]  창조과학회에서는 또한 노아의 방주가 안정적인 구조였다고 주장하지만, 이와는 달리 노아의 방주는 항해가 불가능한 설계에 가깝다. 실제로 창조과학에서 주장하는 방주의 크기와 철제 부품을 사용하지 않은 목재 선박 중에서 가장 큰 수준의 선박들을 비교하면 배수량이 두배 이상 차이난다. 그리고 목재 선박은 강도 상의 문제 때문에 통상 길이 100m, 배수량 2000톤 정도가 한계로 여겨져 왔다. 창조과학회에서는 노아의 방주의 안정성을 실험하기 위한 연구가 있다고 주장하기도 하나, 그 자체의 불합리성에 대한 비판을 받고 있으며, 관련 주요 연구자는 지질학 석사학위, 생물학 학사학위를 가진 초등학교 교사로서, 주류 학계의 학회나 저널 등에 발표한 적이 없으며 또한 정당한 피어 리뷰에 의해 검증받지 않았다.\n",
      "[Answer]  창조과학회\n",
      "[Predict]  창조과학회 \n",
      "\n",
      "82 [Answer Failed!!!]\n",
      "\n",
      "83\n",
      "[Question]  목재로 만들어진 선박은 강도 상의 통상 길이 몇m가 한계인가?\n",
      "[Context]  창조과학회에서는 또한 노아의 방주가 안정적인 구조였다고 주장하지만, 이와는 달리 노아의 방주는 항해가 불가능한 설계에 가깝다. 실제로 창조과학에서 주장하는 방주의 크기와 철제 부품을 사용하지 않은 목재 선박 중에서 가장 큰 수준의 선박들을 비교하면 배수량이 두배 이상 차이난다. 그리고 목재 선박은 강도 상의 문제 때문에 통상 길이 100m, 배수량 2000톤 정도가 한계로 여겨져 왔다. 창조과학회에서는 노아의 방주의 안정성을 실험하기 위한 연구가 있다고 주장하기도 하나, 그 자체의 불합리성에 대한 비판을 받고 있으며, 관련 주요 연구자는 지질학 석사학위, 생물학 학사학위를 가진 초등학교 교사로서, 주류 학계의 학회나 저널 등에 발표한 적이 없으며 또한 정당한 피어 리뷰에 의해 검증받지 않았다.\n",
      "[Answer]  100m\n",
      "[Predict]  100m \n",
      "\n",
      "84\n",
      "[Question]  목재 선박의 배수량 한계는?\n",
      "[Context]  창조과학회에서는 또한 노아의 방주가 안정적인 구조였다고 주장하지만, 이와는 달리 노아의 방주는 항해가 불가능한 설계에 가깝다. 실제로 창조과학에서 주장하는 방주의 크기와 철제 부품을 사용하지 않은 목재 선박 중에서 가장 큰 수준의 선박들을 비교하면 배수량이 두배 이상 차이난다. 그리고 목재 선박은 강도 상의 문제 때문에 통상 길이 100m, 배수량 2000톤 정도가 한계로 여겨져 왔다. 창조과학회에서는 노아의 방주의 안정성을 실험하기 위한 연구가 있다고 주장하기도 하나, 그 자체의 불합리성에 대한 비판을 받고 있으며, 관련 주요 연구자는 지질학 석사학위, 생물학 학사학위를 가진 초등학교 교사로서, 주류 학계의 학회나 저널 등에 발표한 적이 없으며 또한 정당한 피어 리뷰에 의해 검증받지 않았다.\n",
      "[Answer]  2000톤\n",
      "[Predict]  2000톤 \n",
      "\n",
      "85\n",
      "[Question]  1868년 게이오 4년 4월 11일 반류마루는 누구에게 양도되기로 약속되었는가?\n",
      "[Context]  1868년 게이오 4년 4월 11일 에도 성 무혈 개성을 한 이후 신정부 군에게 양도가 약속되어 있었다. 그러나 해군 부총재, 에노모토 다케아키가 기상 불량 등을 이유로 이를 연기한 후에 결국 인도를 거부했다. 도쿠가와 요시노부를 슨푸 번에 이송할 때의 태운 함선으로 사용한 후, 8월 19일 자정 (20일)에는 마쓰오카 바키치를 함장으로 카이요마루, 가이텐마루, 신소쿠마루, 간린마루 등과 함께 막부 해군이 정박하고 있던 시나가와 해역을 탈출했다. 그 때 태풍에 휘말려 침몰직전이 되었지만, 1개월만에 에노모토 해군과 합류하였다. 에조치에 건너가 하코다테 전쟁에서는 에노모토(하코다테 정부) 해군의 주력함이 되었다. 영국이 기증했을 때 엠퍼러(Emperor, 기증 당시 일본의 수장은 황제가 아니라 쇼군으로 인식되고 있었기 때문에 장군을 지칭)로 명명하고 있음에서 알 수 있듯이, 쇼군용 유람 요트로 기증되었다고 생각되지만, 세상이 그것을 허락하지 않았다. 아이러니하게도, 군함에 통합되어 실제로 쇼군이 첫 좌승한 것이 대정봉환 이후 슨푸 번에 이송되었을 때였다.\n",
      "[Answer]  신정부 군\n",
      "[Predict]  신정부 군 \n",
      "\n",
      "86\n",
      "[Question]  1868년 게이오 4년 4월 11일 신정부 군에게 양도되기로 한 반류마루를 기상 불량 등의 이유로 연기한 후 인도를 거부한 사람은 누구인가?\n",
      "[Context]  1868년 게이오 4년 4월 11일 에도 성 무혈 개성을 한 이후 신정부 군에게 양도가 약속되어 있었다. 그러나 해군 부총재, 에노모토 다케아키가 기상 불량 등을 이유로 이를 연기한 후에 결국 인도를 거부했다. 도쿠가와 요시노부를 슨푸 번에 이송할 때의 태운 함선으로 사용한 후, 8월 19일 자정 (20일)에는 마쓰오카 바키치를 함장으로 카이요마루, 가이텐마루, 신소쿠마루, 간린마루 등과 함께 막부 해군이 정박하고 있던 시나가와 해역을 탈출했다. 그 때 태풍에 휘말려 침몰직전이 되었지만, 1개월만에 에노모토 해군과 합류하였다. 에조치에 건너가 하코다테 전쟁에서는 에노모토(하코다테 정부) 해군의 주력함이 되었다. 영국이 기증했을 때 엠퍼러(Emperor, 기증 당시 일본의 수장은 황제가 아니라 쇼군으로 인식되고 있었기 때문에 장군을 지칭)로 명명하고 있음에서 알 수 있듯이, 쇼군용 유람 요트로 기증되었다고 생각되지만, 세상이 그것을 허락하지 않았다. 아이러니하게도, 군함에 통합되어 실제로 쇼군이 첫 좌승한 것이 대정봉환 이후 슨푸 번에 이송되었을 때였다.\n",
      "[Answer]  에노모토 다케아키\n",
      "[Predict]  에노모토 다케아키 \n",
      "\n",
      "87\n",
      "[Question]  1868년 게이오 4년 8월 19일 자정 반류마루가 탈출한 해역은 어디인가?\n",
      "[Context]  1868년 게이오 4년 4월 11일 에도 성 무혈 개성을 한 이후 신정부 군에게 양도가 약속되어 있었다. 그러나 해군 부총재, 에노모토 다케아키가 기상 불량 등을 이유로 이를 연기한 후에 결국 인도를 거부했다. 도쿠가와 요시노부를 슨푸 번에 이송할 때의 태운 함선으로 사용한 후, 8월 19일 자정 (20일)에는 마쓰오카 바키치를 함장으로 카이요마루, 가이텐마루, 신소쿠마루, 간린마루 등과 함께 막부 해군이 정박하고 있던 시나가와 해역을 탈출했다. 그 때 태풍에 휘말려 침몰직전이 되었지만, 1개월만에 에노모토 해군과 합류하였다. 에조치에 건너가 하코다테 전쟁에서는 에노모토(하코다테 정부) 해군의 주력함이 되었다. 영국이 기증했을 때 엠퍼러(Emperor, 기증 당시 일본의 수장은 황제가 아니라 쇼군으로 인식되고 있었기 때문에 장군을 지칭)로 명명하고 있음에서 알 수 있듯이, 쇼군용 유람 요트로 기증되었다고 생각되지만, 세상이 그것을 허락하지 않았다. 아이러니하게도, 군함에 통합되어 실제로 쇼군이 첫 좌승한 것이 대정봉환 이후 슨푸 번에 이송되었을 때였다.\n",
      "[Answer]  시나가와\n",
      "[Predict]  가이텐마루, 신소쿠마루, 간린마루 등과 함께 막부 해군이 정박하고 있던 시나가와 해역 \n",
      "\n",
      "88 [Answer Failed!!!]\n",
      "\n",
      "89\n",
      "[Question]  군함에 통합되어 실제로 쇼군이 엠퍼러에 첫 좌승한것은 대정봉환 이후 어디에 이송되었을 때인가?\n",
      "[Context]  1868년 게이오 4년 4월 11일 에도 성 무혈 개성을 한 이후 신정부 군에게 양도가 약속되어 있었다. 그러나 해군 부총재, 에노모토 다케아키가 기상 불량 등을 이유로 이를 연기한 후에 결국 인도를 거부했다. 도쿠가와 요시노부를 슨푸 번에 이송할 때의 태운 함선으로 사용한 후, 8월 19일 자정 (20일)에는 마쓰오카 바키치를 함장으로 카이요마루, 가이텐마루, 신소쿠마루, 간린마루 등과 함께 막부 해군이 정박하고 있던 시나가와 해역을 탈출했다. 그 때 태풍에 휘말려 침몰직전이 되었지만, 1개월만에 에노모토 해군과 합류하였다. 에조치에 건너가 하코다테 전쟁에서는 에노모토(하코다테 정부) 해군의 주력함이 되었다. 영국이 기증했을 때 엠퍼러(Emperor, 기증 당시 일본의 수장은 황제가 아니라 쇼군으로 인식되고 있었기 때문에 장군을 지칭)로 명명하고 있음에서 알 수 있듯이, 쇼군용 유람 요트로 기증되었다고 생각되지만, 세상이 그것을 허락하지 않았다. 아이러니하게도, 군함에 통합되어 실제로 쇼군이 첫 좌승한 것이 대정봉환 이후 슨푸 번에 이송되었을 때였다.\n",
      "[Answer]  슨푸 번\n",
      "[Predict]  슨푸 번에 \n",
      "\n",
      "90\n",
      "[Question]  1868년 당시 일본의 해군 부총재는?\n",
      "[Context]  1868년 게이오 4년 4월 11일 에도 성 무혈 개성을 한 이후 신정부 군에게 양도가 약속되어 있었다. 그러나 해군 부총재, 에노모토 다케아키가 기상 불량 등을 이유로 이를 연기한 후에 결국 인도를 거부했다. 도쿠가와 요시노부를 슨푸 번에 이송할 때의 태운 함선으로 사용한 후, 8월 19일 자정 (20일)에는 마쓰오카 바키치를 함장으로 카이요마루, 가이텐마루, 신소쿠마루, 간린마루 등과 함께 막부 해군이 정박하고 있던 시나가와 해역을 탈출했다. 그 때 태풍에 휘말려 침몰직전이 되었지만, 1개월만에 에노모토 해군과 합류하였다. 에조치에 건너가 하코다테 전쟁에서는 에노모토(하코다테 정부) 해군의 주력함이 되었다. 영국이 기증했을 때 엠퍼러(Emperor, 기증 당시 일본의 수장은 황제가 아니라 쇼군으로 인식되고 있었기 때문에 장군을 지칭)로 명명하고 있음에서 알 수 있듯이, 쇼군용 유람 요트로 기증되었다고 생각되지만, 세상이 그것을 허락하지 않았다. 아이러니하게도, 군함에 통합되어 실제로 쇼군이 첫 좌승한 것이 대정봉환 이후 슨푸 번에 이송되었을 때였다.\n",
      "[Answer]  에노모토 다케아키\n",
      "[Predict]  에도 성 무혈 개성을 한 이후 신정부 군에게 양도가 약속되어 있었다. 그러나 해군 부총재, 에노모토 다케아키 \n",
      "\n",
      "91 [Answer Failed!!!]\n",
      "\n",
      "92 [Answer Failed!!!]\n",
      "\n",
      "93\n",
      "[Question]  하코다테 전쟁 시 반류마루의 함장의 이름은 무엇인가?\n",
      "[Context]  일련의 하코다테 전쟁은 적아 쌍방의 문서에 마쓰오카 바키치 함장의 능란한 조함 능력과 냉정한 지휘만이 기록되어 있다. 함포 사격으로 마쓰마에 성을 공격하여 엄호한 이후, 1869년 메이지 2년 3월 25일 미야코 만 해전에서는 폭풍우를 만나 요함과 헤어졌을 때에 만날 약속했던 하치노헤 항에서 대기하고 있었기 때문에 참전에는 이르지 못했다. 이 폭풍우 때도 “함장 마쓰오카 바키치는 배를 조정하는 명수로 로프 하나 손상되지 않았다”고 타고 있던 하야시 다다스가 남긴 바 있다. 이 귀로에서 신정부 군의 철갑함의 추격을 받았다. 기관 능력의 차이로 인한 속도차 때문에 도주가 불가능하다고 판단하고 맞장 공격을 하겠다고 전투 준비를 했지만, 철갑선의 사정거리에 들어간 순간에 순풍이 불기 시작하여 추격을 뿌리치고 하코다테로 돌아올 수 있었다.\n",
      "[Answer]  마쓰오카 바키치\n",
      "[Predict]  마쓰오카 바키치 \n",
      "\n",
      "94\n",
      "[Question]  반류마루가 미야코 만 해전에서 폭풍우를 만나 대기하고 있던 항구의 이름은 무엇인가?\n",
      "[Context]  일련의 하코다테 전쟁은 적아 쌍방의 문서에 마쓰오카 바키치 함장의 능란한 조함 능력과 냉정한 지휘만이 기록되어 있다. 함포 사격으로 마쓰마에 성을 공격하여 엄호한 이후, 1869년 메이지 2년 3월 25일 미야코 만 해전에서는 폭풍우를 만나 요함과 헤어졌을 때에 만날 약속했던 하치노헤 항에서 대기하고 있었기 때문에 참전에는 이르지 못했다. 이 폭풍우 때도 “함장 마쓰오카 바키치는 배를 조정하는 명수로 로프 하나 손상되지 않았다”고 타고 있던 하야시 다다스가 남긴 바 있다. 이 귀로에서 신정부 군의 철갑함의 추격을 받았다. 기관 능력의 차이로 인한 속도차 때문에 도주가 불가능하다고 판단하고 맞장 공격을 하겠다고 전투 준비를 했지만, 철갑선의 사정거리에 들어간 순간에 순풍이 불기 시작하여 추격을 뿌리치고 하코다테로 돌아올 수 있었다.\n",
      "[Answer]  하치노헤\n",
      "[Predict]  약속했던 하치노헤 항 \n",
      "\n",
      "95\n",
      "[Question]  반류마루가 미야코 만 해전당시 폭풍우를 만나 요함과 헤어졌을 때에 만날 약속하여 하치노헤 항에서 대기한 날짜는 언제인가?\n",
      "[Context]  일련의 하코다테 전쟁은 적아 쌍방의 문서에 마쓰오카 바키치 함장의 능란한 조함 능력과 냉정한 지휘만이 기록되어 있다. 함포 사격으로 마쓰마에 성을 공격하여 엄호한 이후, 1869년 메이지 2년 3월 25일 미야코 만 해전에서는 폭풍우를 만나 요함과 헤어졌을 때에 만날 약속했던 하치노헤 항에서 대기하고 있었기 때문에 참전에는 이르지 못했다. 이 폭풍우 때도 “함장 마쓰오카 바키치는 배를 조정하는 명수로 로프 하나 손상되지 않았다”고 타고 있던 하야시 다다스가 남긴 바 있다. 이 귀로에서 신정부 군의 철갑함의 추격을 받았다. 기관 능력의 차이로 인한 속도차 때문에 도주가 불가능하다고 판단하고 맞장 공격을 하겠다고 전투 준비를 했지만, 철갑선의 사정거리에 들어간 순간에 순풍이 불기 시작하여 추격을 뿌리치고 하코다테로 돌아올 수 있었다.\n",
      "[Answer]  1869년 메이지 2년 3월 25일\n",
      "[Predict]  1869년 메이지 2년 3월 25일 \n",
      "\n",
      "96 [Answer Failed!!!]\n",
      "\n",
      "97 [Answer Failed!!!]\n",
      "\n",
      "98\n",
      "[Question]  마쓰오카 바키치함장의 능력과 지휘과 기록된 기록되어 남은 전쟁은?\n",
      "[Context]  일련의 하코다테 전쟁은 적아 쌍방의 문서에 마쓰오카 바키치 함장의 능란한 조함 능력과 냉정한 지휘만이 기록되어 있다. 함포 사격으로 마쓰마에 성을 공격하여 엄호한 이후, 1869년 메이지 2년 3월 25일 미야코 만 해전에서는 폭풍우를 만나 요함과 헤어졌을 때에 만날 약속했던 하치노헤 항에서 대기하고 있었기 때문에 참전에는 이르지 못했다. 이 폭풍우 때도 “함장 마쓰오카 바키치는 배를 조정하는 명수로 로프 하나 손상되지 않았다”고 타고 있던 하야시 다다스가 남긴 바 있다. 이 귀로에서 신정부 군의 철갑함의 추격을 받았다. 기관 능력의 차이로 인한 속도차 때문에 도주가 불가능하다고 판단하고 맞장 공격을 하겠다고 전투 준비를 했지만, 철갑선의 사정거리에 들어간 순간에 순풍이 불기 시작하여 추격을 뿌리치고 하코다테로 돌아올 수 있었다.\n",
      "[Answer]  하코다테 전쟁\n",
      "[Predict]  하코다테 전쟁은 \n",
      "\n",
      "99\n",
      "[Question]  미야코 만 해전에서 아쓰오카 바키치 함장이 폭풍우를 만난 년도는?\n",
      "[Context]  일련의 하코다테 전쟁은 적아 쌍방의 문서에 마쓰오카 바키치 함장의 능란한 조함 능력과 냉정한 지휘만이 기록되어 있다. 함포 사격으로 마쓰마에 성을 공격하여 엄호한 이후, 1869년 메이지 2년 3월 25일 미야코 만 해전에서는 폭풍우를 만나 요함과 헤어졌을 때에 만날 약속했던 하치노헤 항에서 대기하고 있었기 때문에 참전에는 이르지 못했다. 이 폭풍우 때도 “함장 마쓰오카 바키치는 배를 조정하는 명수로 로프 하나 손상되지 않았다”고 타고 있던 하야시 다다스가 남긴 바 있다. 이 귀로에서 신정부 군의 철갑함의 추격을 받았다. 기관 능력의 차이로 인한 속도차 때문에 도주가 불가능하다고 판단하고 맞장 공격을 하겠다고 전투 준비를 했지만, 철갑선의 사정거리에 들어간 순간에 순풍이 불기 시작하여 추격을 뿌리치고 하코다테로 돌아올 수 있었다.\n",
      "[Answer]  1869년\n",
      "[Predict]  1869년 메이지 2년 \n",
      "\n",
      "100\n",
      "[Question]  마쓰오카 비키치가 철갑선의 추격을 뿌리치고 하코다테로 돌아올 수 있었던 원인은?\n",
      "[Context]  일련의 하코다테 전쟁은 적아 쌍방의 문서에 마쓰오카 바키치 함장의 능란한 조함 능력과 냉정한 지휘만이 기록되어 있다. 함포 사격으로 마쓰마에 성을 공격하여 엄호한 이후, 1869년 메이지 2년 3월 25일 미야코 만 해전에서는 폭풍우를 만나 요함과 헤어졌을 때에 만날 약속했던 하치노헤 항에서 대기하고 있었기 때문에 참전에는 이르지 못했다. 이 폭풍우 때도 “함장 마쓰오카 바키치는 배를 조정하는 명수로 로프 하나 손상되지 않았다”고 타고 있던 하야시 다다스가 남긴 바 있다. 이 귀로에서 신정부 군의 철갑함의 추격을 받았다. 기관 능력의 차이로 인한 속도차 때문에 도주가 불가능하다고 판단하고 맞장 공격을 하겠다고 전투 준비를 했지만, 철갑선의 사정거리에 들어간 순간에 순풍이 불기 시작하여 추격을 뿌리치고 하코다테로 돌아올 수 있었다.\n",
      "[Answer]  순풍\n",
      "[Predict]  순풍 \n",
      "\n",
      "101\n",
      "[Question]  1869년 5월 11일 하코다테 해전에서 반류마루가 응전을 벌인 함대는?\n",
      "[Context]  같은 해 5월 11일의 하코다테 총공격(하코다테 해전)에서 벤텐 다이바와 기관 고장으로 움직일 수 없는 떠 있는 포대가 된 가이텐마루의 원호를 받으며 신정부 군 함대와 응전을 벌인다. 새 정부 군함 초요마루의 화약고에 작렬탄을 명중시켜 초요마루는 대폭발을 일으켜 2분만에 굉침된다. 이것은 구 막부군의 사기를 단번에 향상시켰다. 그후 반류마루는 신정부군의 집중 포화를 맞았다. 응전을 계속했지만, 오후가 되면서 탄약이 완전히 소진되었기 때문에 부득이 퇴함을 결정했고, 벤텐 다이바 아래까지 물러나 얕은 물에 좌초시켰다. 승무원은 기관을 파괴 후 (함장 마쓰오카 바키치가 ‘나중에 사용할 수도 있을 것’이라고 방화를 금지했기 때문에 불태우지는 않음), 벤텐 다이바 근처에 도착, 적지 한 가운데를 가로질러 오다이바로 들어갔다. 이날 신정부군의 손에 반류마루는 방화를 당했지만, 화재는 돛대를 태우는 정도에 그쳤고, 선체는 대부분 타지 않았으며, 그 중간 돛대가 부러지면서 균형을 잃고 전복되면서 진화되었다.\n",
      "[Answer]  신정부 군 함대\n",
      "[Predict]  가이텐마루의 원호를 받으며 신정부 군 함대 \n",
      "\n",
      "[Total result] :  102  Questions, 44  Answers Failed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dev_json = os.path.join(data_dir, \"korquad_dev.json\")\n",
    "\n",
    "answer_fail_cnt = 0\n",
    "total_cnt = 0\n",
    "\n",
    "with open(dev_json) as f:\n",
    "    for i, line in enumerate(f):\n",
    "        data = json.loads(line)\n",
    "        question = vocab.decode_pieces(data['question'])\n",
    "        context = vocab.decode_pieces(data['context'])\n",
    "        answer = data['answer']\n",
    "        answer_predict = do_predict(model, question, context)\n",
    "        total_cnt += 1\n",
    "        \n",
    "        if answer in answer_predict:\n",
    "            print(i)\n",
    "            print(\"[Question] \", question)\n",
    "            print(\"[Context] \", context)\n",
    "            print(\"[Answer] \", answer)\n",
    "            print(\"[Predict] \", answer_predict, \"\\n\")\n",
    "        else :\n",
    "            print(i, \"[Answer Failed!!!]\\n\")\n",
    "            answer_fail_cnt += 1\n",
    "            \n",
    "        if 100 < i:\n",
    "            break\n",
    "            \n",
    "    print(\"[Total result] : \", total_cnt, \" Questions,\", answer_fail_cnt, \" Answers Failed\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "impossible-occasions",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pretrained model 사용 유무에 대한 results 시각화\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(val_loss_lst, 'b-', label='non-pretrain')\n",
    "plt.plot(val_loss_lst1, 'r-', label='pretrained')\n",
    "plt.title('Validation Loss', fontsize=15)\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(val_acc_lst, 'b-', label='non-pretrain')\n",
    "plt.plot(val_acc_lst1, 'r-', label='pretrained')\n",
    "plt.title('Validation Accuracy', fontsize=15)\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advisory-expansion",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "different-steps",
   "metadata": {},
   "source": [
    "## - 프로젝트 정리   \n",
    "- pretrained model을 사용했을 때가 사용하지 않았을 때 보다 loss와 accuracy 모두 좋은 결과를 볼 수 있었다. 하지만 두개를 비교할 때 보다 단일 모델만 두고 봤을 때는 좋은 결과라고 할 수 없을 것 같다.   \n",
    "\n",
    "![non](https://github.com/YOOHYOJEONG/AIFFEL_LMS_project/blob/master/ex17/results/non_pre.JPG?raw=true)   \n",
    "- 위는 pretrained model을 사용하지 않았을 때의 결과를 시각화 한 것이다. train_loss가 증가하면서 train_accuracy는 감소하다가 약간 상승한 것을 볼 수 있다. val_loss는 감소하기는 하나 아주 미세하게 감소하기 때문에 좋은 결과라고 볼 수 없을 것 같다.   \n",
    "\n",
    "![pre](https://github.com/YOOHYOJEONG/AIFFEL_LMS_project/blob/master/ex17/results/pre.JPG?raw=true)   \n",
    "- 위는 pretrained model을 사용했을 때의 결과를 시각화 한 것이다. 확실히 loss나 accuracy가 pretrained model을 사용하지 않았을 때 보다는 좋지만 train과 val의 loss, accuracy의 간격이 너무 크기 때문에 과적합을 생각안할 수가 없었다.    \n",
    "\n",
    "\n",
    "- 제대로 된 평가를 하기 위해서는 KorQuAD의 실제 평가 지표인 EM과 F1 score를 기준으로 평가를 해야한다고 생각했다."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}