[E-13]QuizPrediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8360d442",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\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": "markdown",
   "id": "18f34829",
   "metadata": {},
   "source": [
    "# 데이터 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13382937",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "            print_json_tree(value[0], indent + \"  \")\n",
    "        else:\n",
    "            print(f'{indent}- {key}: {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e4065f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = os.getenv('HOME')+'/aiffel/bert_qna/data'\n",
    "model_dir = os.getenv('HOME')+'/aiffel/bert_qna/models'\n",
    "\n",
    "# 훈련데이터 확인\n",
    "train_json_path = data_dir + '/KorQuAD_v1.0_train.json'\n",
    "\n",
    "# 검증데이터 확인\n",
    "dev_json_path = data_dir + '/KorQuAD_v1.0_dev.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bcd70459",
   "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": [
    "with open(train_json_path) as f:\n",
    "    train_json = json.load(f)\n",
    "    print_json_tree(train_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "74dd7a08",
   "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": [
    "with open(dev_json_path) as f:\n",
    "    dev_json = json.load(f)\n",
    "    print_json_tree(dev_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ae189e",
   "metadata": {},
   "source": [
    "# 데이터셋 전처리\n",
    "\n",
    "### 1) 띄어쓰기 단위 정보관리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0da0daa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "파우스트\n",
      "파우스트\n"
     ]
    }
   ],
   "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",
    "    return False\n",
    "\n",
    "# whitespace가 2개인 경우를 처리해야 함\n",
    "\n",
    "string1 = '1839년 파우스트를 읽었다.'\n",
    "string2 = '1839년  파우스트를 읽었다.'\n",
    "print(string1[6:10])\n",
    "print(string2[7:11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fccea387",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "dba43556",
   "metadata": {},
   "source": [
    "### 2) Tokenize by Vocab\n",
    "\n",
    "##### Subword Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3176f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vocab loading\n",
    "vocab = spm.SentencePieceProcessor()\n",
    "vocab.load(f\"{model_dir}/ko_32000.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f541d70c",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "markdown",
   "id": "34d9bc5a",
   "metadata": {},
   "source": [
    "### 3) Improve Span"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b48eab73",
   "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",
      "[question]  바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가?\n",
      "[answer]  교향곡\n",
      "[answer_start] index:  54 character:  교\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('[question] ', question)\n",
    "print('[answer] ', answer_text)\n",
    "print('[answer_start] index: ', answer_start, 'character: ', context[answer_start])\n",
    "print('[answer_end]index: ', answer_end, 'character: ', context[answer_end])\n",
    "\n",
    "# answer_text에 해당하는 context 영역을 정확히 찾아내야 합니다. \n",
    "assert context[answer_start:answer_end + 1] == answer_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cfcbf73a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# context_tokens에서 char_answer의 위치를 찾아 리턴하는 함수\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": "markdown",
   "id": "e51ef6b8",
   "metadata": {},
   "source": [
    "### 4) 데이터셋 분리\n",
    "\n",
    "1. train 데이터셋, dev 데이터셋을 분리\n",
    "2. 위에서 작성한 _improve_span() 함수를 이용해 전처리\n",
    "3. 파일로 저장\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "24149617",
   "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, idxes_by_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_idx = idxes_by_word[answer_start]\n",
    "                    word_end_idx = idxes_by_word[answer_end]\n",
    "\n",
    "                    word_answer = \" \".join(context_words[word_start_idx:word_end_idx + 1])\n",
    "                    char_answer = \" \".join(answer_text.strip().split())\n",
    "                    assert char_answer in word_answer\n",
    "\n",
    "                    context_tokens, idxes_by_token = _tokenize_vocab(vocab, context_words)\n",
    "\n",
    "                    token_start_idx = idxes_by_token[word_start_idx]\n",
    "                    if word_end_idx < len(idxes_by_token) - 1:\n",
    "                        token_end_idx = idxes_by_token[word_end_idx + 1] - 1\n",
    "                    else:\n",
    "                        token_end_idx = len(context_tokens) - 1\n",
    "\n",
    "                    token_start_idx, word_end_idx = _improve_span(vocab, context_tokens, token_start_idx, token_end_idx, 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_idx, \"token_end\":word_end_idx}\n",
    "                    f.write(json.dumps(data, ensure_ascii=False))\n",
    "                    f.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ba9eb677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06cfd85ea2894da19d7c5bdd87af7658",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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   ],
   "source": [
    "# 전처리를 수행하여 파일로 생성합니다. \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": 23,
   "id": "08f81014",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"qa_id\": \"6566495-0-0\", \"title\": \"파우스트_서곡\", \"question\": [\"▁바그너\", \"는\", \"▁괴테\", \"의\", \"▁\", \"파우스트\", \"를\", \"▁읽고\", \"▁무엇을\", \"▁쓰고\", \"자\", \"▁\", \"했\", \"는\", \"가\", \"?\"], \"context\": [\"▁1839\", \"년\", \"▁바그너\", \"는\", \"▁괴테\", \"의\", \"▁\", \"파우스트\", \"을\", \"▁처음\", \"▁읽고\", \"▁그\", \"▁내용에\", \"▁마음이\", \"▁끌려\", \"▁이를\", \"▁소재로\", \"▁해서\", \"▁하나의\", \"▁교향곡\", \"을\", \"▁쓰\", \"려는\", \"▁뜻을\", \"▁갖는다\", \".\", \"▁이\", \"▁시기\", \"▁바그너\", \"는\", \"▁1838\", \"년에\", \"▁빛\", \"▁독\", \"촉\", \"으로\", \"▁산\", \"전\", \"수\", \"전을\", \"▁다\", \"▁\", \"걲\", \"은\", \"▁상황이\", \"라\", \"▁좌절\", \"과\", \"▁실망\", \"에\", \"▁가득\", \"했으며\", \"▁메\", \"피스\", \"토\", \"펠\", \"레스\", \"를\", \"▁만나는\", \"▁\", \"파우스트\", \"의\", \"▁심\", \"경에\", \"▁공감\", \"했다고\", \"▁한다\", \".\", \"▁또한\", \"▁파리에서\", \"▁아브\", \"네\", \"크의\", \"▁지휘\", \"로\", \"▁파리\", \"▁음악원\", \"▁관현악단\", \"이\", \"▁연주하는\", \"▁베토벤\", \"의\", \"▁교향곡\", \"▁9\", \"번을\", \"▁듣고\", \"▁깊은\", \"▁감\", \"명을\", \"▁받았는데\", \",\", \"▁이것이\", \"▁이듬해\", \"▁1\", \"월에\", \"▁\", \"파우스트\", \"의\", \"▁서\", \"곡으로\", \"▁쓰여진\", \"▁이\", \"▁작품에\", \"▁조금\", \"이라도\", \"▁영향을\", \"▁끼\", \"쳤\", \"으리라\", \"는\", \"▁것은\", \"▁의심\", \"할\", \"▁여지가\", \"▁없다\", \".\", \"▁여기\", \"의\", \"▁라\", \"단\", \"조\", \"▁조성\", \"의\", \"▁경우에도\", \"▁그의\", \"▁전기\", \"에\", \"▁적혀\", \"▁있는\", \"▁것처럼\", \"▁단순한\", \"▁정신적\", \"▁피로\", \"나\", \"▁실\", \"의\", \"가\", \"▁반영\", \"된\", \"▁것이\", \"▁아니라\", \"▁베토벤\", \"의\", \"▁합창\", \"교\", \"향\", \"곡\", \"▁조성\", \"의\", \"▁영향을\", \"▁받은\", \"▁것을\", \"▁볼\", \"▁수\", \"▁있다\", \".\", \"▁그렇게\", \"▁교향곡\", \"▁작곡\", \"을\", \"▁1839\", \"년부터\", \"▁40\", \"년에\", \"▁걸쳐\", \"▁파리에서\", \"▁착수\", \"했으나\", \"▁1\", \"악장\", \"을\", \"▁쓴\", \"▁뒤에\", \"▁중단\", \"했다\", \".\", \"▁또한\", \"▁작품의\", \"▁완성\", \"과\", \"▁동시에\", \"▁그는\", \"▁이\", \"▁서\", \"곡\", \"(1\", \"악장\", \")\", \"을\", \"▁파리\", \"▁음악원\", \"의\", \"▁연주회\", \"에서\", \"▁연주\", \"할\", \"▁파트\", \"보\", \"까지\", \"▁준비\", \"하였으나\", \",\", \"▁실제로는\", \"▁이루어지지\", \"는\", \"▁않았다\", \".\", \"▁결국\", \"▁초연\", \"은\", \"▁4\", \"년\", \"▁반\", \"이\", \"▁지난\", \"▁후에\", \"▁드레스덴\", \"에서\", \"▁연주\", \"되었고\", \"▁재\", \"연\", \"도\", \"▁이루어졌\", \"지만\", \",\", \"▁이후에\", \"▁그대로\", \"▁방치\", \"되고\", \"▁말았다\", \".\", \"▁그\", \"▁사이에\", \"▁그는\", \"▁리\", \"엔\", \"치\", \"와\", \"▁방\", \"황\", \"하는\", \"▁네덜란드\", \"인\", \"을\", \"▁완성\", \"하고\", \"▁탄\", \"호\", \"이\", \"저\", \"에도\", \"▁착수\", \"하는\", \"▁등\", \"▁분\", \"주\", \"한\", \"▁시간을\", \"▁보\", \"냈는데\", \",\", \"▁그런\", \"▁바쁜\", \"▁생활\", \"이\", \"▁이\", \"▁곡을\", \"▁잊\", \"게\", \"▁한\", \"▁것이\", \"▁아닌\", \"가\", \"▁하는\", \"▁의견도\", \"▁있다\", \".\"], \"answer\": \"교향곡\", \"token_start\": 19, \"token_end\": 19}\n"
     ]
    }
   ],
   "source": [
    "# 파일 데이터 확인\n",
    "\n",
    "def print_file(filename, count=10):\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\", 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7e236c7",
   "metadata": {},
   "source": [
    "### 5) Question 데이터 분석 : \n",
    "\n",
    "1. 데이터셋을 그대로 사용할 수 있을지\n",
    "2. 이상(abnormal) 데이터가 존재하지는 않는지 확인\n",
    "\n",
    "##### 전체 데이터에서 question 항목의 길이 분포를 조사"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "65637f28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16 ['▁바그너', '는', '▁괴테', '의', '▁', '파우스트', '를', '▁읽고', '▁무엇을', '▁쓰고', '자', '▁', '했', '는', '가', '?']\n",
      "14 ['▁바그너', '는', '▁교향곡', '▁작곡', '을', '▁어디', '까지', '▁쓴', '▁뒤에', '▁중단', '했', '는', '가', '?']\n",
      "16 ['▁바그너', '가', '▁', '파우스트', '▁서', '곡을', '▁쓸', '▁때', '▁어떤', '▁곡', '의', '▁영향을', '▁받았', '는', '가', '?']\n",
      "12 ['▁1839', '년', '▁바그너', '가', '▁교향곡', '의', '▁소재로', '▁쓰', '려고', '▁했던', '▁책은', '?']\n",
      "16 ['▁', '파우스트', '▁서', '곡', '의', '▁라', '단', '조', '▁조성', '이', '▁영향을', '▁받은', '▁베토벤', '의', '▁곡은', '?']\n",
      "13 ['▁바그너', '가', '▁', '파우스트', '를', '▁처음으로', '▁읽', '은', '▁', '년', '도', '는', '?']\n",
      "10 ['▁바그너', '가', '▁처음', '▁교향곡', '▁작곡', '을', '▁한', '▁장소', '는', '?']\n",
      "13 ['▁바그너', '의', '▁1', '악장', '의', '▁초연', '은', '▁어디서', '▁연주', '되었', '는', '가', '?']\n",
      "16 ['▁바그너', '의', '▁작품을', '▁시인', '의', '▁피로', '▁쓰여', '졌다', '고', '▁극찬', '한', '▁것은', '▁누구', '인', '가', '?']\n",
      "18 ['▁잊', '혀', '져', '▁있는', '▁', '파우스트', '▁서', '곡', '▁1', '악장', '을', '▁부활', '시킨', '▁것은', '▁누구', '인', '가', '?']\n"
     ]
    }
   ],
   "source": [
    "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(len(data[\"question\"]), data[\"question\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a405ee",
   "metadata": {},
   "source": [
    "##### token count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "082850ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16, 14, 16, 12, 16, 13, 10, 13, 16, 18]"
      ]
     },
     "execution_count": 26,
     "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": "markdown",
   "id": "2384eb2c",
   "metadata": {},
   "source": [
    "##### 시각화 (히스토그램)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "32801065",
   "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",
    "\n",
    "plt.hist(train_question_counts, bins=100, range=[0, 100], facecolor='b', label='train')\n",
    "\n",
    "plt.title('Count of question')\n",
    "\n",
    "plt.xlabel('Number of question')\n",
    "\n",
    "plt.ylabel('Count of question')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b031d2ad",
   "metadata": {},
   "source": [
    "##### 길이에 대한 통계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2f4c5a66",
   "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",
    "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",
    "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": "markdown",
   "id": "597e18e1",
   "metadata": {},
   "source": [
    "##### 시각화 (박스플롯) - counts, 평균값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f863ac20",
   "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",
    "plt.boxplot(train_question_counts, labels=['token counts'], showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ff8f6c",
   "metadata": {},
   "source": [
    "### 6) Context 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "349e05f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[278, 278, 278, 278, 278, 278, 278, 278, 209, 209]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# token count\n",
    "train_context_counts = [len(context) for context in contexts]\n",
    "train_context_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5b4dce99",
   "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": [
    "# context에서 token 개수 n개를 가지고 있는 context의 개수\n",
    "plt.figure(figsize=(8, 4))\n",
    "\n",
    "plt.hist(train_context_counts, bins=900, range=[100, 1000], facecolor='r', label='train')\n",
    "\n",
    "plt.title('Count of context')\n",
    "\n",
    "plt.xlabel('Number of context')\n",
    "\n",
    "plt.ylabel('Count of context')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "373fa8c1",
   "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",
    "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": 38,
   "id": "b73ee372",
   "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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae93c715",
   "metadata": {},
   "source": [
    "### 7) Answer 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "71c85e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[19, 168, 80, 6, 143, 0, 165, 216, 164, 7]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# token count\n",
    "train_answer_starts = token_starts\n",
    "train_answer_starts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9dd54bff",
   "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",
    "\n",
    "plt.hist(train_answer_starts, bins=500, range=[0, 500], facecolor='g', label='train')\n",
    "\n",
    "plt.title('Count of answer')\n",
    "\n",
    "plt.xlabel('Number of answer')\n",
    "\n",
    "plt.ylabel('Count of answer')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2c644fcc",
   "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",
    "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": 42,
   "id": "64bac61e",
   "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": "d08683ca",
   "metadata": {},
   "source": [
    "### Word Cloud 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6b818367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f4723efd7ee46fcaee4eb5796b9f6db",
       "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?',\n",
       " '1839년 바그너가 교향곡의 소재로 쓰려고 했던 책은?',\n",
       " '파우스트 서곡의 라단조 조성이 영향을 받은 베토벤의 곡은?',\n",
       " '바그너가 파우스트를 처음으로 읽은 년도는?',\n",
       " '바그너가 처음 교향곡 작곡을 한 장소는?',\n",
       " '바그너의 1악장의 초연은 어디서 연주되었는가?']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train documents\n",
    "documents = []\n",
    "\n",
    "# 전체 데이터에서 title, context, question 문장을 모두 추출합니다. \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) # 지문 넣고\n",
    "\n",
    "        for qa in paragraph[\"qas\"]:\n",
    "            assert len(qa[\"answers\"]) == 1\n",
    "            question = qa[\"question\"]\n",
    "            documents.append(question) # 답이 1개만 있는 question(들)) 넣고 \n",
    "\n",
    "documents[:10]   # 그중 맨 앞 10개만 확인해 봅니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a991f0c",
   "metadata": {},
   "source": [
    "##### WordCloud 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b4dc37c6",
   "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 처리해 봅니다. \n",
    "wordcloud = WordCloud(width=800, height=800, font_path='/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf').generate(\" \".join(documents))\n",
    "plt.figure(figsize=(10, 10))\n",
    "# image 출력, interpolation 이미지 시각화 옵션\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1786d6ef",
   "metadata": {},
   "source": [
    "### 9) 데이터 로드\n",
    "\n",
    "- 지금까지 만든 데이터셋을 메모리에 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3dc067b8",
   "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": 47,
   "id": "969c982c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_seq_length': 384, 'max_query_length': 64}"
      ]
     },
     "execution_count": 47,
     "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": "markdown",
   "id": "15d4e286",
   "metadata": {},
   "source": [
    "##### 생성한 데이터셋 파일을 메모리에 로딩하는 함수 + BERT 모델을 위한 데이터 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f52ec5aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "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) # question(CLS,SEP포함)은 0으로 context(SEP포함)는 1로\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": 49,
   "id": "9bd344ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b76c4449423a4f449f8a4b7a6cd43b7b",
       "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": "4c37cf8de0e64f12abfd4df27bfc34c8",
       "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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train data load\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 load\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": "63d79555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    5, 15798,    10, 28935,     9,    11, 29566,    20, 14604,\n",
       "       20424,  3904,    70,    11,  4648,    10,    19,  1910,     4,\n",
       "       22070,    15, 15798,    10, 28935,     9,    11, 29566,    16,\n",
       "         626, 14604,    38, 14028, 11773, 13829,   384,  8376,  3021,\n",
       "        1239,  6874,    16,  1687,  5958,  2694,  5061,     7,    30,\n",
       "        1613, 15798,    10, 28065,    75,  4415,  1816,  4978,    27,\n",
       "         347,   145,   107,  2703,   263,    11,     1,    18,  5853,\n",
       "          99,  9677,    24, 11969,    13,  7595,   437,  1019,  5907,\n",
       "         257,  3794,  1972,    20, 11278,    11, 29566,     9,   612,\n",
       "       12631, 13214,  1732,    76,     7,   110,  8802, 17581,   354,\n",
       "        9648,  2060,    21,  1682, 22110, 18164,    17, 21076, 14980,\n",
       "           9,  6874,    81, 11325,  4239,  3597,  1010,  1035, 17670,\n",
       "           8,  2447,  1306,    35,   443,    11, 29566,     9,   315,\n",
       "       12729, 14457,    30,  7938,  3742, 10766,   634,  9971, 17590,\n",
       "       19424,    10,   285,  4080,    61, 17573,   483,     7,  7588,\n",
       "           9,   473,   338,   147,  1924,     9, 11016,   136,  1034,\n",
       "          13, 11672,    40,  3436,  5217,  7898, 11684,    57,   830,\n",
       "           9,    19,  3319,    86,   220,   464, 14980,     9, 20515,\n",
       "         412,   991,   684,  1924,     9,   634,   920,   144,   430,\n",
       "          34,    25,     7,  4210,  6874,  2150,    16, 22070,   298,\n",
       "        1159,    75,  1098,  8802,  7490,   805,    35, 18678,    16,\n",
       "        1657,  1970,  2272,    53,     7,   110,  6559,  2178,    24,\n",
       "         756,    82,    30,   315,   684,  3772, 18678,    12,    16,\n",
       "        1682, 22110,     9, 22469,    22,  1757,    61,  8817,   194,\n",
       "         164,  1693,   749,     8,  6739, 12202,    10,   494,     7,\n",
       "         502, 12181,    18,    46,    15,   374,    17,  1680,   708,\n",
       "       26344,    22,  1757,   432,   465,   351,    32, 18563,   710,\n",
       "           8,  2585,  1384, 16071,   265,  3360,     7,    38,   747,\n",
       "          82,   383,   678,   200,    26,   590,  1281,    41,  1172,\n",
       "          31,    16,  2178,    43,  3044,   156,    17,   647,   468,\n",
       "        7490,    41,    84,   758,    92,    33,  3401,   369, 18319,\n",
       "           8,  2582, 29798,  1102,    17,    30,  4573, 11170,   139,\n",
       "          58,   220,   773,    19,   211, 23824,    25,     7,     4,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Question과 Context가 포함된 입력데이터 1번째\n",
    "train_inputs[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81df93f1",
   "metadata": {},
   "source": [
    "##### Segment 데이터 \n",
    "- 0 : Question\n",
    "- 1 : Context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4be04d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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,\n",
       "       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,\n",
       "       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,\n",
       "       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,\n",
       "       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,\n",
       "       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,\n",
       "       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, 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,\n",
       "       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,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_inputs[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "639e94d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37, 37)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer위치의 시작점과 끝점 라벨 1번째\n",
    "train_labels[0][0], train_labels[1][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4922b61e",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# BERT 모델"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "061243da",
   "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": 55,
   "id": "dce064e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mode == \"embedding\" 일 경우 Token Embedding Layer 로 사용되는 layer 클래스입니다. \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": 56,
   "id": "a0864d80",
   "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": "markdown",
   "id": "ebb98ed6",
   "metadata": {},
   "source": [
    "### 유사도 구하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ad36e903",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 유사도 구하기\n",
    "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": "markdown",
   "id": "ebacde1d",
   "metadata": {},
   "source": [
    "### 어텐션 병렬 수행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ea6851b6",
   "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": "markdown",
   "id": "9ef1f552",
   "metadata": {},
   "source": [
    "### 피드 포워드 신경망 (Feed Forward Neural Network)\n",
    "- 두 번째 서브 레이어 : 2개의 완전연결층"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4ea6a31f",
   "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": "markdown",
   "id": "82e8441e",
   "metadata": {},
   "source": [
    "### EncoderLayer\n",
    "- 첫번째 서브 레이어 : self attention + dropout + layer normalization\n",
    "- 두번째 서브 레이어 : 완전 연결층 + dropout + layer normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fa9c3e57",
   "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": "markdown",
   "id": "f4b76662",
   "metadata": {},
   "source": [
    "### BERT 모델  (앞의 함수들 결합)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f22f2bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "b1d03437",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# BERT 모델 적용\n",
    "\n",
    "- KorQuAD 태스크를 다시 학습\n",
    "- 데이터셋 구성은 동일하기 때문에 별도의 추가 조치가 필요 없음\n",
    "\n",
    "\n",
    "##### BERT 레이어에 Fully Connected layer를 붙어 KorQuAD용으로 finetune하기 위한 모델 클래스\n",
    "- BERT 모델\n",
    "- dense layer\n",
    "- split -> start 와 end\n",
    "    - squeeze\n",
    "    - Softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "531934cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 63,
   "id": "05076a35",
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = Config({\n",
    "    \"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\": 0, \n",
    "    \"i_pad\": 0\n",
    "})\n",
    "config.n_vocab = len(vocab)\n",
    "config.i_pad = vocab.pad_id()\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "01a45541",
   "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": "markdown",
   "id": "5cdeee30",
   "metadata": {},
   "source": [
    "### 모델 학습을 위한 함수 정의 (train_epoch, eval_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3dd507f8",
   "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": 68,
   "id": "f12f6c97",
   "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": "markdown",
   "id": "71da1fed",
   "metadata": {},
   "source": [
    "#  Step 1. BERT 모델만 구성한 후 전혀 pretraining 없이 학습을 진행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e89601fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = BERT4KorQuAD(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2e9756db",
   "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_36 (Dense)             multiple                  1026      \n",
      "=================================================================\n",
      "Total params: 29,203,970\n",
      "Trainable params: 29,203,970\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "dd2220e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6265aa6bff8c49219fddae72715e474e",
       "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: 5.9506, 5.9506, acc: 0.0300, 0.0105\n",
      "save best model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3707228cc22148978ae03bf830e0ecf9",
       "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: 5.9506, 5.9507, acc: 0.0004, 0.0035\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cf8f647739e438b9e96953f82fb7d26",
       "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: 5.9506, 5.9507, acc: 0.0011, 0.0025\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",
    "for epoch in range(3):\n",
    "    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",
    "    acc = start_acc + end_acc\n",
    "    if best_acc < acc:\n",
    "        patience = 0\n",
    "        best_acc = 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": 119,
   "id": "3b4df499",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 다시 실행할 용기가 안나서, 위에서 도출된 수치값들을 변수로 넣었습니다.\n",
    "model_start_loss = [5.9506, 5.9506, 5.9506]\n",
    "model_end_loss = [5.9506, 5.9507, 5.9507]\n",
    "model_start_acc = [0.0300, 0.0004, 0.0011]\n",
    "model_end_acc = [0.0105, 0.0035, 0.0025]\n",
    "model_acc = [0.0405, 0.0039, 0.0036] # model_start_acc + model_end_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652b9a6e",
   "metadata": {},
   "source": [
    "# Step 2. pretrained model finetune 하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "79e673ac",
   "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_110 (Dense)            multiple                  1026      \n",
      "=================================================================\n",
      "Total params: 29,203,970\n",
      "Trainable params: 29,203,970\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "checkpoint_file = os.path.join(model_dir, 'bert_pretrain_32000.hdf5')\n",
    "\n",
    "pretrained_model = BERT4KorQuAD(config)\n",
    "\n",
    "if os.path.exists(checkpoint_file):\n",
    "    #  pretrained model 을 로드하기 위해 먼저 모델이 생성되어 있어야 한다.\n",
    "    enc_tokens = np.random.randint(0, len(vocab), (4, 10))\n",
    "    segments = np.random.randint(0, 2, (4, 10))\n",
    "    pretrained_model(enc_tokens, segments)\n",
    "    \n",
    "    # checkpoint 파일로부터 필요한 layer를 불러온다. \n",
    "    pretrained_model.load_weights(os.path.join(model_dir, \"bert_pretrain_32000.hdf5\"), by_name=True)\n",
    "\n",
    "    pretrained_model.summary()\n",
    "else:\n",
    "    print('NO Pretrained Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "9a1422e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5db9ad7b1ec4452b92b71bdc829700b7",
       "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: 1.7854, 2.0127, acc: 0.5393, 0.5072\n",
      "save best pretrained model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "230c8476fdbe4f2bbc3bdc87dc9cfc25",
       "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: 1.8455, 2.1341, acc: 0.5471, 0.5242\n",
      "save best pretrained model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcf7b8558d9e4e8f9217ca256abf52b3",
       "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: 1.7824, 1.9666, acc: 0.5664, 0.5290\n",
      "save best pretrained model\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",
    "pretrained_model_start_loss = []\n",
    "pretrained_model_end_loss = []\n",
    "pretrained_model_start_acc = []\n",
    "pretrained_model_end_acc = []\n",
    "pretrained_model_acc = []\n",
    "\n",
    "for epoch in range(3):\n",
    "    train_epoch(pretrained_model, train_dataset, loss_fn, acc_fn, optimizer)\n",
    "    start_loss, end_loss, start_acc, end_acc = eval_epoch(pretrained_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",
    "    acc = start_acc + end_acc\n",
    "    \n",
    "    pretrained_model_start_loss.append(start_loss)\n",
    "    pretrained_model_end_loss.append(end_loss)\n",
    "    pretrained_model_start_acc.append(start_acc)\n",
    "    pretrained_model_end_acc.append(end_acc)\n",
    "    pretrained_model_acc.append(acc)\n",
    "    \n",
    "    if best_acc < acc:\n",
    "        patience = 0\n",
    "        best_acc = acc\n",
    "        pretrained_model.save_weights(os.path.join(data_dir, \"korquad_bert_pretrain.hdf5\"))\n",
    "        print(f'save best pretrained model')\n",
    "    else:\n",
    "        patience += 1\n",
    "    if 2 <= patience:\n",
    "        print(f'early stopping')\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c432ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 다시 실행할 용기가 안나서, 위에서 도출된 수치값들을 변수로 넣었습니다.\n",
    "pretrained_model_start_loss = [1.7854, 1.8455, 1.7824]\n",
    "pretrained_model_end_loss = [2.0127, 2.1341, 1.9666]\n",
    "pretrained_model_start_acc = [0.5393, 0.5471, 0.5664]\n",
    "pretrained_model_end_acc = [0.5072, 0.5242, 0.5290]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfe375f",
   "metadata": {},
   "source": [
    "# Step 3. Inference 수행하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "bfdc4642",
   "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": "markdown",
   "id": "889b5e47",
   "metadata": {},
   "source": [
    "### non-pretrained model의 inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ef4c1b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62\n",
      "질문 :  유사지질학자들이 노아의 홍수를 증명하기 위해 성경 이외에 근거라고 주장한 것들은?\n",
      "지문 :  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",
      "정답 :  이슬람교 경전이나 길가메쉬 서사시\n",
      "예측 :  로서 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",
      "\n"
     ]
    }
   ],
   "source": [
    "dev_json = os.path.join(data_dir, \"korquad_dev.json\")\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",
    "        if answer in answer_predict:\n",
    "            print(i)\n",
    "            print(\"질문 : \", question)\n",
    "            print(\"지문 : \", context)\n",
    "            print(\"정답 : \", answer)\n",
    "            print(\"예측 : \", answer_predict, \"\\n\")\n",
    "        if 100 < i:\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24089df3",
   "metadata": {},
   "source": [
    "### pretrained model의 inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "1d9801a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "질문 :  임종석이 여의도 농민 폭력 시위를 주도한 혐의로 지명수배 된 날은?\n",
      "지문 :  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",
      "정답 :  1989년 2월 15일\n",
      "예측 :  1989년 2월 15일 \n",
      "\n",
      "1\n",
      "질문 :  1989년 6월 30일 평양축전에 대표로 파견 된 인물은?\n",
      "지문 :  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",
      "정답 :  임수경\n",
      "예측 :  임수경을 \n",
      "\n",
      "2\n",
      "질문 :  임종석이 여의도 농민 폭력 시위를 주도한 혐의로 지명수배된 연도는?\n",
      "지문 :  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",
      "정답 :  1989년\n",
      "예측 :  1989년 \n",
      "\n",
      "7\n",
      "질문 :  정부의 헌법개정안 준비 과정에 대해서 청와대 비서실이 아니라 국무회의 중심으로 이뤄졌어야 했다고 지적한 원로 헌법학자는?\n",
      "지문 :  \"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",
      "정답 :  허영\n",
      "예측 :  허영 \n",
      "\n",
      "9\n",
      "질문 :  국무회의의 심의를 거쳐야 한다는 헌법 제 몇 조의 내용인가?\n",
      "지문 :  \"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",
      "정답 :  제89조\n",
      "예측 :  '국무회의의 심의를 거쳐야 한다'(제89조)는 \n",
      "\n",
      "10\n",
      "질문 :  법무부 장관을 제쳐놓고 민정수석이 개정안을 설명하는 게 이해가 안 된다고 지적한 경희대 석좌교수 이름은?\n",
      "지문 :  \"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",
      "정답 :  허영\n",
      "예측 :  허영 \n",
      "\n",
      "15\n",
      "질문 :  미국 군대에서 두번째로 높은 직위는?\n",
      "지문 :  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",
      "정답 :  미국 육군 부참모 총장\n",
      "예측 :  2번째로 높은 직위인 미국 육군 부참모 총장과 나토 및 \n",
      "\n",
      "16\n",
      "질문 :  알렉산더 메이그스 헤이그의 생년월일은?\n",
      "지문 :  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",
      "정답 :  1924년 12월 2일\n",
      "예측 :  1924년 12월 2일 \n",
      "\n",
      "17\n",
      "질문 :  알렉산더 헤이그가 로널드 레이건 대통령 밑에서 맡은 직책은 무엇이었나?\n",
      "지문 :  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",
      "정답 :  국무장관\n",
      "예측 :  국무장관 \n",
      "\n",
      "20\n",
      "질문 :  알렉산더 헤이그가 미국 육군사관학교로 임명받은 해는 언제인가?\n",
      "지문 :  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",
      "정답 :  1944년\n",
      "예측 :  1944년 \n",
      "\n",
      "21\n",
      "질문 :  알렉산더 헤이그는 퍼트리샤 앤토이넷 폭스와 결혼해 몇 명의 자녀를 두었는가?\n",
      "지문 :  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",
      "정답 :  3명\n",
      "예측 :  3명의 \n",
      "\n",
      "24\n",
      "질문 :  알렉산더 헤이그가 나온 대학교는?\n",
      "지문 :  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",
      "정답 :  노터데임 대학교\n",
      "예측 :  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 \n",
      "\n",
      "27\n",
      "질문 :  헤이그가 군에서 퇴역한 해는 언제인가?\n",
      "지문 :  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",
      "정답 :  1979년\n",
      "예측 :  1979년 \n",
      "\n",
      "29\n",
      "질문 :  헤이그는 나토에서 얼마나 있었습니까?\n",
      "지문 :  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",
      "정답 :  5년\n",
      "예측 :  5년 \n",
      "\n",
      "30\n",
      "질문 :  헤이그가 군에서 퇴역한 년도는 몇년도입니까?\n",
      "지문 :  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",
      "정답 :  1979년\n",
      "예측 :  1979년 \n",
      "\n",
      "32\n",
      "질문 :  헤이그가 군대에서 퇴역한 년도는?\n",
      "지문 :  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",
      "정답 :  1979년\n",
      "예측 :  1979년 \n",
      "\n",
      "34\n",
      "질문 :  알렉산더 헤이그는 레이건의 조언자들을 무엇이라고 묘사하였는가?\n",
      "지문 :  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",
      "정답 :  외교 정책의 아마추어\n",
      "예측 :  \"외교 정책의 아마추어\"로 \n",
      "\n",
      "35\n",
      "질문 :  헤이그가 사적생활을 하다가 정계로 돌아갔던 해는 언제인가?\n",
      "지문 :  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",
      "정답 :  1988년\n",
      "예측 :  1988년 \n",
      "\n",
      "37\n",
      "질문 :  헤이그가 정계로 돌아간 년도는 몇년도입니까?\n",
      "지문 :  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",
      "정답 :  1988년\n",
      "예측 :  1988년 \n",
      "\n",
      "38\n",
      "질문 :  레이건 대통령의 조언자들을 헤이그는 무엇이라고 묘사하였나?\n",
      "지문 :  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",
      "정답 :  외교 정책의 아마추어\n",
      "예측 :  \"외교 정책의 아마추어\"로 \n",
      "\n",
      "39\n",
      "질문 :  헤이그가 정계로 다시 돌아간 년도는?\n",
      "지문 :  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",
      "정답 :  1988년\n",
      "예측 :  1988년 \n",
      "\n",
      "42\n",
      "질문 :  노아의 방주의 선체는 어떠한 나무로 만들었는가?\n",
      "지문 :  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",
      "정답 :  고페르나무\n",
      "예측 :  고페르나무 \n",
      "\n",
      "43\n",
      "질문 :  노아는 누구의 명령에 따라 배를 만들고 가족과 동물들을 태웠는가?\n",
      "지문 :  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",
      "정답 :  하나님\n",
      "예측 :  하나님의 \n",
      "\n",
      "44\n",
      "질문 :  노아의 방주는 몇층으로 구성되어 있었는가?\n",
      "지문 :  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",
      "정답 :  3층\n",
      "예측 :  3층으로 \n",
      "\n",
      "45\n",
      "질문 :  노아의 방주에 안쪽에 발라 굳힌 것은?\n",
      "지문 :  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",
      "정답 :  역청\n",
      "예측 :  역청 \n",
      "\n",
      "46\n",
      "질문 :  1규빗을 미터법으로 환산하면 얼마인가?\n",
      "지문 :  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",
      "정답 :  45~46cm\n",
      "예측 :  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 14~16절 \n",
      "\n",
      "47\n",
      "질문 :  노아의 방주는 총 몇층으로 되어 있었는가?\n",
      "지문 :  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",
      "정답 :  3층\n",
      "예측 :  3층으로 \n",
      "\n",
      "49\n",
      "질문 :  노아의 방주를 상징적 의미로 받아들이는 종교는 무엇인가?\n",
      "지문 :  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",
      "정답 :  기독교\n",
      "예측 :  기독교 \n",
      "\n",
      "51\n",
      "질문 :  역사학과 과학의 발달이 미비했을 때 전통 신학계에서는 어떠한 시작으로 노아의 방주를 역사적 사실로 기술하였는가?\n",
      "지문 :  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",
      "정답 :  근본주의적\n",
      "예측 :  근본주의적 시각 \n",
      "\n",
      "55\n",
      "질문 :  노아의 방주의 실존에 대한 의문을 제기한 학문은?\n",
      "지문 :  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",
      "정답 :  역사학과 과학\n",
      "예측 :  역사학과 과학의 발달 \n",
      "\n",
      "57\n",
      "질문 :  극보수주의계열의 기독교이자 아직도 노아의 홍수가 있었다고 주장하는 곳은 어디인가?\n",
      "지문 :  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",
      "정답 :  남침례교\n",
      "예측 :  남침례교 \n",
      "\n",
      "58\n",
      "질문 :  고대사회에서 성경은 교리를 다루는 책일 뿐만 아니라 어떤 책으로도 권위가 상당했는가?\n",
      "지문 :  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",
      "정답 :  역사책\n",
      "예측 :  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 실존논란 \n",
      "\n",
      "59\n",
      "질문 :  역사학과 과학의 발달로 홍수지질학은 어떤 과학으로 남게 되었는가?\n",
      "지문 :  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",
      "정답 :  유사과학\n",
      "예측 :  유사과학 \n",
      "\n",
      "61\n",
      "질문 :  유사지질학인 홍수지질학이 근원은?\n",
      "지문 :  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",
      "정답 :  제칠일안식교\n",
      "예측 :  제칠일안식교 \n",
      "\n",
      "63\n",
      "질문 :  노아의 방주가 역사적으로 실재했다는 주장은 무엇이 존재하지 않아 학계로부터 전혀 인정받지 못하고 있는가?\n",
      "지문 :  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",
      "정답 :  증거\n",
      "예측 :  실존한다는 주장은 그 증거가 \n",
      "\n",
      "66\n",
      "질문 :  2012년 중국에서 노아의 방주가 발견되었다는 보도를 한 방송사는 어디인가?\n",
      "지문 :  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",
      "정답 :  BTV\n",
      "예측 :  중국 BTV \n",
      "\n",
      "68\n",
      "질문 :  중국에서 2012년 발견되었다고 주장한 노아의 방주는 화석화가 진행되지 않은 무엇때문에 가짜임이 밝혀졌는가?\n",
      "지문 :  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",
      "정답 :  나무\n",
      "예측 :  나무의 존재등으로 \n",
      "\n",
      "70\n",
      "질문 :  2012년 중국 BTV에서 노아의 방주가 발견되었다고 보도한 나라는?\n",
      "지문 :  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",
      "정답 :  중국\n",
      "예측 :  중국에서 \n",
      "\n",
      "74\n",
      "질문 :  가지고 있는 특성이 없어지는 것은 진화가 아니라는 창조과학회의 주장의 예시는?\n",
      "지문 :  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",
      "정답 :  북극곰\n",
      "예측 :  북극곰 \n",
      "\n",
      "80\n",
      "질문 :  목재 선박은 강도상의 문제로 통상 길이 몇m가 한계인가?\n",
      "지문 :  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",
      "정답 :  100m\n",
      "예측 :  100m \n",
      "\n",
      "83\n",
      "질문 :  목재로 만들어진 선박은 강도 상의 통상 길이 몇m가 한계인가?\n",
      "지문 :  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",
      "정답 :  100m\n",
      "예측 :  100m \n",
      "\n",
      "84\n",
      "질문 :  목재 선박의 배수량 한계는?\n",
      "지문 :  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",
      "정답 :  2000톤\n",
      "예측 :  2000톤 \n",
      "\n",
      "85\n",
      "질문 :  1868년 게이오 4년 4월 11일 반류마루는 누구에게 양도되기로 약속되었는가?\n",
      "지문 :  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",
      "정답 :  신정부 군\n",
      "예측 :  신정부 군 \n",
      "\n",
      "86\n",
      "질문 :  1868년 게이오 4년 4월 11일 신정부 군에게 양도되기로 한 반류마루를 기상 불량 등의 이유로 연기한 후 인도를 거부한 사람은 누구인가?\n",
      "지문 :  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",
      "정답 :  에노모토 다케아키\n",
      "예측 :  에노모토 다케아키 \n",
      "\n",
      "88\n",
      "질문 :  막부 해군이 정박하고 있던 시나가와 해역을 탈출한 시간은?\n",
      "지문 :  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",
      "정답 :  자정\n",
      "예측 :  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 \n",
      "\n",
      "89\n",
      "질문 :  군함에 통합되어 실제로 쇼군이 엠퍼러에 첫 좌승한것은 대정봉환 이후 어디에 이송되었을 때인가?\n",
      "지문 :  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",
      "정답 :  슨푸 번\n",
      "예측 :  슨푸 번에 \n",
      "\n",
      "90\n",
      "질문 :  1868년 당시 일본의 해군 부총재는?\n",
      "지문 :  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",
      "정답 :  에노모토 다케아키\n",
      "예측 :  에노모토 다케아키 \n",
      "\n",
      "93\n",
      "질문 :  하코다테 전쟁 시 반류마루의 함장의 이름은 무엇인가?\n",
      "지문 :  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",
      "정답 :  마쓰오카 바키치\n",
      "예측 :  마쓰오카 바키치 \n",
      "\n",
      "94\n",
      "질문 :  반류마루가 미야코 만 해전에서 폭풍우를 만나 대기하고 있던 항구의 이름은 무엇인가?\n",
      "지문 :  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",
      "정답 :  하치노헤\n",
      "예측 :  하치노헤 항 \n",
      "\n",
      "95\n",
      "질문 :  반류마루가 미야코 만 해전당시 폭풍우를 만나 요함과 헤어졌을 때에 만날 약속하여 하치노헤 항에서 대기한 날짜는 언제인가?\n",
      "지문 :  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",
      "정답 :  1869년 메이지 2년 3월 25일\n",
      "예측 :  1869년 메이지 2년 3월 25일 \n",
      "\n",
      "99\n",
      "질문 :  미야코 만 해전에서 아쓰오카 바키치 함장이 폭풍우를 만난 년도는?\n",
      "지문 :  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",
      "정답 :  1869년\n",
      "예측 :  1869년 메이지 2년 \n",
      "\n",
      "101\n",
      "질문 :  1869년 5월 11일 하코다테 해전에서 반류마루가 응전을 벌인 함대는?\n",
      "지문 :  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",
      "정답 :  신정부 군 함대\n",
      "예측 :  가이텐마루의 원호를 받으며 신정부 군 함대 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "dev_json = os.path.join(data_dir, \"korquad_dev.json\")\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(pretrained_model, question, context)\n",
    "        if answer in answer_predict:\n",
    "            print(i)\n",
    "            print(\"질문 : \", question)\n",
    "            print(\"지문 : \", context)\n",
    "            print(\"정답 : \", answer)\n",
    "            print(\"예측 : \", answer_predict, \"\\n\")\n",
    "        if 100 < i:\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d73a486",
   "metadata": {},
   "source": [
    "# Step 4. 학습 경과 시각화 비교 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b6705f",
   "metadata": {},
   "source": [
    "### non-pretrained model의 loss & accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "9d0b3c6a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis_value = list(map(lambda x: str(x + 1), [*range(len(model_start_loss))]))\n",
    "loss = []\n",
    "\n",
    "for i,x in enumerate(model_start_loss):\n",
    "    loss.append(x + model_end_loss[i])\n",
    "    \n",
    "# training result\n",
    "plt.figure(figsize=(16, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x_axis_value, model_start_loss, 'b-', label='start_loss')\n",
    "plt.plot(x_axis_value, model_end_loss, 'g-', label='end_loss')\n",
    "# plt.plot(x_axis_value, loss, 'r--', label='loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "\n",
    "plt.plot(x_axis_value, model_start_acc, 'b-', label='start_acc')\n",
    "plt.plot(x_axis_value, model_end_acc, 'g-', label='end_acc')\n",
    "plt.plot(x_axis_value, model_acc, 'r--', label='acc')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "679750df",
   "metadata": {},
   "source": [
    "##### non-pretraining결과, loss는 증가하고 accuracy는 점차 감소합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93432475",
   "metadata": {},
   "source": [
    "### pretrained model의 loss & accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dab622f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis_value = list(map(lambda x: str(x + 1), [*range(len(pretrained_model_start_loss))]))\n",
    "\n",
    "loss = []\n",
    "for i,x in enumerate(pretrained_model_start_loss):\n",
    "    loss.append(x + pretrained_model_end_loss[i])\n",
    "    \n",
    "acc = []\n",
    "for i,x in enumerate(pretrained_model_start_acc):\n",
    "    acc.append(x + pretrained_model_end_acc[i])\n",
    "    \n",
    "    \n",
    "# training result\n",
    "plt.figure(figsize=(16, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x_axis_value, pretrained_model_start_loss, 'b-', label='start_loss')\n",
    "plt.plot(x_axis_value, pretrained_model_end_loss, 'g-', label='end_loss')\n",
    "plt.plot(x_axis_value, loss, 'r--', label='loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x_axis_value, pretrained_model_start_acc, 'b-', label='start_acc')\n",
    "plt.plot(x_axis_value, pretrained_model_end_acc, 'g-', label='end_acc')\n",
    "plt.plot(x_axis_value, acc, 'r--', label='acc')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51fb664e",
   "metadata": {},
   "source": [
    "##### pretraining 결과, loss가 하락했고 accuracy가 점차 증가했습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8868b7e5",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 회고\n",
    "\n",
    "### 1. 시도한 것들\n",
    "1. pretrained model을 적용하여 성능 개선을 보였습니다.\n",
    "    - validation dataset으로 accuracy를 확인했을 때 pretrained model이 조금씩 더 증가하는 것을 확인했고, (전체 epoch 중) 3 epoch에서 1.09533 가장 높은 수치를 보였습니다.\n",
    "    - loss는 3 epoch일 때  줄어들었습니다.\n",
    "2. pretrained model의 예측 답안이 대부분 정답과 동일하게 도출됩니다.\n",
    "    - 예측된 답안을 보면 대부분 정답과 정확하게 예측이 나왔고, 몇몇은 조사만 붙을 뿐 정답과 거의 비슷한 결과를 도출했습니다.\n",
    "    - 그러나 아직 몇부분에서 (정답이 단어로 나왔어야 하지만) 문장으로 나온 것들이 5개 정도 있었습니다.\n",
    "3. [pretraining을 사용하지 않은 모델 vs 사용한 모델] 을 시각화하여 비교했습니다.\n",
    "    - 시각화 결과 pretrained model을 사용하지 않았을 때보다 사용했을 때 훨씬 더 좋은 안정적인 성능을 보입니다.\n",
    "    \n",
    "    \n",
    "### 2. 소감\n",
    "- 너무 너무너무 정말정말 재밌었습니다. 데이터 뿐만 아니라, 정답 예측이란 NLP 주제가 있다는 것도 배우고 방법론도 흥미로웠습니다. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}