bert_qna/KorQuAD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BERT 실습"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KorQuAD\n",
    "KorQuAD 1.0 데이터 다운로드\n",
    "```\n",
    "$ wget https://korquad.github.io/dataset/KorQuAD_v1.0_train.json\n",
    "$ wget https://korquad.github.io/dataset/KorQuAD_v1.0_dev.json\n",
    "$ mv KorQuAD_v1.0* ~/aiffel/bert_qna/data\n",
    "\n",
    "$ wget https://aiffelstaticprd.blob.core.windows.net/media/documents/ko_32000.model\n",
    "$ wget https://aiffelstaticprd.blob.core.windows.net/media/documents/ko_32000.vocab\n",
    "$ wget https://aiffelstaticprd.blob.core.windows.net/media/documents/bert_pretrain_32000.hdf5\n",
    "$ mv ko_32000* ~/aiffel/bert_qna/models\n",
    "$ mv bert_pretrain_32000.hdf5 ~/aiffel/bert_qna/models\n",
    "\n",
    "$ wget https://aiffelstaticprd.blob.core.windows.net/media/documents/kowiki.txt.zip\n",
    "$ mv kowiki.txt.zip ~/aiffel/bert_qna/data\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 한국어 폰트 설치를 확인합니다.  \n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "fontpath = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf'\n",
    "font = fm.FontProperties(fname=fontpath, size=9)\n",
    "plt.rc('font', family='NanumBarunGothic') \n",
    "mpl.font_manager._rebuild()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_seed = 1234\n",
    "random.seed(random_seed)\n",
    "np.random.seed(random_seed)\n",
    "tf.random.set_seed(random_seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 데이터 불러오기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print_json_tree : json 포맷의 데이터에서 리스트의 첫번째 아이템의 실제 내용 출력 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": [
    "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",
    "with open(train_json_path) as f:\n",
    "    train_json = json.load(f)\n",
    "    print_json_tree(train_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- version: KorQuAD_v1.0_dev\n",
      "- data: [140]\n",
      "  - paragraphs: [2]\n",
      "    - qas: [7]\n",
      "      - answers: [1]\n",
      "        - text: 1989년 2월 15일\n",
      "        - answer_start: 0\n",
      "      - id: 6548850-0-0\n",
      "      - question: 임종석이 여의도 농민 폭력 시위를 주도한 혐의로 지명수배 된 날은?\n",
      "    - context: 1989년 2월 15일 여의도 농민 폭력 시위를 주도한 혐의(폭력행위등처벌에관한법률위반)으로 지명수배되었다. 1989년 3월 12일 서울지방검찰청 공안부는 임종석의 사전구속영장을 발부받았다. 같은 해 6월 30일 평양축전에 임수경을 대표로 파견하여 국가보안법위반 혐의가 추가되었다. 경찰은 12월 18일~20일 사이 서울 경희대학교에서 임종석이 성명 발표를 추진하고 있다는 첩보를 입수했고, 12월 18일 오전 7시 40분 경 가스총과 전자봉으로 무장한 특공조 및 대공과 직원 12명 등 22명의 사복 경찰을 승용차 8대에 나누어 경희대학교에 투입했다. 1989년 12월 18일 오전 8시 15분 경 서울청량리경찰서는 호위 학생 5명과 함께 경희대학교 학생회관 건물 계단을 내려오는 임종석을 발견, 검거해 구속을 집행했다. 임종석은 청량리경찰서에서 약 1시간 동안 조사를 받은 뒤 오전 9시 50분 경 서울 장안동의 서울지방경찰청 공안분실로 인계되었다.\n",
      "  - title: 임종석\n"
     ]
    }
   ],
   "source": [
    "# 검증데이터 확인\n",
    "dev_json_path = data_dir + '/KorQuAD_v1.0_dev.json'\n",
    "with open(dev_json_path) as f:\n",
    "    dev_json = json.load(f)\n",
    "    print_json_tree(dev_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"paragraphs\": [\n",
      "    {\n",
      "      \"qas\": [\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"교향곡\",\n",
      "              \"answer_start\": 54\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566495-0-0\",\n",
      "          \"question\": \"바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"1악장\",\n",
      "              \"answer_start\": 421\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566495-0-1\",\n",
      "          \"question\": \"바그너는 교향곡 작곡을 어디까지 쓴 뒤에 중단했는가?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"베토벤의 교향곡 9번\",\n",
      "              \"answer_start\": 194\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566495-0-2\",\n",
      "          \"question\": \"바그너가 파우스트 서곡을 쓸 때 어떤 곡의 영향을 받았는가?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"파우스트\",\n",
      "              \"answer_start\": 15\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566518-0-0\",\n",
      "          \"question\": \"1839년 바그너가 교향곡의 소재로 쓰려고 했던 책은?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"합창교향곡\",\n",
      "              \"answer_start\": 354\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566518-0-1\",\n",
      "          \"question\": \"파우스트 서곡의 라단조 조성이 영향을 받은 베토벤의 곡은?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"1839\",\n",
      "              \"answer_start\": 0\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"5917067-0-0\",\n",
      "          \"question\": \"바그너가 파우스트를 처음으로 읽은 년도는?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"파리\",\n",
      "              \"answer_start\": 410\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"5917067-0-1\",\n",
      "          \"question\": \"바그너가 처음 교향곡 작곡을 한 장소는?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"드레스덴\",\n",
      "              \"answer_start\": 534\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"5917067-0-2\",\n",
      "          \"question\": \"바그너의 1악장의 초연은 어디서 연주되었는가?\"\n",
      "        }\n",
      "      ],\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",
      "    },\n",
      "    {\n",
      "      \"qas\": [\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"한스 폰 뷜로\",\n",
      "              \"answer_start\": 402\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566495-1-0\",\n",
      "          \"question\": \"바그너의 작품을 시인의 피로 쓰여졌다고 극찬한 것은 누구인가?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"리스트\",\n",
      "              \"answer_start\": 23\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566495-1-1\",\n",
      "          \"question\": \"잊혀져 있는 파우스트 서곡 1악장을 부활시킨 것은 누구인가?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"20루이의 금\",\n",
      "              \"answer_start\": 345\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566495-1-2\",\n",
      "          \"question\": \"바그너는 다시 개정된 총보를 얼마를 받고 팔았는가?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"리스트\",\n",
      "              \"answer_start\": 23\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566518-1-0\",\n",
      "          \"question\": \"파우스트 교향곡을 부활시킨 사람은?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"한스 폰 뷜로\",\n",
      "              \"answer_start\": 402\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566518-1-1\",\n",
      "          \"question\": \"파우스트 교향곡을 피아노 독주용으로 편곡한 사람은?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"리스트\",\n",
      "              \"answer_start\": 23\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"5917067-1-0\",\n",
      "          \"question\": \"1악장을 부활시켜 연주한 사람은?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"한스 폰 뷜로\",\n",
      "              \"answer_start\": 402\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"5917067-1-1\",\n",
      "          \"question\": \"파우스트 교향곡에 감탄하여 피아노곡으로 편곡한 사람은?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"1840년\",\n",
      "              \"answer_start\": 3\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"5917067-1-2\",\n",
      "          \"question\": \"리스트가 바그너와 알게 된 연도는?\"\n",
      "        }\n",
      "      ],\n",
      "      \"context\": \"한편 1840년부터 바그너와 알고 지내던 리스트가 잊혀져 있던 1악장을 부활시켜 1852년에 바이마르에서 연주했다. 이것을 계기로 바그너도 이 작품에 다시 관심을 갖게 되었고, 그 해 9월에는 총보의 반환을 요구하여 이를 서곡으로 간추린 다음 수정을 했고 브라이트코프흐 & 헤르텔 출판사에서 출판할 개정판도 준비했다. 1853년 5월에는 리스트가 이 작품이 수정되었다는 것을 인정했지만, 끝내 바그너의 출판 계획은 무산되고 말았다. 이후 1855년에 리스트가 자신의 작품 파우스트 교향곡을 거의 완성하여 그 사실을 바그너에게 알렸고, 바그너는 다시 개정된 총보를 리스트에게 보내고 브라이트코프흐 & 헤르텔 출판사에는 20루이의 금을 받고 팔았다. 또한 그의 작품을 “하나하나의 음표가 시인의 피로 쓰여졌다”며 극찬했던 한스 폰 뷜로가 그것을 피아노 독주용으로 편곡했는데, 리스트는 그것을 약간 변형되었을 뿐이라고 지적했다. 이 서곡의 총보 첫머리에는 파우스트 1부의 내용 중 한 구절을 인용하고 있다.\"\n",
      "    },\n",
      "    {\n",
      "      \"qas\": [\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"주제, 동기\",\n",
      "              \"answer_start\": 70\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566495-2-0\",\n",
      "          \"question\": \"서주에는 무엇이 암시되어 있는가?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"제1바이올린\",\n",
      "              \"answer_start\": 148\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566495-2-1\",\n",
      "          \"question\": \"첫부분에는 어떤 악기를 사용해 더욱 명확하게 나타내는가?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"소나타 형식\",\n",
      "              \"answer_start\": 272\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566495-2-2\",\n",
      "          \"question\": \"주요부는 어떤 형식으로 되어 있는가?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"저음 주제\",\n",
      "              \"answer_start\": 102\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566518-2-0\",\n",
      "          \"question\": \"첫 부분의 주요주제를 암시하는 주제는?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"D장조\",\n",
      "              \"answer_start\": 409\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"6566518-2-1\",\n",
      "          \"question\": \"제2주제의 축소된 재현부의 조성은?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"4/4박자\",\n",
      "              \"answer_start\": 35\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"5917067-2-0\",\n",
      "          \"question\": \"곡이 시작할때의 박자는?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"고뇌와 갈망 동기, 청춘의 사랑 동기\",\n",
      "              \"answer_start\": 115\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"5917067-2-1\",\n",
      "          \"question\": \"이 곡의 주요 주제는?\"\n",
      "        },\n",
      "        {\n",
      "          \"answers\": [\n",
      "            {\n",
      "              \"text\": \"D장조\",\n",
      "              \"answer_start\": 409\n",
      "            }\n",
      "          ],\n",
      "          \"id\": \"5917067-2-2\",\n",
      "          \"question\": \"제 2주제에선 무슨 장조로 재현되는가?\"\n",
      "        }\n",
      "      ],\n",
      "      \"context\": \"이 작품은 라단조, Sehr gehalten(아주 신중하게), 4/4박자의 부드러운 서주로 서주로 시작되는데, 여기에는 주요 주제, 동기의 대부분이 암시, 예고되어 있다. 첫 부분의 저음 주제는 주요 주제(고뇌와 갈망 동기, 청춘의 사랑 동기)를 암시하고 있으며, 제1바이올린으로 더욱 명확하게 나타난다. 또한 그것을 이어받는 동기도 중요한 역할을 한다. 여기에 새로운 소재가 더해진 뒤에 새로운 주제도 연주된다. 주요부는 Sehr bewegt(아주 격동적으로), 2/2박자의 자유로운 소나타 형식으로 매우 드라마틱한 구상과 유기적인 구성을 하고 있다. 여기에는 지금까지의 주제나 소재 외에도 오보에에 의한 선율과 제2주제를 떠올리게 하는 부차적인 주제가 더해지는데, 중간부에서는 약보3이 중심이 되고 제2주제는 축소된 재현부에서 D장조로 재현된다. 마지막에는 주요 주제를 회상하면서 조용히 마친다.\"\n",
      "    }\n",
      "  ],\n",
      "  \"title\": \"파우스트_서곡\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# json 데이터 실제 형태 출력\n",
    "print(json.dumps(train_json[\"data\"][0], indent=2, ensure_ascii=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 데이터 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-1. 띄어쓰기 단위 정보관리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _is_whitespace(c):\n",
    "    if c == \" \" or c == \"\\t\" or c == \"\\r\" or c == \"\\n\" or ord(c) == 0x202F:\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('파우스트', '파우스트')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# whitespace가 2개인 경우를 처리해야 함\n",
    "string1 = '1839년 파우스트을 읽었다.'\n",
    "string2 = '1839년  파우스트을 읽었다.'\n",
    "string1[6:10], string2[7:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'1' : ['1'] : [0]\n",
      "'8' : ['18'] : [0, 0]\n",
      "'3' : ['183'] : [0, 0, 0]\n",
      "'9' : ['1839'] : [0, 0, 0, 0]\n",
      "'년' : ['1839년'] : [0, 0, 0, 0, 0]\n",
      "' ' : ['1839년'] : [0, 0, 0, 0, 0, 0]\n",
      "'파' : ['1839년', '파'] : [0, 0, 0, 0, 0, 0, 1]\n",
      "'우' : ['1839년', '파우'] : [0, 0, 0, 0, 0, 0, 1, 1]\n",
      "'스' : ['1839년', '파우스'] : [0, 0, 0, 0, 0, 0, 1, 1, 1]\n",
      "'트' : ['1839년', '파우스트'] : [0, 0, 0, 0, 0, 0, 1, 1, 1, 1]\n",
      "'을' : ['1839년', '파우스트을'] : [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]\n",
      "' ' : ['1839년', '파우스트을'] : [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]\n",
      "'읽' : ['1839년', '파우스트을', '읽'] : [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2]\n",
      "'었' : ['1839년', '파우스트을', '읽었'] : [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2]\n",
      "'다' : ['1839년', '파우스트을', '읽었다'] : [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2]\n",
      "'.' : ['1839년', '파우스트을', '읽었다.'] : [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2]\n"
     ]
    }
   ],
   "source": [
    "word_tokens = []\n",
    "char_to_word = []\n",
    "prev_is_whitespace = True\n",
    "\n",
    "# 첫번째 문장(string1)에 대해 띄어쓰기 영역 정보를 표시\n",
    "for c in string1:\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",
    "    print(f'\\'{c}\\' : {word_tokens} : {char_to_word}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'1' : ['1'] : [0]\n",
      "'8' : ['18'] : [0, 0]\n",
      "'3' : ['183'] : [0, 0, 0]\n",
      "'9' : ['1839'] : [0, 0, 0, 0]\n",
      "'년' : ['1839년'] : [0, 0, 0, 0, 0]\n",
      "' ' : ['1839년'] : [0, 0, 0, 0, 0, 0]\n",
      "' ' : ['1839년'] : [0, 0, 0, 0, 0, 0, 0]\n",
      "'파' : ['1839년', '파'] : [0, 0, 0, 0, 0, 0, 0, 1]\n",
      "'우' : ['1839년', '파우'] : [0, 0, 0, 0, 0, 0, 0, 1, 1]\n",
      "'스' : ['1839년', '파우스'] : [0, 0, 0, 0, 0, 0, 0, 1, 1, 1]\n",
      "'트' : ['1839년', '파우스트'] : [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]\n",
      "'을' : ['1839년', '파우스트을'] : [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]\n",
      "' ' : ['1839년', '파우스트을'] : [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]\n",
      "'읽' : ['1839년', '파우스트을', '읽'] : [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2]\n",
      "'었' : ['1839년', '파우스트을', '읽었'] : [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2]\n",
      "'다' : ['1839년', '파우스트을', '읽었다'] : [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2]\n",
      "'.' : ['1839년', '파우스트을', '읽었다.'] : [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2]\n"
     ]
    }
   ],
   "source": [
    "word_tokens = []\n",
    "char_to_word = []\n",
    "prev_is_whitespace = True\n",
    "\n",
    "# 두번째 문장(string2)에 대해 띄어쓰기 영역 정보를 표시\n",
    "for c in string2:\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",
    "    print(f'\\'{c}\\' : {word_tokens} : {char_to_word}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 위 기능을 함수로 정의\n",
    "def _tokenize_whitespace(string):\n",
    "    word_tokens = []\n",
    "    char_to_word = []\n",
    "    prev_is_whitespace = True\n",
    "\n",
    "    for c in string:\n",
    "        if _is_whitespace(c):  # 현재 문자가 빈칸, 줄바꿈 등인지 체크한다.\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",
   "metadata": {},
   "source": [
    "위와 같은 방법으로 띄어쓰기 단위로 token을 정리한 후, word token 영역별로 유니크한 숫자(어절 번호)를 부여한다. SQuAD 유형의 문제를 풀 때 글자 혹은 subword 단위로 token이 분리되는 것에 대비해서 **원래 데이터가 띄어쓰기 단위로 어떠했었는지 word token 영역별로 추가 정보를 관리**하면 도움이 된다. 아래와 같이 글자별로 word_token 영역을 표시해 주는 char_to_word list를 관리해 둔다. 이 값은 현재 글자가 몇 번째 어절에 포함된 것이었는지를 말해준다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'1' : 0\n",
      "'8' : 0\n",
      "'3' : 0\n",
      "'9' : 0\n",
      "'년' : 0\n",
      "' ' : 0\n",
      "'파' : 1\n",
      "'우' : 1\n",
      "'스' : 1\n",
      "'트' : 1\n",
      "'을' : 1\n",
      "' ' : 1\n",
      "'읽' : 2\n",
      "'었' : 2\n",
      "'다' : 2\n",
      "'.' : 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(['1839년', '파우스트을', '읽었다.'], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 첫번째 문장(string1)에 대해 띄어쓰기 영역 정보를 표시\n",
    "word_tokens, char_to_word = _tokenize_whitespace(string1)\n",
    "for c, i in zip(list(string1), char_to_word):\n",
    "    print(f'\\'{c}\\' : {i}')\n",
    "\n",
    "word_tokens, char_to_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'1' : 0\n",
      "'8' : 0\n",
      "'3' : 0\n",
      "'9' : 0\n",
      "'년' : 0\n",
      "' ' : 0\n",
      "' ' : 0\n",
      "'파' : 1\n",
      "'우' : 1\n",
      "'스' : 1\n",
      "'트' : 1\n",
      "'을' : 1\n",
      "' ' : 1\n",
      "'읽' : 2\n",
      "'었' : 2\n",
      "'다' : 2\n",
      "'.' : 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(['1839년', '파우스트을', '읽었다.'],\n",
       " [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 두번째 문장(string2)에 대해 띄어쓰기 영역 정보를 표시\n",
    "word_tokens, char_to_word = _tokenize_whitespace(string2)\n",
    "for c, i in zip(list(string2), char_to_word):\n",
    "    print(f'\\'{c}\\' : {i}')\n",
    "\n",
    "word_tokens, char_to_word"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-2. Tokenize by Vocab\n",
    "**Subword Segmentation**  \n",
    "- ex) 읽다, 읽었다, 읽어라, 읽고, 읽으려면, 읽다가 ... → 읽 + 었다  \n",
    "- SentencePiece모델을 이용해 Subword 기반의 텍스트 전처리 진행\n",
    "\n",
    "SentencePiece  \n",
    "- 구글 오픈소스 모델\n",
    "- WordPiece 등 다른 모델들을 통합하여 제공\n",
    "- 언어마다 다른 문법규칙을 활용하지 않고, 적절한 Subword 분절 규칙을 학습하거나, 혹은 자주 사용되는 구문을 하나의 단어로 묶어내는 등 통계적인 방법을 사용  \n",
    "\n",
    "koNLPy  \n",
    "- 한국어 형태소분석기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['▁1839', '년', '▁', '파우스트', '을', '▁읽', '었다', '.'], [0, 2, 5])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vocab loading\n",
    "vocab = spm.SentencePieceProcessor()\n",
    "vocab.load(f\"{model_dir}/ko_32000.model\")\n",
    "\n",
    "# word를 subword로 변경하면서 index 저장\n",
    "word_to_token = []\n",
    "context_tokens = []\n",
    "for (i, word) in enumerate(word_tokens):\n",
    "    word_to_token.append(len(context_tokens))\n",
    "    tokens = vocab.encode_as_pieces(word)  # SentencePiece를 사용해 Subword로 쪼갭니다.\n",
    "    for token in tokens:\n",
    "        context_tokens.append(token)\n",
    "\n",
    "context_tokens, word_to_token"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- '_' 표시는 앞부분이 공백이라는 뜻이다.\n",
    "- word_to_token의 [0, 2, 5]란 context_tokens에 쪼개져 담긴 0번, 2번, 5번 토큰인 '▁1839', '▁', '▁읽' 이 어절단위의 첫번째 토큰이 된다는 정보를 담아둔 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 위 기능을 함수로 정의 \n",
    "def _tokenize_vocab(vocab, context_words):\n",
    "    word_to_token = []\n",
    "    context_tokens = []\n",
    "    for (i, word) in enumerate(context_words):\n",
    "        word_to_token.append(len(context_tokens))\n",
    "        tokens = vocab.encode_as_pieces(word)\n",
    "        for token in tokens:\n",
    "            context_tokens.append(token)\n",
    "    return context_tokens, word_to_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1839년', '파우스트을', '읽었다.']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(['▁1839', '년', '▁', '파우스트', '을', '▁읽', '었다', '.'], [0, 2, 5])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(word_tokens)  # 처리해야 할 word 단위 입력\n",
    "\n",
    "context_tokens, word_to_token = _tokenize_vocab(vocab, word_tokens)\n",
    "context_tokens, word_to_token   # Subword 단위로 토큰화한 결과"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-3. Improve Span\n",
    "context(지문), question(질문), answer(정답) 뽑아내기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1839년', '바그너는', '괴테의', '파우스트을', '처음', '읽고', '그', '내용에', '마음이', '끌려', '이를', '소재로', '해서', '하나의', '교향곡을', '쓰려는', '뜻을', '갖는다.', '이', '시기']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3],\n",
       " '1839년 바그너는 괴테의 파우스트을')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# context를 띄어쓰기(word) 단위로 토큰화한 결과 \n",
    "word_tokens, char_to_word = _tokenize_whitespace(context)\n",
    "print( word_tokens[:20])\n",
    "char_to_word[:20], context[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ['▁1839', '년']\n",
      "2 ['▁바그너', '는']\n",
      "4 ['▁괴테', '의']\n",
      "6 ['▁', '파우스트', '을']\n",
      "9 ['▁처음']\n",
      "10 ['▁읽고']\n",
      "11 ['▁그']\n",
      "12 ['▁내용에']\n",
      "13 ['▁마음이']\n",
      "14 ['▁끌려']\n",
      "15 ['▁이를']\n",
      "16 ['▁소재로']\n",
      "17 ['▁해서']\n",
      "18 ['▁하나의']\n",
      "19 ['▁교향곡', '을']\n",
      "21 ['▁쓰', '려는']\n",
      "23 ['▁뜻을']\n",
      "24 ['▁갖는다', '.']\n",
      "26 ['▁이']\n",
      "27 ['▁시기']\n"
     ]
    }
   ],
   "source": [
    "# 띄어쓰기(word) 단위로 쪼개진 context(word_tokens)를 Subword로 토큰화한 결과 \n",
    "context_tokens, word_to_token = _tokenize_vocab(vocab, word_tokens)\n",
    "for i in range(min(20, len(word_to_token) - 1)):\n",
    "    print(word_to_token[i], context_tokens[word_to_token[i]:word_to_token[i + 1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "위에서 context에 포함된 answer의 시작인덱스(answer_start)와 종료인덱스(answer_end)를 구했다. 이 위치를 어절(word) 단위로 변환해본다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14, '교향곡', ['교향곡을'])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# answer_start와 answer_end로부터 word_start와 word_end를 구합니다. \n",
    "word_start = char_to_word[answer_start]\n",
    "word_end = char_to_word[answer_end]\n",
    "word_start, word_end, answer_text, word_tokens[word_start:word_end + 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 20, ['▁교향곡', '을'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# word_start로부터 word_end까지의 context를 Subword 단위로 토큰화한 결과\n",
    "token_start = word_to_token[word_start]\n",
    "if word_end < len(word_to_token) - 1:\n",
    "    token_end = word_to_token[word_end + 1] - 1\n",
    "else:\n",
    "    token_end = len(context_tokens) - 1\n",
    "token_start, token_end, context_tokens[token_start:token_end + 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'▁교향곡'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 실제 정답인 answer_text도 Subword 기준으로 토큰화해 둡니다. \n",
    "token_answer = \" \".join(vocab.encode_as_pieces(answer_text))\n",
    "token_answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X >> (19, 20) ▁교향곡 을\n",
      "O >> (19, 19) ▁교향곡\n",
      "X >> (20, 20) 을\n"
     ]
    }
   ],
   "source": [
    "# 정답 토큰 범위를 찾는 코드\n",
    "# 정답이 될수 있는 new_start와 new_end의 경우를 순회탐색합니다. \n",
    "for new_start in range(token_start, token_end + 1):\n",
    "    for new_end in range(token_end, new_start - 1, -1):\n",
    "        text_span = \" \".join(context_tokens[new_start : (new_end + 1)])\n",
    "        if text_span == token_answer:   # 정답과 일치하는 경우\n",
    "            print(\"O >>\", (new_start, new_end), text_span)\n",
    "        else:\n",
    "            print(\"X >>\", (new_start, new_end), text_span)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 위 기능을 함수로 정의\n",
    "# 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": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "token_start: 19  token_end: 19\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['▁교향곡']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_start, token_end = _improve_span(vocab, context_tokens, token_start, token_end, answer_text)\n",
    "print('token_start:', token_start, ' token_end:', token_end)\n",
    "context_tokens[token_start:token_end + 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-4. 데이터셋 분리\n",
    "train 데이터셋, dev 데이터셋을 분리하여, 위에서 작성한 `_improve_span()` 함수를 이용해 전처리 후 파일로 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dump_korquad(vocab, json_data, out_file):\n",
    "    with open(out_file, \"w\") as f:\n",
    "        for data in tqdm(json_data[\"data\"]):\n",
    "            title = data[\"title\"]\n",
    "            for paragraph in data[\"paragraphs\"]:\n",
    "                context = paragraph[\"context\"]\n",
    "                context_words, char_to_word = _tokenize_whitespace(context)\n",
    "\n",
    "                for qa in paragraph[\"qas\"]:\n",
    "                    assert len(qa[\"answers\"]) == 1\n",
    "                    qa_id = qa[\"id\"]\n",
    "                    question = qa[\"question\"]\n",
    "                    answer_text = qa[\"answers\"][0][\"text\"]\n",
    "                    answer_start = qa[\"answers\"][0][\"answer_start\"]\n",
    "                    answer_end = answer_start + len(answer_text) - 1\n",
    "\n",
    "                    assert answer_text == context[answer_start:answer_end + 1]\n",
    "\n",
    "                    word_start = char_to_word[answer_start]\n",
    "                    word_end = char_to_word[answer_end]\n",
    "\n",
    "                    word_answer = \" \".join(context_words[word_start:word_end + 1])\n",
    "                    char_answer = \" \".join(answer_text.strip().split())\n",
    "                    assert char_answer in word_answer\n",
    "\n",
    "                    context_tokens, word_to_token = _tokenize_vocab(vocab, context_words)\n",
    "\n",
    "                    token_start = word_to_token[word_start]\n",
    "                    if word_end < len(word_to_token) - 1:\n",
    "                        token_end = word_to_token[word_end + 1] - 1\n",
    "                    else:\n",
    "                        token_end = len(context_tokens) - 1\n",
    "\n",
    "                    token_start, token_end = _improve_span(vocab, context_tokens, token_start, token_end, char_answer)\n",
    "\n",
    "                    data = {\"qa_id\": qa_id, \"title\": title, \"question\": vocab.encode_as_pieces(question), \"context\": context_tokens, \"answer\": char_answer, \"token_start\": token_start, \"token_end\":token_end}\n",
    "                    f.write(json.dumps(data, ensure_ascii=False))\n",
    "                    f.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전처리를 수행하여 파일로 생성\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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "{\"qa_id\": \"5917067-0-1\", \"title\": \"파우스트_서곡\", \"question\": [\"▁바그너\", \"가\", \"▁처음\", \"▁교향곡\", \"▁작곡\", \"을\", \"▁한\", \"▁장소\", \"는\", \"?\"], \"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\": \"파리\", \"token_start\": 165, \"token_end\": 165}\n",
      "{\"qa_id\": \"5917067-0-2\", \"title\": \"파우스트_서곡\", \"question\": [\"▁바그너\", \"의\", \"▁1\", \"악장\", \"의\", \"▁초연\", \"은\", \"▁어디서\", \"▁연주\", \"되었\", \"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\": \"드레스덴\", \"token_start\": 216, \"token_end\": 216}\n",
      "{\"qa_id\": \"6566495-1-0\", \"title\": \"파우스트_서곡\", \"question\": [\"▁바그너\", \"의\", \"▁작품을\", \"▁시인\", \"의\", \"▁피로\", \"▁쓰여\", \"졌다\", \"고\", \"▁극찬\", \"한\", \"▁것은\", \"▁누구\", \"인\", \"가\", \"?\"], \"context\": [\"▁한편\", \"▁1840\", \"년부터\", \"▁바그너\", \"와\", \"▁알고\", \"▁지내던\", \"▁리스트\", \"가\", \"▁잊\", \"혀\", \"져\", \"▁있던\", \"▁1\", \"악장\", \"을\", \"▁부활\", \"시켜\", \"▁1852\", \"년에\", \"▁바이마르\", \"에서\", \"▁연주\", \"했다\", \".\", \"▁이것을\", \"▁계기로\", \"▁바그너\", \"도\", \"▁이\", \"▁작품에\", \"▁다시\", \"▁관심을\", \"▁갖게\", \"▁되었고\", \",\", \"▁그\", \"▁해\", \"▁9\", \"월에는\", \"▁총\", \"보\", \"의\", \"▁반환\", \"을\", \"▁요구\", \"하여\", \"▁이를\", \"▁서\", \"곡으로\", \"▁간\", \"추\", \"린\", \"▁다음\", \"▁수정\", \"을\", \"▁했고\", \"▁브\", \"라이트\", \"코프\", \"흐\", \"▁&\", \"▁헤르\", \"텔\", \"▁출판사\", \"에서\", \"▁출판\", \"할\", \"▁개정\", \"판\", \"도\", \"▁준비\", \"했다\", \".\", \"▁1853\", \"년\", \"▁5\", \"월에는\", \"▁리스트\", \"가\", \"▁이\", \"▁작품이\", \"▁수정\", \"되었다\", \"는\", \"▁것을\", \"▁인정\", \"했지만\", \",\", \"▁끝내\", \"▁바그너\", \"의\", \"▁출판\", \"▁계획은\", \"▁무산\", \"되고\", \"▁말았다\", \".\", \"▁이후\", \"▁1855\", \"년에\", \"▁리스트\", \"가\", \"▁자신의\", \"▁작품\", \"▁\", \"파우스트\", \"▁교향곡\", \"을\", \"▁거의\", \"▁완성\", \"하여\", \"▁그\", \"▁사실을\", \"▁바그너\", \"에게\", \"▁알\", \"렸고\", \",\", \"▁바그너\", \"는\", \"▁다시\", \"▁개정된\", \"▁총\", \"보를\", \"▁리스트\", \"에게\", \"▁보내고\", \"▁브\", \"라이트\", \"코프\", \"흐\", \"▁&\", \"▁헤르\", \"텔\", \"▁출판사\", \"에는\", \"▁20\", \"루이\", \"의\", \"▁금\", \"을\", \"▁받고\", \"▁팔았다\", \".\", \"▁또한\", \"▁그의\", \"▁작품을\", \"▁“\", \"하나\", \"하나\", \"의\", \"▁음\", \"표\", \"가\", \"▁시인\", \"의\", \"▁피로\", \"▁쓰여\", \"졌다\", \"”\", \"며\", \"▁극찬\", \"했던\", \"▁한스\", \"▁폰\", \"▁\", \"뷜\", \"로\", \"가\", \"▁그것을\", \"▁피아노\", \"▁독주\", \"용으로\", \"▁편곡\", \"했는데\", \",\", \"▁리스트\", \"는\", \"▁그것을\", \"▁약간\", \"▁변형\", \"되었을\", \"▁뿐\", \"이라고\", \"▁지적했다\", \".\", \"▁이\", \"▁서\", \"곡\", \"의\", \"▁총\", \"보\", \"▁첫\", \"머리\", \"에는\", \"▁\", \"파우스트\", \"▁1\", \"부의\", \"▁내용\", \"▁중\", \"▁한\", \"▁구절\", \"을\", \"▁인용\", \"하고\", \"▁있다\", \".\"], \"answer\": \"한스 폰 뷜로\", \"token_start\": 164, \"token_end\": 168}\n",
      "{\"qa_id\": \"6566495-1-1\", \"title\": \"파우스트_서곡\", \"question\": [\"▁잊\", \"혀\", \"져\", \"▁있는\", \"▁\", \"파우스트\", \"▁서\", \"곡\", \"▁1\", \"악장\", \"을\", \"▁부활\", \"시킨\", \"▁것은\", \"▁누구\", \"인\", \"가\", \"?\"], \"context\": [\"▁한편\", \"▁1840\", \"년부터\", \"▁바그너\", \"와\", \"▁알고\", \"▁지내던\", \"▁리스트\", \"가\", \"▁잊\", \"혀\", \"져\", \"▁있던\", \"▁1\", \"악장\", \"을\", \"▁부활\", \"시켜\", \"▁1852\", \"년에\", \"▁바이마르\", \"에서\", \"▁연주\", \"했다\", \".\", \"▁이것을\", \"▁계기로\", \"▁바그너\", \"도\", \"▁이\", \"▁작품에\", \"▁다시\", \"▁관심을\", \"▁갖게\", \"▁되었고\", \",\", \"▁그\", \"▁해\", \"▁9\", \"월에는\", \"▁총\", \"보\", \"의\", \"▁반환\", \"을\", \"▁요구\", \"하여\", \"▁이를\", \"▁서\", \"곡으로\", \"▁간\", \"추\", \"린\", \"▁다음\", \"▁수정\", \"을\", \"▁했고\", \"▁브\", \"라이트\", \"코프\", \"흐\", \"▁&\", \"▁헤르\", \"텔\", \"▁출판사\", \"에서\", \"▁출판\", \"할\", \"▁개정\", \"판\", \"도\", \"▁준비\", \"했다\", \".\", \"▁1853\", \"년\", \"▁5\", \"월에는\", \"▁리스트\", \"가\", \"▁이\", \"▁작품이\", \"▁수정\", \"되었다\", \"는\", \"▁것을\", \"▁인정\", \"했지만\", \",\", \"▁끝내\", \"▁바그너\", \"의\", \"▁출판\", \"▁계획은\", \"▁무산\", \"되고\", \"▁말았다\", \".\", \"▁이후\", \"▁1855\", \"년에\", \"▁리스트\", \"가\", \"▁자신의\", \"▁작품\", \"▁\", \"파우스트\", \"▁교향곡\", \"을\", \"▁거의\", \"▁완성\", \"하여\", \"▁그\", \"▁사실을\", \"▁바그너\", \"에게\", \"▁알\", \"렸고\", \",\", \"▁바그너\", \"는\", \"▁다시\", \"▁개정된\", \"▁총\", \"보를\", \"▁리스트\", \"에게\", \"▁보내고\", \"▁브\", \"라이트\", \"코프\", \"흐\", \"▁&\", \"▁헤르\", \"텔\", \"▁출판사\", \"에는\", \"▁20\", \"루이\", \"의\", \"▁금\", \"을\", \"▁받고\", \"▁팔았다\", \".\", \"▁또한\", \"▁그의\", \"▁작품을\", \"▁“\", \"하나\", \"하나\", \"의\", \"▁음\", \"표\", \"가\", \"▁시인\", \"의\", \"▁피로\", \"▁쓰여\", \"졌다\", \"”\", \"며\", \"▁극찬\", \"했던\", \"▁한스\", \"▁폰\", \"▁\", \"뷜\", \"로\", \"가\", \"▁그것을\", \"▁피아노\", \"▁독주\", \"용으로\", \"▁편곡\", \"했는데\", \",\", \"▁리스트\", \"는\", \"▁그것을\", \"▁약간\", \"▁변형\", \"되었을\", \"▁뿐\", \"이라고\", \"▁지적했다\", \".\", \"▁이\", \"▁서\", \"곡\", \"의\", \"▁총\", \"보\", \"▁첫\", \"머리\", \"에는\", \"▁\", \"파우스트\", \"▁1\", \"부의\", \"▁내용\", \"▁중\", \"▁한\", \"▁구절\", \"을\", \"▁인용\", \"하고\", \"▁있다\", \".\"], \"answer\": \"리스트\", \"token_start\": 7, \"token_end\": 7}\n"
     ]
    }
   ],
   "source": [
    "# 제대로 되었는지 확인\n",
    "def print_file(filename, count=10):\n",
    "    \"\"\"\n",
    "    파일 내용 출력\n",
    "    :param filename: 파일 이름\n",
    "    :param count: 출력 라인 수\n",
    "    \"\"\"\n",
    "    with open(filename) as f:\n",
    "        for i, line in enumerate(f):\n",
    "            if count <= i:\n",
    "                break\n",
    "            print(line.strip())\n",
    "\n",
    "print_file(f\"{data_dir}/korquad_train.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-5. 데이터 분석 : Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "전체 데이터에서 question 항목의 길이 분포 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 ['▁바그너', '는', '▁괴테', '의', '▁', '파우스트', '를', '▁읽고', '▁무엇을', '▁쓰고', '자', '▁', '했', '는', '가', '?']\n",
      "168 ['▁바그너', '는', '▁교향곡', '▁작곡', '을', '▁어디', '까지', '▁쓴', '▁뒤에', '▁중단', '했', '는', '가', '?']\n",
      "80 ['▁바그너', '가', '▁', '파우스트', '▁서', '곡을', '▁쓸', '▁때', '▁어떤', '▁곡', '의', '▁영향을', '▁받았', '는', '가', '?']\n",
      "6 ['▁1839', '년', '▁바그너', '가', '▁교향곡', '의', '▁소재로', '▁쓰', '려고', '▁했던', '▁책은', '?']\n",
      "143 ['▁', '파우스트', '▁서', '곡', '의', '▁라', '단', '조', '▁조성', '이', '▁영향을', '▁받은', '▁베토벤', '의', '▁곡은', '?']\n",
      "0 ['▁바그너', '가', '▁', '파우스트', '를', '▁처음으로', '▁읽', '은', '▁', '년', '도', '는', '?']\n",
      "165 ['▁바그너', '가', '▁처음', '▁교향곡', '▁작곡', '을', '▁한', '▁장소', '는', '?']\n",
      "216 ['▁바그너', '의', '▁1', '악장', '의', '▁초연', '은', '▁어디서', '▁연주', '되었', '는', '가', '?']\n",
      "164 ['▁바그너', '의', '▁작품을', '▁시인', '의', '▁피로', '▁쓰여', '졌다', '고', '▁극찬', '한', '▁것은', '▁누구', '인', '가', '?']\n",
      "7 ['▁잊', '혀', '져', '▁있는', '▁', '파우스트', '▁서', '곡', '▁1', '악장', '을', '▁부활', '시킨', '▁것은', '▁누구', '인', '가', '?']\n"
     ]
    }
   ],
   "source": [
    "questions = []\n",
    "contexts = []\n",
    "token_starts = []\n",
    "with open(f\"{data_dir}/korquad_train.json\") as f:\n",
    "    for i, line in enumerate(f):\n",
    "        data = json.loads(line)\n",
    "        questions.append(data[\"question\"])\n",
    "        contexts.append(data[\"context\"])\n",
    "        token_starts.append(data[\"token_start\"])\n",
    "        if i < 10:\n",
    "            print(data[\"token_start\"], data[\"question\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16, 14, 16, 12, 16, 13, 10, 13, 16, 18]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# token count\n",
    "train_question_counts = [len(question) for question in questions]\n",
    "train_question_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "# 그래프에 대한 이미지 사이즈 선언\n",
    "# figsize: (가로, 세로) 형태의 튜플로 입력\n",
    "plt.figure(figsize=(8, 4))\n",
    "# histogram 선언\n",
    "# bins: 히스토그램 값들에 대한 버켓 범위, \n",
    "# range: x축 값의 범위\n",
    "# facecolor: 그래프 색상\n",
    "# label: 그래프에 대한 라벨\n",
    "plt.hist(train_question_counts, bins=100, range=[0, 100], facecolor='b', label='train')\n",
    "# 그래프 제목\n",
    "plt.title('Count of question')\n",
    "# 그래프 x 축 라벨\n",
    "plt.xlabel('Number of question')\n",
    "# 그래프 y 축 라벨\n",
    "plt.ylabel('Count of question')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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",
    "# https://ko.wikipedia.org/wiki/%EB%B0%B1%EB%B6%84%EC%9C%84%EC%88%98\n",
    "# 백분위수(Percentile)는 크기가 있는 값들로 이뤄진 자료를 순서대로 나열했을 때 백분율로 나타낸 특정 위치의 값을 이르는 용어이다.\n",
    "# 일반적으로 크기가 작은 것부터 나열하여 가장 작은 것을 0, 가장 큰 것을 100으로 한다.\n",
    "# 100개의 값을 가진 어떤 자료의 20 백분위수는 그 자료의 값들 중 20번째로 작은 값을 뜻한다. 50 백분위수는 중앙값과 같다.\n",
    "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": "code",
   "execution_count": 34,
   "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",
    "# 박스플롯 생성\n",
    "# 첫번째 파라메터: 여러 분포에 대한 데이터 리스트를\n",
    "# labels: 입력한 데이터에 대한 라벨\n",
    "# showmeans: 평균값을 표현\n",
    "# 참고: https://leebaro.tistory.com/entry/%EB%B0%95%EC%8A%A4-%ED%94%8C%EB%A1%AFbox-plot-%EC%84%A4%EB%AA%85\n",
    "plt.boxplot(train_question_counts, labels=['token counts'], showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-6. 데이터 분석 : Context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[278, 278, 278, 278, 278, 278, 278, 278, 209, 209]"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "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": [
    "# 그래프에 대한 이미지 사이즈 선언\n",
    "# figsize: (가로, 세로) 형태의 튜플로 입력\n",
    "plt.figure(figsize=(8, 4))\n",
    "# histogram 선언\n",
    "# bins: 히스토그램 값들에 대한 버켓 범위, \n",
    "# range: x축 값의 범위\n",
    "# facecolor: 그래프 색상\n",
    "# label: 그래프에 대한 라벨\n",
    "plt.hist(train_context_counts, bins=900, range=[100, 1000], facecolor='r', label='train')\n",
    "# 그래프 제목\n",
    "plt.title('Count of context')\n",
    "# 그래프 x 축 라벨\n",
    "plt.xlabel('Number of context')\n",
    "# 그래프 y 축 라벨\n",
    "plt.ylabel('Count of context')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
    "# https://ko.wikipedia.org/wiki/%EB%B0%B1%EB%B6%84%EC%9C%84%EC%88%98\n",
    "# 백분위수(Percentile)는 크기가 있는 값들로 이뤄진 자료를 순서대로 나열했을 때 백분율로 나타낸 특정 위치의 값을 이르는 용어이다.\n",
    "# 일반적으로 크기가 작은 것부터 나열하여 가장 작은 것을 0, 가장 큰 것을 100으로 한다.\n",
    "# 100개의 값을 가진 어떤 자료의 20 백분위수는 그 자료의 값들 중 20번째로 작은 값을 뜻한다. 50 백분위수는 중앙값과 같다.\n",
    "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": 39,
   "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",
    "# 박스플롯 생성\n",
    "# 첫번째 파라메터: 여러 분포에 대한 데이터 리스트를\n",
    "# labels: 입력한 데이터에 대한 라벨\n",
    "# showmeans: 평균값을 표현\n",
    "# 참고: https://leebaro.tistory.com/entry/%EB%B0%95%EC%8A%A4-%ED%94%8C%EB%A1%AFbox-plot-%EC%84%A4%EB%AA%85\n",
    "plt.boxplot(train_context_counts, labels=['token counts'], showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-7. 데이터 분석 : Answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[19, 168, 80, 6, 143, 0, 165, 216, 164, 7]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# token count\n",
    "train_answer_starts = token_starts\n",
    "train_answer_starts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "# 그래프에 대한 이미지 사이즈 선언\n",
    "# figsize: (가로, 세로) 형태의 튜플로 입력\n",
    "plt.figure(figsize=(8, 4))\n",
    "# histogram 선언\n",
    "# bins: 히스토그램 값들에 대한 버켓 범위, \n",
    "# range: x축 값의 범위\n",
    "# facecolor: 그래프 색상\n",
    "# label: 그래프에 대한 라벨\n",
    "plt.hist(train_answer_starts, bins=500, range=[0, 500], facecolor='g', label='train')\n",
    "# 그래프 제목\n",
    "plt.title('Count of answer')\n",
    "# 그래프 x 축 라벨\n",
    "plt.xlabel('Number of answer')\n",
    "# 그래프 y 축 라벨\n",
    "plt.ylabel('Count of answer')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
    "# https://ko.wikipedia.org/wiki/%EB%B0%B1%EB%B6%84%EC%9C%84%EC%88%98\n",
    "# 백분위수(Percentile)는 크기가 있는 값들로 이뤄진 자료를 순서대로 나열했을 때 백분율로 나타낸 특정 위치의 값을 이르는 용어이다.\n",
    "# 일반적으로 크기가 작은 것부터 나열하여 가장 작은 것을 0, 가장 큰 것을 100으로 한다.\n",
    "# 100개의 값을 가진 어떤 자료의 20 백분위수는 그 자료의 값들 중 20번째로 작은 값을 뜻한다. 50 백분위수는 중앙값과 같다.\n",
    "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": 43,
   "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",
    "# 박스플롯 생성\n",
    "# 첫번째 파라메터: 여러 분포에 대한 데이터 리스트를\n",
    "# labels: 입력한 데이터에 대한 라벨\n",
    "# showmeans: 평균값을 표현\n",
    "# 참고: https://leebaro.tistory.com/entry/%EB%B0%95%EC%8A%A4-%ED%94%8C%EB%A1%AFbox-plot-%EC%84%A4%EB%AA%85\n",
    "plt.boxplot(train_answer_starts, labels=['token counts'], showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-8. 데이터 분석 : Word Cloud\n",
    "**워드 클라우드(Word Cloud)**  \n",
    "- 자료의 빈도수를 시각화해서 나타내는 방법\n",
    "- 문서의 핵심 단어를 한눈에 파악할 수 있고, 빅데이터를 분석할 때 데이터의 특징을 도출하기 위해서 활용\n",
    "- 빈도수가 높은 단어일수록 글씨 크기가 큰 특징이 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb715d0a88724273a09d01fc48be4817",
       "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": 44,
     "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)\n",
    "\n",
    "        for qa in paragraph[\"qas\"]:\n",
    "            assert len(qa[\"answers\"]) == 1\n",
    "            question = qa[\"question\"]\n",
    "            documents.append(question)\n",
    "\n",
    "documents[:10]   # 그중 맨 앞 10개만 확인해 봅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'파우스트_서곡 1839년 바그너는 괴테의 파우스트을 처음 읽고 그 내용에 마음이 끌려 이를 소재로 해서 하나의 교향곡을 쓰려는 뜻을 갖는다. 이 시기 바그너는 1838년에 빛 독촉으로 산전수전을 다 걲은 상황이라 좌절과 실망에 가득했으며 메피스토펠레스를 만나는 파우스트의 심경에 공감했다고 한다. 또한 파리에서 아브네크의 지휘로 파리 음악원 관현악단이 연주하는 베토벤의 교향곡 9번을 듣고 깊은 감명을 받았는데, 이것이 이듬해 1월에 파우스트의 서곡으로 쓰여진 이 작품에 조금이라도 영향을 끼쳤으리라는 것은 의심할 여지가 없다. 여기의 라단조 조성의 경우에도 그의 전기에 적혀 있는 것처럼 단순한 정신적 피로나 실의가 반영된 것이 아니라 베토벤의 합창교향곡 조성의 영향을 받은 것을 볼 수 있다. 그렇게 교향곡 작곡을 1839년부터 40년에 걸쳐 파리에서 착수했으나 1악장을 쓴 뒤에 중단했다. 또한 작품의 완성과 동시에 그는 이 서곡(1악장)을 파리 음악원의 연주회에서 연주할 파트보까지 준비하였으나, 실제로는 이루어지지는 않았다. 결국 초연은 4년 반이 지난 후에 드레스덴에서 연주되었고 재연도 이루어졌지만, 이후에 그대로 방치되고 말았다. 그 사이에 그는 리엔치와 방황하는 네덜란드인을 완성하고 탄호이저에도 착수하는 등 분주한 시간을 보냈는데, 그런 바쁜 생활이 이 곡을 잊게 한 것이 아닌가 하는 의견도 있다. 바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가? 바그너는 교향곡 작곡을 어디까지 쓴 뒤에 중단했는가? 바그너가 파우스트 서곡을 쓸 때 어떤 곡의 영향을 받았는가? 1839년 바그너가 교향곡의 소재로 쓰려고 했던 책은? 파우스트 서곡의 라단조 조성이 영향을 받은 베토벤의 곡은? 바그너가 파우스트를 처음으로 읽은 년도는? 바그너가 처음 교향곡 작곡을 한 장소는? 바그너의 1악장의 초연은 어디서 연주되었는가?'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# documents를 전부 이어 하나의 문장으로 만들면 이렇게 보입니다. \n",
    "\" \".join(documents[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
    "# https://datascienceschool.net/view-notebook/6e71dbff254542d9b0a054a7c98b34ec/\n",
    "# image 출력, interpolation 이미지 시각화 옵션\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-9. 데이터 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_seq_length': 384, 'max_query_length': 64}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Config(dict):\n",
    "    \"\"\"\n",
    "    json을 config 형태로 사용하기 위한 Class\n",
    "    :param dict: config dictionary\n",
    "    \"\"\"\n",
    "    __getattr__ = dict.__getitem__\n",
    "    __setattr__ = dict.__setitem__\n",
    "\n",
    "\n",
    "args = Config({\n",
    "    'max_seq_length': 384,\n",
    "    'max_query_length': 64,\n",
    "})\n",
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 생성한 데이터셋 파일을 메모리에 로딩하는 함수\n",
    "def load_data(args, filename):\n",
    "    inputs, segments, labels_start, labels_end = [], [], [], []\n",
    "\n",
    "    n_discard = 0\n",
    "    with open(filename, \"r\") as f:\n",
    "        for i, line in enumerate(tqdm(f, desc=f\"Loading ...\")):\n",
    "            data = json.loads(line)\n",
    "            token_start = data.get(\"token_start\")\n",
    "            token_end = data.get(\"token_end\")\n",
    "            question = data[\"question\"][:args.max_query_length]\n",
    "            context = data[\"context\"]\n",
    "            answer_tokens = \" \".join(context[token_start:token_end + 1])\n",
    "            context_len = args.max_seq_length - len(question) - 3\n",
    "\n",
    "            if token_end >= context_len:\n",
    "                # 최대 길이내에 token이 들어가지 않은 경우 처리하지 않음\n",
    "                n_discard += 1\n",
    "                continue\n",
    "            context = context[:context_len]\n",
    "            assert len(question) + len(context) <= args.max_seq_length - 3\n",
    "\n",
    "            tokens = ['[CLS]'] + question + ['[SEP]'] + context + ['[SEP]']\n",
    "            ids = [vocab.piece_to_id(token) for token in tokens]\n",
    "            ids += [0] * (args.max_seq_length - len(ids))\n",
    "            inputs.append(ids)\n",
    "            segs = [0] * (len(question) + 2) + [1] * (len(context) + 1)\n",
    "            segs += [0] * (args.max_seq_length - len(segs))\n",
    "            segments.append(segs)\n",
    "            token_start += (len(question) + 2)\n",
    "            labels_start.append(token_start)\n",
    "            token_end += (len(question) + 2)\n",
    "            labels_end.append(token_end)\n",
    "    print(f'n_discard: {n_discard}')\n",
    "\n",
    "    return (np.array(inputs), np.array(segments)), (np.array(labels_start), np.array(labels_end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d54f27ab9d0a48a28a2e605d6f52cc2b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading ...: |          | 0/? [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": "2ea44eda47d34c7ea12b5edbd1617ef6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading ...: |          | 0/? [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": 50,
     "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,
   "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": "code",
   "execution_count": 52,
   "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": [
    "# Question을 0으로, Context를 1로 구분해 준 Segment 데이터 1번째\n",
    "train_inputs[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. LSTM\n",
    "- input 2개, output 2개인 모델\n",
    "- input : train_inputs[0], train_inputs[1]\n",
    "- input : 각각 Question + Context의 데이터와 Segment\n",
    "- output : Answer의 시작점과 끝 위치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model_lstm(n_vocab, n_seq, d_model):\n",
    "    tokens = tf.keras.layers.Input((None,), name='tokens')\n",
    "    segments = tf.keras.layers.Input((None,), name='segments')\n",
    "\n",
    "    hidden = tf.keras.layers.Embedding(n_vocab, d_model)(tokens) + tf.keras.layers.Embedding(2, d_model)(segments) # (bs, n_seq, d_model)\n",
    "\n",
    "    hidden = tf.keras.layers.LSTM(d_model, return_sequences=True)(hidden)  # (bs, n_seq, d_model)\n",
    "    hidden = tf.keras.layers.LSTM(d_model, return_sequences=True)(hidden)  # (bs, n_seq, d_model)\n",
    "    hidden = tf.keras.layers.Dense(2)(hidden) # (bs, n_seq, 2)\n",
    "    start_logits, end_logits = tf.split(hidden, 2, axis=-1)  # (bs, n_seq, 1), (bs, n_seq, 1)\n",
    "    start_logits = tf.squeeze(start_logits, axis=-1)  # (bs, n_seq)  차원 중 사이즈가 1인 것을 찾아 스칼라값으로 바꿔 해당 차원을 제거\n",
    "    start_outputs = tf.keras.layers.Softmax(name=\"start\")(start_logits)\n",
    "    end_logits = tf.squeeze(end_logits, axis=-1)  # (bs, n_seq)\n",
    "    end_outputs = tf.keras.layers.Softmax(name=\"end\")(end_logits)\n",
    "\n",
    "    model = tf.keras.Model(inputs=(tokens, segments), outputs=(start_outputs, end_outputs))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = build_model_lstm(n_vocab=len(vocab), n_seq=512, d_model=512)\n",
    "tf.keras.utils.plot_model(model, 'model.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=tf.keras.losses.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(learning_rate=5e-4), metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 9.1250 - start_loss: 4.4189 - end_loss: 4.7060 - start_accuracy: 0.0685 - end_accuracy: 0.0525\n",
      "Epoch 00001: val_start_accuracy improved from -inf to 0.09112, saving model to /home/ssac14/aiffel/bert_qna/data/korquad_lstm.hdf5\n",
      "469/469 [==============================] - 221s 472ms/step - loss: 9.1250 - start_loss: 4.4189 - end_loss: 4.7060 - start_accuracy: 0.0685 - end_accuracy: 0.0525 - val_loss: 8.2442 - val_start_loss: 3.9176 - val_end_loss: 4.3266 - val_start_accuracy: 0.0911 - val_end_accuracy: 0.0792\n",
      "Epoch 2/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 7.2974 - start_loss: 3.4786 - end_loss: 3.8188 - start_accuracy: 0.1254 - end_accuracy: 0.1167\n",
      "Epoch 00002: val_start_accuracy improved from 0.09112 to 0.09831, saving model to /home/ssac14/aiffel/bert_qna/data/korquad_lstm.hdf5\n",
      "469/469 [==============================] - 223s 476ms/step - loss: 7.2974 - start_loss: 3.4786 - end_loss: 3.8188 - start_accuracy: 0.1254 - end_accuracy: 0.1167 - val_loss: 8.1871 - val_start_loss: 3.8885 - val_end_loss: 4.2985 - val_start_accuracy: 0.0983 - val_end_accuracy: 0.0753\n",
      "Epoch 3/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 6.6994 - start_loss: 3.2164 - end_loss: 3.4830 - start_accuracy: 0.1554 - end_accuracy: 0.1487\n",
      "Epoch 00003: val_start_accuracy did not improve from 0.09831\n",
      "469/469 [==============================] - 223s 476ms/step - loss: 6.6994 - start_loss: 3.2164 - end_loss: 3.4830 - start_accuracy: 0.1554 - end_accuracy: 0.1487 - val_loss: 8.5525 - val_start_loss: 4.0708 - val_end_loss: 4.4817 - val_start_accuracy: 0.0973 - val_end_accuracy: 0.0797\n",
      "Epoch 4/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 6.2322 - start_loss: 3.0118 - end_loss: 3.2204 - start_accuracy: 0.1818 - end_accuracy: 0.1773\n",
      "Epoch 00004: val_start_accuracy did not improve from 0.09831\n",
      "469/469 [==============================] - 223s 474ms/step - loss: 6.2322 - start_loss: 3.0118 - end_loss: 3.2204 - start_accuracy: 0.1818 - end_accuracy: 0.1773 - val_loss: 8.9189 - val_start_loss: 4.2632 - val_end_loss: 4.6558 - val_start_accuracy: 0.0878 - val_end_accuracy: 0.0727\n",
      "Epoch 5/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 5.7181 - start_loss: 2.7885 - end_loss: 2.9297 - start_accuracy: 0.2185 - end_accuracy: 0.2151\n",
      "Epoch 00005: val_start_accuracy improved from 0.09831 to 0.10358, saving model to /home/ssac14/aiffel/bert_qna/data/korquad_lstm.hdf5\n",
      "469/469 [==============================] - 223s 475ms/step - loss: 5.7181 - start_loss: 2.7885 - end_loss: 2.9297 - start_accuracy: 0.2185 - end_accuracy: 0.2151 - val_loss: 8.9504 - val_start_loss: 4.3040 - val_end_loss: 4.6465 - val_start_accuracy: 0.1036 - val_end_accuracy: 0.1001\n",
      "Epoch 6/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 4.7721 - start_loss: 2.3420 - end_loss: 2.4300 - start_accuracy: 0.3162 - end_accuracy: 0.3133\n",
      "Epoch 00006: val_start_accuracy improved from 0.10358 to 0.13167, saving model to /home/ssac14/aiffel/bert_qna/data/korquad_lstm.hdf5\n",
      "469/469 [==============================] - 223s 475ms/step - loss: 4.7721 - start_loss: 2.3420 - end_loss: 2.4300 - start_accuracy: 0.3162 - end_accuracy: 0.3133 - val_loss: 9.6917 - val_start_loss: 4.5721 - val_end_loss: 5.1196 - val_start_accuracy: 0.1317 - val_end_accuracy: 0.1222\n",
      "Epoch 7/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 3.9746 - start_loss: 1.9490 - end_loss: 2.0256 - start_accuracy: 0.4074 - end_accuracy: 0.3989\n",
      "Epoch 00007: val_start_accuracy improved from 0.13167 to 0.14501, saving model to /home/ssac14/aiffel/bert_qna/data/korquad_lstm.hdf5\n",
      "469/469 [==============================] - 223s 475ms/step - loss: 3.9746 - start_loss: 1.9490 - end_loss: 2.0256 - start_accuracy: 0.4074 - end_accuracy: 0.3989 - val_loss: 10.6492 - val_start_loss: 5.0204 - val_end_loss: 5.6289 - val_start_accuracy: 0.1450 - val_end_accuracy: 0.1315\n",
      "Epoch 8/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 3.3180 - start_loss: 1.6338 - end_loss: 1.6842 - start_accuracy: 0.4837 - end_accuracy: 0.4797\n",
      "Epoch 00008: val_start_accuracy did not improve from 0.14501\n",
      "469/469 [==============================] - 224s 477ms/step - loss: 3.3180 - start_loss: 1.6338 - end_loss: 1.6842 - start_accuracy: 0.4837 - end_accuracy: 0.4797 - val_loss: 11.7045 - val_start_loss: 5.5489 - val_end_loss: 6.1556 - val_start_accuracy: 0.1441 - val_end_accuracy: 0.1401\n",
      "Epoch 9/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 2.7214 - start_loss: 1.3437 - end_loss: 1.3777 - start_accuracy: 0.5623 - end_accuracy: 0.5592\n",
      "Epoch 00009: val_start_accuracy did not improve from 0.14501\n",
      "469/469 [==============================] - 223s 475ms/step - loss: 2.7214 - start_loss: 1.3437 - end_loss: 1.3777 - start_accuracy: 0.5623 - end_accuracy: 0.5592 - val_loss: 12.8716 - val_start_loss: 6.1148 - val_end_loss: 6.7569 - val_start_accuracy: 0.1389 - val_end_accuracy: 0.1333\n",
      "Epoch 10/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 2.1800 - start_loss: 1.0764 - end_loss: 1.1036 - start_accuracy: 0.6456 - end_accuracy: 0.6403\n",
      "Epoch 00010: val_start_accuracy did not improve from 0.14501\n",
      "469/469 [==============================] - 223s 476ms/step - loss: 2.1800 - start_loss: 1.0764 - end_loss: 1.1036 - start_accuracy: 0.6456 - end_accuracy: 0.6403 - val_loss: 14.4052 - val_start_loss: 6.8503 - val_end_loss: 7.5548 - val_start_accuracy: 0.1441 - val_end_accuracy: 0.1345\n",
      "Epoch 11/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 1.7197 - start_loss: 0.8475 - end_loss: 0.8722 - start_accuracy: 0.7168 - end_accuracy: 0.7137\n",
      "Epoch 00011: val_start_accuracy did not improve from 0.14501\n",
      "469/469 [==============================] - 223s 475ms/step - loss: 1.7197 - start_loss: 0.8475 - end_loss: 0.8722 - start_accuracy: 0.7168 - end_accuracy: 0.7137 - val_loss: 16.0423 - val_start_loss: 7.7494 - val_end_loss: 8.2929 - val_start_accuracy: 0.1334 - val_end_accuracy: 0.1329\n",
      "Epoch 12/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 1.3657 - start_loss: 0.6711 - end_loss: 0.6947 - start_accuracy: 0.7754 - end_accuracy: 0.7692\n",
      "Epoch 00012: val_start_accuracy did not improve from 0.14501\n",
      "469/469 [==============================] - 223s 476ms/step - loss: 1.3657 - start_loss: 0.6711 - end_loss: 0.6947 - start_accuracy: 0.7754 - end_accuracy: 0.7692 - val_loss: 16.6785 - val_start_loss: 8.1622 - val_end_loss: 8.5162 - val_start_accuracy: 0.1348 - val_end_accuracy: 0.1308\n"
     ]
    }
   ],
   "source": [
    "# 약 1epoch당 2-3분 소요\n",
    "# early stopping\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_start_accuracy', patience=5)\n",
    "# save weights\n",
    "save_weights = tf.keras.callbacks.ModelCheckpoint(os.path.join(data_dir, \"korquad_lstm.hdf5\"), monitor='val_start_accuracy', verbose=1, save_best_only=True, mode='max', save_freq='epoch', save_weights_only=True)\n",
    "\n",
    "history = model.fit(train_inputs, train_labels, epochs=20, batch_size=128, validation_data=(dev_inputs, dev_labels), callbacks=[early_stopping, save_weights])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Iio3ipxd+okulLjZ9fmnrhC0tXryYuXPnMm7cOLu+jvxM05fwcOjeHRYuNFuiTJwISaPBbeZubd19ezq11r8qpQJSPNQB2AIEJRWgo4BZtokphLC71auhaVOz9cmLL0LXrlC3rtWphANZuHAhI0aMSL6fP39+pkyZYmGiO5sxYwa9evWyOoZI4xISE/hqw1d8uv5TSuYsyapOqyiXq5zVsYRFgoKCmDp1avL9ypUr3zRnPK2YMWMGffv2tTqGcCBLlkC3bnDxotkGpV8/cErFiZaPspCQH1AQaAl4A38opdZprcNvPVEp1RPoCcgkZyHSisWLoUQJs79minkjQjyoZs2a0axZM6tj3NXVq1dp1KgRRYsW5bPPPrM6jkjDzl45S4e5HVh5aCXty7dnTJMxeLk92OJZIn3q2bMnPXv2tDrGXR06dIiOHTvy3HPPUaVKFavjCAdw9aqZNfXjj1C+PKxcaT6mtkcpOhOAWVrra0C4Umo7phC9rejUWgcBQWCGYTxOUCHEY4qNBXd3+OYbuHzZ7LMpRDqUOXPmey4KIgTA70d/p+2ctpy7eo6gJkF0f6r7fRfMEsJqxYoVY9OmTVbHEA5ixw5o3x727oW33jKLBnl4WJPlUTpVNwJ1AZRSnkAF4F9bhhJC2Njs2WZV2iNHzFgKKTiFEBlUok5kyIYh1J5UG09XT/7s/ic9KveQglMIkW4kJMDQoVC1KkRGmt7NESOsKzjh0Xo65wA1lFIhQDzwmdb6jG1jCSFsZupU6NQJqlWDHDmsTiOEEJaJuBpBp3mdWHpgKYFlAwlqGkRWd7kIJ4RIP44dM2/71q+Hl16CsWMhZ06rUz1g0am1XgesS/o8EXjTbomEELYzaZJZKKhmTVi0CLxkrpIQImPafHwzgbMDOXvlLD82/pFX/V+V3k0hRLoybRq8+qrp6ZwwATp3hrTSzKXimkVCiFQ1f/6NlWmXLJGCUwiRIWmt+WbzN9SaWAs3Zzc2d9tMn6f7SMEphEg3IiOhQwdo1w7KlIG//4YuXdJOwQlSdAqRftWpA+++C7/9BpkzW51GOICJEycyYMCABz5/586drFq16qFe48iRI8yePfthownxSM5Hn6f59Ob0X9mfZqWa8VfPv6icr7LVsYTFHrate1Bjxozh008/tfnzCnEvJ05A9eowfTp8/jn8/jsUK2Z1qttJ0fkA7tU42avhEuKRzZgBV66YxYKGDbN21rhI16ToFGnZkYtHqPZLNZYdWMaoRqOY3Xo23h7eVscSQgibOXgQnn3WzONcuRIGDgSXR1mxJxWkrVgBAbc/9vLL0KeP2WSmcePbj3fpYm4REdCq1c3H1q2zfUYh0rKvv4YPPoAvv4QPP7Q6jbiLgDu0dS+//DJ9+vTh6tWrNL5DW9elSxe6dOlCREQErW5p69bdp617+eWXeffdd6lSpQpxcXFUrFiRmjVr8u+//xIZGUmTJk3uu5/l8uXL+eCDD/Dy8iJPnjy89tprfP3111y9epW///6bpUuX0rt379uec+LEicycORMwwxxPnTrFyZMnCQgIYMGCBXh731wE7Nu3jzfffJPY2FgiIyMZPXo0VatWZefOnbz77rvExcXh5ubGuHHjOHDgAIMGDcLZ2RlPT0+GDh1K9+7d2bJlCwADBgzAz8+PgIAAGjRoQNWqVTl69CizZs2iV69eREZGcvHiRT777DOaNWvG4cOHef3114mKisLZ2Zkvv/ySL774gqVLlwKmF+PcuXN89NFH9/xeCeuFng2lQXADouOjWdt5LTUK1bA6kuWUUn2B9oACvtVaz0hxbAjwTIrTKwBPaq2PPs5rBkwMuO2xl8u+TJ+n+3A17iqNp9yhravUhS6VuhBxNYJWM29p67qsu+fr2aKtu3r1Kq+99hqHDx8mNjaWt99+m9atWxMQEIC/vz87duwgPDyckSNHUqdOHX788UcmTZpEzpw58fT0pFy5cnd83k2bNvHJJ5+QmJjIlStXmD59OsWKFWPNmjU3tWMzZsxg8eLFjBw5Eg8PD3LlysWrr77KmDFjmD59OgBt2rShd+/eAHz44Yfkzp0bFxcXvvjiiwdqPzt27Mgff/zB6NGjAdNWli9fnvbt29/zeyPSln/+gYYNIS4O1q4Ff3+rE91b2io6U5ktGqeU5s+fz4gRI3B1dSVr1qz89NNPeHp60rJlS2JiYnB1dWXYsGG4uLjQo0cPMmfOjLe3N8HBwWTLls1+/1CR/mkNX3wBgwaZAf3vvWd1IpGGvP7664wdO5YqVaowb948mjVrRseOHfHz8+Po0aM8/fTT923rNm7cSPv27XnnnXc4evQohQoVYsCAAezbt4+vv/46+XXu9Jznzp1j48aNuLq6sm7dupvePN0qS5YsTJ8+HVdXV4KCgpg0aRJVq1YlMDCQWbNmUaFCBWJjY4mNjaV79+5s2LCB/Pnzc+nSJU6dOnXX/KdOnWLo0KHky5eP8+fPM3r0aLJnz86iRYsYM2YMzZo1o2vXrgwYMIBGjRqRmJhIZGQk4eHhnDx5knz58jFr1iwmTJjwiD8FkVo2H9/MC1NfILNrZjZ03UC5XHcuAjISpVRxoBtQDXAHtiqlVmitLwBorT9Ica4PsBA4ZkXWx2GLtm7YsGH4+fkxYcIEoqOjqVy5Ms2bNwfAy8uL1atXs2HDBoYNG0aBAgWYMGECmzZtwt3dnc6dO9/1efPmzcvixYu5du0an376KbNnz6Z37963tWMnT57kiy++YNOmTXh7exMREUFoaOhdn/fEiROsWLECLy8vTpw48cDt51dffUVMTAweHh4sW7aMTz755NG+6cISf/xh+uI8PWH1ajOPM61LW0Xnva7WZ8587+M+Pg/ds2mLxum6c+fOMWDAAEJCQvDy8mLhwoW8++67DBo0iMjISDZv3ozWmoiICJYuXcozzzzD999/z9mzZ3F1dX2o3ELcRGv4+GOz42+XLvDLL+DsbHUqh2HF1f979Uxmzpz5nsd9fHzu27N5q+eee44BAwYQFRXFuHHjCAoK4uOPPyZ37twUKVIEJ6f7z7QYNGgQ48eP5/XXX6dmzZoULlz4puMRERF8/fXXd3zOWrVqPXA7988///Drr79SpkwZIiMjuXr1KhEREbi5uVGhQgUA3N3d2bVrF2XLliV//vyAKVbPnz9/03MlJCQkf166dGny5csHwMGDB/nmm28oVaoUHh4eXL16FTC9rI0aNQLAycmJ7Nmz06NHD4KDg+ncuTMuLi4UKlTogf4dwhpL/ltCq5mtKJC1ACs6rqBItiJWR0or6gALtdbXgGtKqd+B6sDiO5z7JvC91lrfekAp1RPoCTzQ78K9eiYzu2a+53GfzD737dm8lS3aupCQEM6ePZs8wiExMZHTp08DJI8yKViwIBEREezcuZNnn30Wd3d3AKpVq8aZM3feRfD3339n9erVlClThosXL5I1a1b+/fff29qx5cuXU7t27eRRID4+PrctepWybatatSpeSQsFPmj76e7uzosvvsiCBQsoWrQo/v7+ZJa1HxzGypXQogXky2c+L1LE6kQPJkPP6XzuuefYt29fcuP06quv8vXXX/P++++zZMmSB2qcrjt48CB+fn7Jv/jPPfccO3fupGTJknz//fcMGDCAr776Ci8vL7p160aNGjXo168f8+bNk1908XjOnjWFZo8eMG6cFJwPIcXV/1pAfeBTpVT268e11h9orQO01gFAK2AfDnj1H6Bbt2589tlneHl5MXfuXEqXLs0333xD/fr1iYyMvO/XHz58mJ49e/L9998zcuRIdu/efdMboeDg4Ls+Z8q29H4rhg4YMIDhw4czcODA5LbRx8eHK1eusHfvXgBiY2Px9fUlNDQ0+Q1eVFQUOXLk4NSpUyQmJhIbG3vTfNOUGQYPHswbb7zB559/Tp48eZIfL1q0KGvXrgXMG81z587Rvn175s6dy8yZM+nYseN9v0/COlP+mULz6c3x8/FjY7eNUnDezAeISHE/AvC99SSllDfQCJh5pyfRWgdprf211v6+vrd9eZrwuG1duXLleP3111m3bh3r1q1j6dKlyQW2S4rJclprChYsyNatW5OLwA0bNtz1eT/44IPkdUASExMBKFGixG3tWNmyZVm/fj2XL18G4OzZs/j4+HDsmPnTc+HCBTZt2pT8vCnbtgdtPyMjI+nVqxfBwcFMnz6dLl263Pf7ItKGOXPghRegRAnYsMFxCk5Iaz2dFrhT4/Thhx+yf/9+3n777Qd+nuLFi7Nv3z4uX76Ml5cXGzZsoFKlSpw4cYIKFSpQrVo1fv75ZwYPHkzPnj0JDAykTZs2tG/fnoIFC95xDpcQ93T9InTu3LB9u7nk9RAXSgRg0dV/K7Rr146CBQsyc+ZMcuXKRdu2bVm8eDFPPvnkAw3vnz17Nr/99hsuLi7kz5+fEiVKkJiYyPDhw9mzZw/9+/enT58+931OPz8/9u7dS9OmTfniiy+oVKnSTcc7depE7dq1KVCgACVKlEh+fPLkyfTq1QulFO7u7gQFBTFq1ChatGiBm5sbWbNmZcqUKXTo0IGqVatSqFAhnnjiiTtmCAwMpEuXLuTPnz/56j/A+PHj6devH4MGDcLFxYX//e9/VK5cmcqVKzNmzBhCQkLu+30S1vjuz+94Y9kbBBQJYEGbBWR1z2p1pLTmApBye3jvpMdu1RcYq7VOuMMxh/C4bd1HH31E7969GTduHFprnnrqKb799ts7nvvMM89QvXp1qlSpgre3N5kyZbrr87Zp04YKFSqQL1++5Itd2bJlu2M79sYbb1CnTh0yZ86Mr68vs2bN4oknnqB69eoUL14cPz+/O77Gw7SfRYoUQSnFtm3bGDFixH2/L8J648eb/oVq1czW69mz3/9r0hJ1h/dPduHv769v/YO9d+9eSpcunSqvfzfR0dG3NU5Zs2blySefZObMmZw9e5aJEyfeNG8ppZTH5s2bx4gRI3Bzc8PLy4vRo0cTFhZG//79UUpx7do1Ro0aRWhoKD///DNubm54eHgwZcoUcubMedtzp4Xvj0ijEhPN7r/e3jB0aNraiOkhKaW2a60tmf6ulPoAuKS1/iHp/pfAf1rribec5w2sBqre781YWm3rxKNbvHgxc+fOZdy4cXc9R37G1tBa88naTxi8YTAv+r3I1Jem4uGSNlfstritKweMBWoCbsA2oCGm/YtKOscT2ARUSboQd0/S1jm+H3/8kfPnzzNw4MC7niM/07RhxAh45x1o0ADmzjVzOdOqu7V1Gb6nM1OmTERE3BhxsmvXruTPf/jhB4B7DjtIeezFF1/kxRdfvOl4vnz5WL9+/U2PPf3003Tt2vUxUosMLSEBuneHiRPNSrXicWSYq/8PIygoiKlTpybfr1y5MsOHD3fY13lcM2bMoG/fvlbHELdISEzgtSWvMXb7WF558hXGNBmDi1OGf1tzR1rrUKXUImAzoIERQAAQCDRLOq0XMOlBCs70wl5t0Oeff86aNWuS77/wwgv079//sZ/XlrTWzJs3j0mTJlkdRdyD1vDJJzB4MLRuDcHBkDSF2PForVPlVrlyZX2rPXv23PZYWjZ27Fhdq1at5Nvbb79t19dztO+PSAVxcVq3a6c1aP3pp1onJlqd6LEBITqV2qFbb0A5zJV9ZyATEArkB7KmOMcT2Am4Pchzpoe2ThgHDx7U1atX1++///59z5WfceqKiYvRrWa20nyKHrBygE50gLbQyrbOHjdp6xzX5s2b9dNPP62///77+54rP1PrJCRo/dpr5i3fK69oHR9vdaIHc7e2Ti4JPoSePXvSs2dPq2OIjKxzZ5g61axUK72cj03L1X9xD8WKFbtpwQ6RNlyKvcSLM15k9eHVDG8wnLefefD1F4QQZi7q1q1brY4h7iEuDrp2hSlT4N13Ydgwh55JBaSB4bVa6/uuZpgRmQsFQtyiRQuoXBkeYpErcW9a6yHAkFsenpLiuE1WWJC2Lv2S9jr1hF8Jp/HUxuw4tYNJLSbRqWInqyOJW0hbl35I22aN6GgIDITffjN9DAMGOH7BCRYXnR4eHpw7d46cOXNKA5WC1ppz587h4ZE2F0MQqSwmBrZuhZo1zYB+4XCkrUu/pL1OPccij9EguAFHI48yL3AeTUs1tTqSuIW0demHtG3WiIqCZs3g99/hp5/MmpHphaVFZ4ECBQgLCyM8PNzKGGmSh4cHBQoUsDqGsNrVq6Z3c/16OHAACha0OpF4BNLWpW/SXtvf3vC9NJjcgEuxl1jRYQXPFX7O6kjiDqStS1+kbUtdERHw/POwc6cZVtu2rdWJbMvSotPV1ZWiRYtaGUGItOviRXO5a+NGGDdOCk4HJm2dEI/uz7A/aTy1MW7Obqzvsp6KeSpaHUnchbR1QjyasDCzHcrhwzB/PrzwgtWJbM/yOZ1CiDs4dQoaNoR9+2DaNDO4XwghMpgVB1fQckZLcnvlZmXHlRTLXszqSEIIYVP//Qf168P587B8uZlNlR5J0SlEWjR5Mhw6BIsXm5ZICCEymBmhM+g4ryNlfMuwrMMy8njlsTqSEELY1N9/mz6GhARYtw6eesrqRPbjdL8TlFKllFKblVLTb3ncQykVqpT61G7phMho4uLMx3ffNS2RFJxCiAzop20/0XZOW6oVqMa6Luuk4BRCpDubN0NAALi6woYN6bvghAcoOoGqwHd3ePxzYKVt4wiRga1ZA6VLm3EWSkHx4lYnEkKIVKW15tN1n/LaktdoUrIJyzssJ5tHNqtjCSGETS1fDvXqga+vWbrDz8/qRPZ336JTa/0rcDrlY0qpqkAuYIGdcgmRscyebZYsy5QJMme2Oo0QQqS6RJ3I60tf57P1n9G5YmfmBs4lk2smq2MJIYRNzZoFTZtCqVKmh7NwYasTpY4H6em8iVLKHfgKeOsBzu2plApRSoXI8tlC3MXo0fDyy/D002Zjpvz5rU4khBCp6lrCNdrPbc+P237knWfeYXzz8bg4ybITQoj0ZfJkaNMGqlSBtWshd26rE6Wehy46gc+Ab7XWF+53otY6SGvtr7X29/X1fYSXEiKdmzYN+vQxa2OvWAHZs1udSAghUlV8Yjzt5rRjeuh0htYbyjcNvsFJPcrbEyGESLtmzoTOnc08zhUrIFs2qxOlrke5jFgeKKKU6gDkB7Irpc5orUfbNpoQGUCzZvDVV9C/P7jIVX0hRMaitabXb72Ys3cOIxqM4K1n7juISgghHM78+dCuHdSoAQsXZsyZVA/9LldrnbxdqVKqC1BECk4hHkJsLHzxBbz3HmTNCh98YHUiIYRIdVpr3l3xLuN3jmdgzYFScAoh0qUlS27Molq8GDw9rU5kjQcqOrXW64B1d3h8om3jCJHOXboELVvCqlVQoYJphYQQIgP6asNXjNgygtervM5nAZ9ZHUcIIWxu1Srztq98eVi6FLJksTqRdWQ8nxCpJTwcGjeGHTtg0iQpOIUQGdaPW3/k47Uf06FCB0Y2GolSyupIQghhU+vXm1lUpUplzDmct5KiU4jUcOQINGgAYWGwYIFZOEgIITKgKf9Moe/SvjQt2ZTxzcbLokFCiHRn82bzVq9IEVi5EnLmtDqR9aToFCI1KAUeHmacRfXqVqcRQghLLPp3EZ3ndyagSAAzW8/E1dnV6khCCGFTISFm6/W8eWH1asiVy+pEaYMUnULY05494Odndv7duROc5Iq+ECJjWn9kPa1ntebJvE+yoM0CPFw8rI4khBA2tXOnGdiWMyesWWMKT2HIO2Ah7OW336ByZfj6a3NfCk4hRAa1/eR2mk5rStFsRVnafilZ3bNaHSnDUUr1VUr9oZTaopQKvMPxJ5VSm5VSG5VSi6zIKIQj270b6tcHLy9TcBYsaHWitEV6OoWwhwkToEcPeOop81EIITKofRH7aDSlETky5WBFxxX4ZPaxOlKGo5QqDnQDqgHuwFal1Aqt9YWk49mAsUALrfVJpZS8PxTiIfz7L9StC66upuAsUsTqRGmPdL0IYWvDhkG3blCnjml5fH2tTiSEEJY4evEo9YPr46ycWdlxJQWyFrA6UkZVB1iotb6mtb4E/A6kXGCgA7AFCFJKbQRetCCjEA7p4EHzlk9r87avRAmrE6VNUnQKYUuHDsEnn0CbNrBokRljIdI0GXImhH2cuXyG+sH1uRR7ieUdlvNEziesjpSR+QARKe5HACmviPoBhYGWmIJziFLqtiumSqmeSqkQpVRIeHi4PfMK4RCOHjUFZ3S0WSvSz8/qRGmXDJ8Qwhbi4sDFBYoVgy1boEIFmcPpAGTImRD2cTHmIo2mNCIsKoyVHVdSMU9FqyNldBeAlJs2eCc9dl0CMEtrfQ0IV0ptxxSiN1WWWusgIAjA399f2zWxEGnciRNmSG1UlFmltnx5qxOlbfKuWIjHFRoKTz8NwcHmfqVKUnA6DpsMOZOr/0LccDXuKk2nNWX32d3MC5xHjUI1rI4kYCPQWCnlrJTKBAQAIUqprCmO1wVQSnkCFYB/rQgqhCM4fdoUnGfPwrJlZgkPcW/yzliIR5WYCMOHmxVqT52SnX8dk02GnGmtg7TW/lprf1+ZwysysGsJ12g1sxWbjm1iSsspNCzR0OpIAtBahwKLgM3AWmAEpvCcnHTKHCBSKRUCrAY+01qfsSCqEGleRATUqwfHj8OSJVC1qtWJHIMMFRPiURw9Cp07w/r10KIFBAXJgkGOySZDzoQQkJCYQKd5nVh6YClBTYJoXba11ZFEClrrIcCQWx6eknQsEXgztTMJ4WjOnzfbohw8CIsXw7PPWp3IcUhPpxCPYscO+OsvGD8e5s6VgtNxyZAzIWxAa81rS15jxu4ZDKs3jB6VZasoIUT6EhkJjRrBnj0wf75ZQEg8OOnpFOJBRUTApk3QvLnp3Tx0CHxkvzlHprUOTVqRdjOguTHkLBBohhlyViNpyFk8MuRMiDv6cPWHjN0+lgE1BtC/Rn+r4wghhE1dvgyNG5s+h7lzoaHMHHhoUnQK8SCWLjV7b166BMeOQY4cUnCmEzLkTIjHM2zTML7e9DW9K/fmq7pfWR1HCCFs6upVaNoU/vwTZswwn4uHJ8NrhbiXK1fg1VfN5S0fH9i40RScQggh+Hn7z7y/6n3alGvDD41/QClldSQhhLCZmBgzuG39erNJwUsvWZ3IcUlPpxB3ExMD/v6wfz+88w4MHgweHlanEkKINGHm7pn0WtSL50s8z6QWk3B2crY6khBC2My1a9CqFaxcCRMmQNu2VidybFJ0CnGrxESzz6aHB/TqZfbdDAiwOpUQQqQZyw4so8PcDtQoVIPZL8/GzdnN6khCCGEzcXHQpo1ZoXbMGOjSxepEju++w2uVUqWUUpuVUtOT7vsqpYKVUuuTNkPva/+YQqSSffugWjVYtcrcf/NNKTiFECKFTcc20XJGS8rmKstvbX8js2tmqyMJIYTNxMVBu3Ywbx6MGmX6H8Tje5A5nVWB71Lc9wWGaq1rAc8BHyuZxCEcXWIi/PADPPmkWZU2NtbqREIIkeb8deovXpj6AgW9C7K8w3KyeWSzOpIQQthMfDx06ACzZ8OIEdCvn9WJ0o/7Dq/VWv+qlApIcX9PisM5gTCttbZ9NCFSyYkT0LWrGbTfuDH88gvkzWt1KiGESFN2nNpBvV/rkc0jGys7riSXZy6rIwkhhM3Ex0PHjjBzJvzvf/DWW1YnSl8eefXapI3SfwW63+OcnklDcEPCw8Mf9aWEsK/ffjP7b44ZA4sWScEphBC3+Pv039QLrkcW9yys7byWQt6FrI4khBA2k5AAnTvD9OkwdCi8+67VidKfRyo6lVJZgNmYjdJ33u08rXWQ1tpfa+3v6+v7iBGFsIMLF2DDBvN5r16wd6/5KCPFhRDiJrvO7KLur3XJ7JqZtZ3XUjR7UasjCSGEzSQkmAFvU6fCV1/Be+9ZnSh9euiiUynlDczHzOtcb/NEQtjbqlVQvjy0bGl2/FUKCslVeyGEuNXus7up+2tdPFw8WNt5LcWyF7M6khBC2ExCArzyitmDc/Bg+OADqxOlX4/S0/kR4Ad8qpRal3TLb+NcQtjeuXPmUlb9+pAlCyxdCpll1UUhhLiTveF7qfNrHVycXFjTeQ0lcpSwOpIQQthMYiL06AGTJsFnn8FHH1mdKH17oH06tdbrgHVJn78HSMezcCxnz0LZsnDxormMNXAgZMpkdSohhEiT9kfsp86vdVAo1nReQ8mcJa2OJIQQNpOYaGZVTZgAn3xibsK+HqjoFMJhXbwI2bJBrlxmz81mzczQWiGEEHf077l/qT2pNok6kXWd1+Hn42d1JCGEsJnERHj1VbNZwccfw6efWp0oY3jk1WuFSNOuXTOD8wsWhF27zGMffSQFpxBC3MOB8weoPak2cYlxrOm0htK+pa2OJIQQNqM1vPYaBAWZgW+ffy5rSKYW6ekU6c/GjdCzp1mR9uWXwcfH6kRCCJHmHbpwiNqTahMbH8vazmspm6us1ZGEEMJmtIa+fc0Oee+/D19+KQVnapKeTpF+XG9NnnvOrEq7eDHMmCH7bgohxH0cuXiE2pNqczXuKqs7raZ8bhkVIoRIP7SGN96An34ye3AOGSIFZ2qTolM4Pq3NR6UgRw545x3YvRsaN7Y2lxBCOICjF48SMDGAS7GXWNVxFRXzVLQ6khBC2IzW8NZb8P335uOwYVJwWkGKTuHYDh82xeXy5eb+55/DN9+Ap6e1uYQQwgEcjzxO7Um1uRhzkZUdV/Jk3ietjiSEEDajtenZHDXK9HQOHy4Fp1Wk6BSOKS4Ohg4126Bs3Gi2RBFCCPHAwqLCqD2pNueiz7Gy40oq56tsdSRhR0qpvkqpP5RSW5RSgbccK6qUOpVi//UpVuUUwla0hvfegxEj4PXX4dtvpeC0kiwkJBzP1q1mN99//oHmzc14iYIFrU4lhBAO4+Slk9SZVIezV86ysuNKns7/tNWRhB0ppYoD3YBqgDuwVSm1Qmt9IemUbMBUrfU7FkUUwqa0NqvTfvMN9Oljejql4LSW9HQKx7NzJ5w7B/Pmwfz5UnAKIcRDOHXpFLUn1ebU5VMs67CMqgWqWh1J2F8dYKHW+prW+hLwO1A9xfHsQFOl1Cal1DKlVMCdnkQp1VMpFaKUCgkPD7d7aCEehdZm/82hQ6F3b/jhByk40wIpOkXapzXMmQNTp5r73bub7VBatLA0lkgfZMiZyEhOXz5NnV/rcCLqBEvbL6V6wer3/yKRHvgAESnuRwC+Ke6v01qX1FrXAN4GJiilUh4HQGsdpLX211r7+/redlgIy2kNn3wCX31lBsX9+KMUnGmFDK8VaduxY2YblN9+g9q1oW1bcHKCLFmsTibSARlyJjKSs1fOUvfXuhyLPMbS9kt5ttCzVkcSqecCkDPFfe+kxwDQWiem+HyPUuov4AlAujOFQ/nsMxg8GF55xezH6STda2mG/ChE2hQbawbilykDq1ebz1eskMtVwtZkyJnIEMKvhFP317ocvnCYxe0WU7NwTasjidS1EWislHJWSmUCAoAQpVRWAKVUaaWUa9Ln+YAyQKhVYYV4FJ9/borOrl0hKEgKzrRGejpF2vTnn9C/PzRpYhYKKlLE6kQifXqgIWcASqkywGKlVBWt9U2VpdY6CAgC8Pf31/aNLMTDibgaQb3gehw4f4BFbRcRUCTA6kgilWmtQ5VSi4DNgAZGYArPQKAZUAIYp5SKAxTQS2sdZVFcIR7al1/CoEHQuTP8/LMUnGmRFJ0i7fj77xsr09asCSEhUFmW8Bd2JUPORLp2Pvo89YPrsz9iPwvbLqRusbpWRxIW0VoPAYbc8vCUpGO/Ab+leighbODrr83CQR07wrhx4OxsdSJxJ3IdQFjvzBno2ROefNJcprp61TwuBaewPxlyJtKtC9EXqB9cnz3he5jfZj4NijewOpIQQtjUiBFma5R27WDCBCk40zIpOoV1YmLMetZPPGFairfegt27IXNmq5OJDEJrHQpcH3K2lhtDziYnnVICWK+UWg9MR4acCQdxIfoCDSY3IPRsKPMC59GoRCOrIwkhhE398AO88w60bg2TJknBmdbJ8FphnePHzXiIxo3hf/+DkiWtTiQyIBlyJtKbc1fPUT+4PrvDdzPn5Tk0fqKx1ZGEEMKmgoLg9deheXOYMgVcpKJJ8+RHJFLXX3/BwoXw6aemh3PvXihRwupUQgiRLoRfCadecD32R+xnfuB8nn/ieasjCSGETU2cCL16mT6LGTPA1dXqROJB3Hd4rVKqlFJqs1JqeorHvkx67I+7bSEgxE1OnYJu3cDfH376yczjBCk4hRDCRs5eOUudX+vw77l/Wdh2oRScQoh0Z8oU83ayfn2YMwfc3a1OJB7Ug8zprAp8d/2OUqoOUElrXR14CRijlJIeU3Fn0dHw1VemV3PyZDP4/r//IHduq5MJIUS6cfryaWpPqs3B8wdZ1HaRLBokhEh3Zs2CTp0gIADmzwcPD6sTiYdx36JTa/0rcDrFQ3WBWUnHTgJHgVJ3+lrZMF0QHQ3Dh0ODBrBnj5m76e1tdSohhEg3Tl46ScDEAI5cPMKS9ktkWxQhRLqzYIFZofaZZ8wsLVlz0vE8yuq199tMPZnWOkhr7a+19vf1veMpIj3ats1sgZKQADlymBVp586VobRCCGFjJ6JOEDAxgLCoMJa1X0ZAkQCrIwkhhE0tWWJWqK1c2Xzu5WV1IvEoHqXovIDZQP26mzZTFxnYiRPQuTNUqWIuSR06ZB7Pk8faXEIIkQ4djzxOrYm1OH35NCs6ruC5ws9ZHUkIIWxqxQpo2RIqVIBlyyBrVqsTiUf1KEXnRqAZgFLKBzO0dr8tQwkHc+0afP652fJk+nQYMMDM23ziCauTCSFEunT04lFqTaxF+NVwVnZcSfWC1a2OJIQQNrV2rdkSxc/PFJ/ZslmdSDyOR1kAaAnQQCm1GVO0vqG1jrFtLOEQLl82YxxcXMwiQY0bw9ChUKyY1cmEECLdOnThEHUm1SEyNpJVHVfxdP6nrY4khBA2tXEjNGkCxYvDypVmtpZwbA9UdGqt1wHrkj5PBPrZL5JI07SGzZvh229hwwY4fNjM5t6yRVoEIYSwswPnD1BnUh0uX7vM6k6reSrvU1ZHEkIIm/rzT9OPUaAArFoFsixM+iBbnYgHExcHs2ebYnPbNsie3SwWFBdnjkvBKYQQdvXvuX+pM6kOMfExrOm8hkp5KlkdSQghbGr7dmjYEHLlgjVrZFmQ9ESKTvFgQkLMWtUlS8KPP5oFgzw9rU4lhBAZwr6IfdSZVIf4xHjWdl5L+dzlrY4khBA29fffUL++6ddYswby57c6kbAlKTrFnf37L4wcCZkymX02q1UzM7pr1gSnR1l/SgghxKPYE76HOpPqALCuyzrK+JaxOJEQQtjW7t1Qr57pz1izBgoVsjqRsDWpHsQNWsPq1WbmdqlSMG4cxCStEaUUBARIwSmEEKko9GwoARMDcFJOUnAKIdKl/fuhbl1wdTX9G0WLWp1I2EOarCDOnYPYWKtTZECff24uM23dCoMGwbFjZiitEEKIVPf36b+pPak2rs6urOuyDj8fP6sjCSGETR04AHXqmH6PNWugRAmrEwl7SXPDa2Ni4LnnoFw5mDYNnJ2tTpSOnT0Lo0dDo0ZQtSq0bQsFC5q5mx4eVqcTQogMa8epHdQLroenqydrOq+hRA55JyaESF+OHDEFZ2wsrFtn9uMU6VeaKzo9POCVV+Ddd82CqKNHm5GdwoZ27TLzNadMMb/pbm6m6CxZ0tyEEEJYJuRkCPWD6+Pt7s3azmspml3Gmgkh0pfjx6F2bbPl+5o1prNJpG9prugEeOcdCA+HoUPN3jxffGF1olR2+bJZLfbkSbMlSXy8qcQBVqyAHTtuPB4XZ7qDP//cHP/5Z7NnZlzcjXOyZjXzMwH69jVDZjNlgm7d4I03zPxNIYQQlvsz7E8aTm5Ijkw5WNN5DUWyFbE6khBC2NTJk6aH8/x5s5RIpUpWJxKpIU0WnQBDhkBEBAweDD4+pjZKlxITzQzqkiVN8Th4sJlPmZh483ldu5pFfObOhbFjbzzu7Aze3jeKzp07YflyMxv7+i1XrhvnZ8tmvrk9e8remkIIkYZsPr6ZRpMbkcszF2s6r6GQtyzfKGxHKdUXaA8o4Fut9Yw7nOMBhACztdafpm5CkRGcOWMWDTp9GlauBH9/qxOJ1JJmi06lYMwYcxXkzTchZ07o0MHqVDYQGQkbN8Kff5oeya1bzWP//APly8Mzz8DHH5stSooVu1E4Xh9jPHIkjBhhHnNxuX3s8Y8/3nvxn8GD7fZPE0II8Wg2HtvI81OeJ69XXtZ0XkOBrAWsjiTSEaVUcaAbUA1wB7YqpVZorS/ccurnwMrUzicyhogIs17lsWOwbJl5qysyjjRbdIKpqaZOhcaNTUdf9uzwwgtWp3oIcXGmmNyyBWrVMgPWN20yW5I4OZkis00b81uXL5/5mrp1ze1uZIEfIYRIVzYe20ijyY0o6F2Q1Z1Wky9LPqsjifSnDrBQa30NuKaU+h2oDiy+foJSqiqQC5gIBNzpSZRSPYGeAIVkI0XxEC5dguefN6vVLl5sFg0VGUuaLjrB1Fjz55vJxq1bm674GjWsTnUPkZFmEuqff5p5mdf3ufzmG1N0PvecWaKrcmXw8rI0qhBChpwJa/177l+aTWtGgawFWNt5LXm88lgdSaRPPkBEivsRgO/1O0opd+AroBVQ8W5PorUOAoIA/P39tV2SinQnNhZatDBLksyfb+ZziownzRedYNbBWbrU1GtNmsD69VChgsWhrl6F7dtNL+aff8KTT8JHH5kFesaNg9Kl4dVXTS9mtWpmKxKALFlMr6cQwnIy5ExY6dzVczSZ2gQXJxeWtl8qBaewpwtAzhT3vZMeu+4zzEW3C0q2DBA2lJAA7dubFWonTTLv40XG5BBFJ5i1cFasML2cDRuaUarFiqXSiycmmuV0c+c2959/3nS5JiSY+8WK3aiC3dzMuS4O860VIiOTIWfCEtcSrtFyZkuORR5jTec1si2KsLeNwFil1NeAG6YtG6KUyqq1jgLKA0WUUh2A/EB2pdQZrfVoyxILh6c19OkDc+aY5Ug6dbI6kbCSQ1VGhQubwvO556BBA7MeTx57XBg+f94s8LNly43FfnLmhP/+M8erVjXDY6tVM5/7+t789VJwCuEoZMiZSHVaa3r+1pPfj/7O1JZTqV6wutWRRDqntQ5VSi0CNgMaGIEpPAOBZlrr5BUzlFJdgCJScIrHNXAgBAXBBx/AW29ZnUZYzeGqozJlzATkunWhUSMzPTJbtsd4wuuL/WzbZrYRcXKC99+HX34xn5crB61amQJTa7Na7Kef2uYfI4Swmgw5E6luyMYhTPp7Ep8FfEbb8m2tjiMyCK31EGDILQ9PucN5E1MlkEjXRo6EL7+EHj3MRyHSZtF54QK8+66ZH5nyVr8++PtTrUwUm99YymfDMvHFcx589W0m3LNlMsNcc+QwheTly+Zr3N1v31Zk1y749VfTi5lysZ+AAPDzg759oV07s3lQliyp/s8XQqQaGXImUtXM3TP5aM1HdKjQgYE1B1odRwghbC442PRstmwJo0ff/jZcZExps+i8fBmWL4foaHOLiTG9jF5ephA8epSKQ9owFyAUqJ/0dePHm71VQkKgeorhSh4epgCdOBGaNYNDh+D77+Gpp6B37xvDZAsXNudXvOsoOiFEOiJDzkRq2hK2hU7zOvFsoWf5pekvSO+5ECK9WbzYvBWvXRumTAFnZ6sTibTikYpOpVQmYBxQGNM7sFRr/YnNUhUsCGFhN+5rDdeu3bhU8sQTsHs3REczb2o0P42I5oU6MfSrXREnMMXjt9+aYvV64RodfaOofP55iIoyi/4IITI0GXImUsORi0doPr05+bPmZ17gPNxd3K2OJIQQNrVpk9nesGJFszWKbC0vUnrUns4uwAWtdTullDOwWSk1T2u9w3bRUlDKDJO9zsPDTO4EXqwMu7zhrUFw/HuzHabKlw/efPPuzyfFphBCiFQSGRNJk6lNuJZwjcXtFuOT2cfqSEIIYVO7dpntUAoWNNscZs1qdSKR1jxq0XkaeDap4MwMOHPz4htA6m0jMHAgRESY5Zh9fWHAALu9lBBCCPHA4hPjeXn2y+w/t5/lHZbj5+NndSQhhLCpw4fNdoaenmaXiVy5rE4k0qJHKjq11vOUUgHAIcyG6h9qrY/c4bxU2UZAKbNK1rlzZlnmnDnNallCCCGEVbTW9FvajxUHV/BL01+oU7SO1ZGEEMKmzpwx63zGxMCGDTdmsglxq0ed09kLUEAxwBWYppQK01qvsGW4h+HkBBMmmIVve/c2i9i+9JJVaYQQQmR03/35HaNDRvN+jfd55alXrI4jhBA2FRlpejhPnYLVq6FsWasTibTM6RG/rhRwTGudoLWOwQy3LWW7WI/GzQ1mzTIL0bZrB2vWWJ1ICCFERvTb/t94a/lbtCzdkq/qfmV1HCGEsKnoaLMhxO7dMGeO2QhCiHt51KLzf0A9pdQmpdSfQA5ggu1iPTpPT1i0CEqWhObNze4pQgghRGrZeXonbee0pXK+ygS/GIyTetQ/tUIIkfbEx0ObNmY47a+/QqNGVicSjuBR53SeAtLsf7EcOcw2nzVqmN1RNm6EUpb3wwohhEjvTl46SZOpTcieKTsL2ywks2tmqyMJIYTNaA09e8LChfDDD9C2rdWJhKNIt5df8+UzK2g5OZkJzim3/RRCCCFs7cq1KzSd1pTI2EgWtV1E3ix5rY4khBA29f77Zg2VQYPgtdesTiMcSbotOgGeeAKWLTMTnRs0MKvbCiGEELaWkJhA+7nt2Xl6J9Nfmk7FPBWtjiSEEDY1bBj873+m2Bw0yOo0wtGk66IT4MknzRCAQ4egcWPzUQghhLClAasGsGD/AkY2HMkLJV+wOo4QQtjU+PGmlzMwEL77zmxXKMTDSPdFJ0CtWjBzJvz1FxQvDtWrw48/Qni41cmEEEI4uqDtQXzzxzf0fbovr1d93eo4QghhU/PnQ48eZtTgr7+aqWtCPKwM89+mWTM4eBC+/houX4a+fSFvXnjhBZg6Fa5csTqhEEIIR7Pq0Cr6LO7D8yWe59tG31odRwghbGrdOrNS7dNPw9y5ZntCIR5Fhik6AQoVMkMD/vkH/v4b3nkHdu2C9u0hd27o0AGWLjVLQQshhBD3sid8D61mtqK0b2mmt5qOi9MjLQgvhBBp0o4dptOmWDFYvNhsSyjEo8pQRWdKFSrA0KFw5Ii5itOunfmFatzYrHz7+uuwZYtZGloIIYRI6eyVszSZ2gQPFw8WtV1EVvesVkcSQgib+e8/aNgQsmc3u0HkzGl1IuHoMmzReZ2Tk5nzGRQEp0/DvHnm/s8/wzPPmBVwP/kE9u+3OqkQQoi0ICY+hhbTW3Dq8ikWtl1I4WyFrY4khBA2c/asmb+ptSk4CxSwOpFIDzJ80ZmSuzu0aAGzZsGZM2alriJFYPBg8PMDf3/49ls4dcrqpEIIIaygtabrgq78EfYHwS8GUyV/FasjCSGEzcTFQevW5n3wkiVQqpTViUR6IUXnXXh7Q9eusGoVhIXB8OGQmAhvv22u+DRoAJMmQVSU1UmFEEKklk/Xfcr00OkMqTuEVmVaWR1HiAemlOqrlPpDKbVFKRV4y7FCSqnFSqnNScfbWZVTWOvtt+H33+GXX8ziQULYihSdDyBfPvNL+NdfsGcPfPABHDgAXbqYBYhatza9oidOWJ1UCCGEvczcPZPPf/+crpW68n6N962OI8QDU0oVB7oBtYD6wKdKqewpTnEFemutqycd/0Yp2Ykxo5k4EX74wbznbSeXHYSNSdH5kEqXNsNtDx6ETZugWzfYuBFeecX0gJYvD+++CytXQkyM1WmFEELYwq4zu+i6oCvVC1ZnTJMxyPtx4WDqAAu11te01peA34Hq1w9qrQ9qrY8n3S0AhGstSylmJNu2Qe/eULeuWWhTCFuTovMRKQXVq8OPP8LJk2YLlmHDIFcu+P57M/w2Rw54/nkYORL27ZOVcIUQwhFdiL5Aixkt8Hb3Znbr2bg5y0Z1wuH4ABEp7kcAvreepJQKBjYCH97pSZRSPZVSIUqpkPDwcLsEFanvzBl48UXIkwemTwcX2f1J2IEUnTaglNmCpX9/WL0azp+HRYuge3c4dAjeesv0kBYpAj17wpw5cPGi1amFECDznMS9JSQm0H5ue45HHmf2y7PJmyWv1ZGEeBQXAO8U972THruJ1rojUBIYppS6bc1SrXWQ1tpfa+3v63tbzSoc0LVrZprY+fMwfz74+FidSKRXci3DDjw94YUXzA3MXqDLl5vb9OlmOxZnZ6haFRo1MvsgVa5sHhNCpJ4U85yqAe7AVqXUCq319Tdj1+c5HVdKZQH2K6WmybCzjOPTdZ+y9MBSRr8wmuoFq9//C4RImzYCY5VSXwNuQAAwRCmVVWsdpZSqCBxKGnp7CbgGeFmWVqSat9+GDRtg6lSoVMnqNCI9k57OVFCkCPTqBXPnwrlzZlWwAQPM1aVBg0zxmSsXtGkDEyaY4bpCiFRhk3lOMuQsfZq/bz6DNwymW6Vu9Krcy+o4QjwyrXUosAjYDKwFRmAKz8kpTgtWSq3GtIOTtdb7UjunSF3jx5tpYu++C23bWp1GpHcqtS7Y+/v765CQkFR5LUcSHm62ZVm2zGzAe/q0ebxcOWjcGJo1g2rVpBdUpF9Kqe1aa3+LXvsD4JLW+oek+18C/2mtJ95yXjDQGOiktV58r+eUti592Bexjyo/V8HPx4/fu/6Oh4uH1ZGEg7OyrbMHaesc259/Qs2a5rZ0qczjFLZzt7ZOejot5utrri5NmmR6OHfuNKuG5coFI0bAs89C3rxmldz58+HKFasTC5Gu2GSek0hfomKjaDG9BR4uHsx5eY4UnEKIdOX0aWjZEvLnl4WDROp5rKJTKVVYKbU6aZGNjUop+cv8GJSCihXhvffMgkQREaYxqFfPDM198UUzwbtpUzMv9NQpqxML4fA2Ao2VUs5KqUyY4WYhSqmsAEqpiklzOUHmOWUIiTqRTvM6ceD8AWa1nkVB74JWRxJCCJu5dg1atYILF2DePMiZ0+pEIqN45GsbSilnYAbQVWu9VynlrLVOsF004e0NgYHmFhdnJnovWAALF5rVccHMB23WzNzKljWFqxDiwWitQ5VS1+c5aW7McwoEmiWdFpxUeHoi85zSva82fMWC/QsY2XAktYrUsjqOEELY1Jtvmn3mp00zHR1CpJZHntOplGoCtAayALmBadfnRaU4pyfQE6BQoUKVjx49+nhpBWD2+wwNNcXnggVmQ1+AYsVuFKDPPSfDJYRjkHlOIq1Y8t8SmkxtQrvy7Qh+MRglV/GEDUlbJ6w2bpzZzq9/f7O3vBD2YI85nX5AaaAz0ADoqpSqkPIE2c/JPpSC8uXho49g61Y4cQLGjjV7gY4eDXXqmDmhHTrAzJkQFWV1YiGESNsOnD9AuzntqJinIkFNg6TgFEKkK1u2QJ8+UL8+DBlidRqRET1O0ZmA2Wrgktb6CrAKkI56C+TLBz17miG3ERFm/mfz5mZf0MBAMw+0YUOzLPbx4/d/PiGEyEguX7vMizNexNnJmbkvzyWza2arIwkhhM2cPg0vvXRj4SDZEUFY4XGKzo1AQNICHC5ADWCXbWKJR+XlZRYcmjDBNDIbNpjx+0eOQN++UKiQaXgOHLA6qRBCWE9rzSsLX2FP+B6mvzSdotmLWh1JCCFs5vrCQRcvml0QcuSwOpHIqB656NRabwNWAiGYAnS21nqnjXIJG3B2NluuDBsG+/fDvn0wcKDpAS1TBt55x6xeJoQQGdXwP4Yzc/dMvqrzFfWL17c6jhBC2NQbb5iFgyZMgAoV7n++EPbyWFumaK2Haq2f1FpX01qPtFEmYSelSsHnn8N//0GnTvDtt1CiBHz/vVkdVwghMpJVh1bx/qr3aVWmFe/VeM/qOEIIYVM//wxjxsD778PLL1udRmR0j1V0CseUNy/88gvs2AGVKkG/fmZhokWLzMq4QgiR3h25eIQ2s9tQ2qc0E5pPkIWDhBDpyh9/wGuvmTU9vvzS6jRCSNGZoVWsCKtWwW+/mftNm5pVzf7+29pcQghhT9Fx0bSc0ZL4xHjmBc7Dy83L6khCCGEzJ0+a9TsKFoSpU2XhIJE2SNGZwSkFTZrArl1mmO2OHfDkk2Yfp1OnrE4nhBC2pbWm16Je7Di9g8ktJ/NEziesjiSEEDYTG2sWDoqKkoWDRNoiRacAwNXVrG574AC8/Tb8+is88QQMHgxXr1qdTgghbOOHrT8Q/E8wnwV8RpOSTayOI4QQNtWvnxlaO2GCmTolRFohRae4Sfbs8M03sHevmQcwcKBZgGjyZEhMtDqdEEI8ut+P/s7bK96mWalmfFzzY6vjCCGETQUFmduAAdC6tdVphLiZFJ3ijooXhzlzYP16yJ0bOnaEqlXNvp9CCOFowqLCaD2rNcWyF+PXFr/ipOTPnxAi/di82YxYa9TIjFITIq2Rv7rinmrWhK1bzXDbU6fM/Vat4OBBq5MJIcSDiY2PpdXMVlyNu8q8wHl4e3hbHUkIIWzm+sJBhQrJwkEi7ZKiU9yXk5Pp6fz3X7PP59KlUKYM9O8PFy9anU4IIe7t9aWv8+eJP5nUYhJlfMtYHUcIIWwmNtYUnJcumYWDsme3OpEQdyZFp3hgmTObOZ7//QcdOsDw4VCiBPz4I8TFWZ1OCCFuF7Q9iJ//+pkPnv2AlqVbWh1HCCFs5vhxaN8etmyBSZOgXDmrEwlxd1J0ioeWLx+MGwd//QUVKpg5BOXLQ3CwFJ9CiLRjS9gW+i7pS8PiDfmi9hdWxxFCCJv47z945RWz/saCBTBkiOntFCItk6JTPLJKlWD1ali40Gy50qkTlCwJP/0E0dFWpxNCZGSnL5/mpZkvUdC7IFNfmoqzk0xyEkI4tl27oG1b8PMzczd79TJrbAwYYHUyIe5Pik7xWJSCpk3h779N8Zk3L7z2GhQtCkOHms2JhRAiNW04uoEXpr7AhegLzH15Ljkyye7oQiil+iql/lBKbVFKBd5yzFcpFayUWq+UClFK9bUqp7jd1q3QvLkZXbZoEbz7Lhw5At9/bxYPEsIRSNEpbMLJyRSfmzbBunVQsaK58laoEHz8MYSHW51QCJGeaa1Z/O9inh3/LDUn1uR45HGmvjSVinkqWh1NCMsppYoD3YBaQH3gU6VUyiVnfIGhWutawHPAx0oplfpJxXVam/dT9evf2LLu00/h6FFzUT93bqsTCvFwpOgUNqUU1KoFy5dDSAjUqwdffQWFC8Mbb8CxY1YnFEKkJ/GJ8UzbNY1KYyvRZFoTjkcd5/vnv+fIm0do4dfC6nhCpBV1gIVa62ta60vA70D16we11nu01qFJd3MCYVprfeuTKKV6JvWEhoTL1WS70BqWLIFnn4Xatc2Q2mHDTLE5aBDkkIEbwkFJ0SnspnJlmD0b9uyBwEAz17N4cejWDfbvtzqdEMKRxcTHMDZkLKV+KEW7ue2IS4hjUotJHHj9AH2r9CWza2arIwqRlvgAESnuR2B6N2+ilPIEfgW63+lJtNZBWmt/rbW/r+9tXy4eQ2Kiec9UuTK88AKEhcEPP8Dhw2aLuixZrE4oxOORolPYnZ8fTJhgJrv36QPTp0Pp0tC6tVkBVwgryTwnx3Ip9hL/2/Q/io4qSu/FvcmZKSfzAucR2ieUThU74ersanVEIdKiC4B3ivveSY8lU0plAWYDn2mtd6ZetIwtLg5+/RXKljXvi65cgfHj4cABs0ZGpkxWJxTCNqToFKmmUCEYNcoMEfnwQ1i50lzRa9gQ1q83Q0qESE0yz8lxhF8JZ+CagRQaWYj3Vr1HuVzlWNVxFX92/5MWfi1wUvLnTIh72Ag0Vko5K6UyAQFAiFIqK4BSyhuYj2nv1luWMgOJiYExY8yq/507g5sbzJhhRod17Wp2BRAiPXmsv9LKWKmUmmijPCID8PWFwYPN/M6vv4adOyEgAGrUMKuySfEpUpHMc0rjjkUe442lb1B4ZGEGbxhM7SK12dp9Kys7rqRusbrINQAh7i+pHVsEbAbWAiMwhefkpFM+AvwwF97WJd3yW5E1vbt8GYYPh2LF4NVXIU8e+O03817o5ZfBWXZ3EumUy2N+fR8gFMh+vxOFuFXWrPD++9Cvnxl++7//mRVwy5eHDz4ww0xcHvd/qBD3ZrN5TkAQgL+/v1w2sYF9EfsYumkok/8x74nbl2/P+zXep7RvaYuTCeGYtNZDgCG3PDwl6dh7wHupHioDiYiA0aPNiK9z56BOHZg82SwWJNfOREbwyD2dSqkiwAvA9zZLIzKkTJnMXM9//zXzGhISoF07KFXKLD4UGWl1QpGOyTynNCbkZAgvzXyJMj+WYUboDF71f5WD/Q4yscVEKTiFEA4lJsYsDtS8udnH/JNP4Jln4I8/YPVqU3hKwSkyikcqOpPmNH0HvA4k3uM8GXImHpirK3TsaJYHnz8ffHzMJPq8ec18hw0bZOitsDmZ55QGaK1Zc3gN9YPr8/TPT7P60Go+fO5Djr55lO+e/45C3rL7uRDCMWht3q/07Gnev7RuDdu2mW3jdu0yQ2mrVbM6pRCp71EHL/YGlmutDyb1eN6RDDkTj8LJyVwVbNbMrG77yy8wZYrpBS1ZErp3h06dZGNk8fi01qFKqevznDQ35jkFAs24eZ7T9S9rr7U+kfpp059LsZdYuH8h3239jq0ntpLbMzdD6w2lt39vsrpntTqeEEI8sP/+g+BgM2T28GHInBlatjQX0+vWlbmaQqg7rIlx/y9SajzgiXmTlg0oBUzQWn9+t6/x9/fXISEhjxhTZHRXrpghKr/8Ahs3mrmezZqZArRBA2nMHZlSarvW2t/qHLYibd29XY27yuJ/FzNj9wwW/7eYmPgYimUvRv/q/elSqQseLh5WRxTCLqStS38iIsyKs8HB8OefZqhs3brmwviLL4KXl9UJhUh9d2vrHqmnU2vdLcUTBwBd7lVwCvG4PD3NENvOnWHfPhg3DiZNgrlzoUAB6NbN3AoXtjqpEOJWMfExLDuwjBm7Z/Db/t+4EneF3J656f5kdwLLBVK9YHXZ8kQI4RBiYsxK+8HBsGQJxMebBRCHDTPrUeSXNX+FuKPHXhtUa70OWPfYSYR4QH5+ZqXbL780cyN++QW++MLc6tc3vZ/NmoG7u9VJhci4riVcY9WhVczYPYP5++YTFRtFzkw5aV++PYHlAqlVuBbOTjJEQQiR9mltRlkFB8OsWXDxopmv+cYbZvhsxYpWJxQi7ZMNKYTDcnODl14yt2PHzLYr48ebfa58fMzwlldegTJlrE4qRMYQnxjPuiPrmBE6gzl753Ah5gLZPLLxUumXCCwbSJ2idXB1lh3PhRCOQeZpCmE7UnSKdKFQIRg0CD7+GFatMr2f338PI0ZA9eqm9/Pll80wXSGE7STqRDYc3cCM3TOYvWc24VfD8XLzonmp5gSWDaRB8Qa4u8iwAyGEY7h4EaZONYsXppyn+dlnMk9TiMchRadIV5ydoWFDczt71lyh/OUXM9/zjTegbVtTgPr7y95YQjwqrTVbwrYwY/cMZu2ZxclLJ8nkkommpZoSWDaQ50s8TybXTFbHFEKIB6I1hITAmDEwbRpER8s8TSFsTYpOkW7lygXvvANvvw2bN5viMzgYgoKgdGlo3978MSla1OqkQqR9Wmv+OvUX00OnM3PPTI5FHsPd2Z3nn3iewLKBNCnZBC836QIQQjiOK1dMr+aYMWaLtsyZoUMH6NULKle2Op0Q6YsUnSLdUwpq1DC3kSNh+nQzP+Pjj82tenVTgF6fCyqEuCH8SjjB/wQzbsc49oTvwcXJhQbFG/BF7S9oXqo53h7eVkcUQoiHEhpqCs3gYIiKgnLl4McfzXsBb2nS0hStNRdjLhIWFUZYVBjPFHyGbB7ZWH5gOSO2jEh+PCExgVyeuVjeYTlP5HyCdUfWsfLgSnJ75SaXZ67km5+PHy5OUv5YQb7rIkPx9jZXMHv1giNHTAE6ZQq89poZftuwoen9bN5c5n+KjCshMYHlB5czbsc4Fu5fSHxiPFXzV2Vsk7G0KtOKHJlyWB1RCCEeSkwMzJljis2NG81ihC+/DL17m4vPMuUm9WmtibgakVw4hkWF0aB4A4rnKM66I+votagXYVFhXI27mvw16zqvo1aRWlxLuMbFmIv4+fhRr2g9nJ2cOXvlbPLfp20ntjF001ASdMJNrxnRP4KcmXMydONQJu+aTG5PU5Re/9i/Rn9cnFwIiwojLiEOZydn4hPjiU+MB6BkzpIA/HfuP8Kvhicfi0+Mx83ZjYAiASbnkXWcunTqpuPZPLLRumxrAObsmUPE1QhcnV1xdXLF1dmV3J65qV20NgB/hv1JbEJs8jE3ZzeyeWSjkHchAM5eOYtCJR9zdXLFxckFlYb/Iyutdaq8kGwiLNKyf/4xxee0aXD8uCk4W7QwVz3r1wcXuTxjN7Jhetpx8PxBxu8Yz6S/J3Hi0gl8MvvQqUInuj3ZjbK5ylodL13SWnP8+HEyZ86Mj48Ply5d4r///sPZ2fmmW/78+fH09CQ6Oprz58/fdtzT0xMXFxcSExPRWuPk5JSm33xkRNLWWePAATOtZvx4OHcOSpQwF567dJHRTfYWGRPJschjHIs8xtHIoxyLPEaTkk14ttCzbD+5nRrjaxCbEHvT1wS/GEyHCh3YdWYXgzcMpkCWAhTIeuNWLlc5srhneaDXT9SJnI8+z9krZzl75SxnLp+hddnWOCkngv8OZu6+uZy5fCb5+LWEa0R/FI1Sii7zuzDp70k3PZ9vZl/O9j8LwIszXmT+vvk3HS+arSiH3jgEQP3g+qw6tOqm4xVyV+Dv3n8DUOXnKmw7ue2m4zUK1mBjt40AlPmxDHsj9t50vFGJRixtvxSAgt8WJCwq7Kbjrcq0YlbrWQA88f0TRMdF4+7ijpuzG+7O7rxU+iUG1hoIQPPpzXFWzri7uOPubG71i9enVZlWxCfGM2TDENxd3MmXJR8dKnR4oO/3dXdr6+SttBBAhQrmNmSIuQI6ZYrZi2vKFPD1hcBAU4BWrSpXQ0X6cjXuKnP3zmXcjnGsO7IOJ+VEoxKN+O7572hSsgluzm5WR0xXIiIimDJlCqGhoYSGhrJ7924uXbrEtGnTaNOmDdu2baNu3bq3fd2iRYt44YUXWLFiBS1atLjt+Pr166lZsyZTp06lY8eOADg5OeHp6Un27NlZtGgR5cuXZ82aNUydOpUcOXKQI0cOsmfPTo4cOXj++efx8vLi0qVLxMfH4+3tjZOT00P/+7TWXL58mfPnz3PhwgUuXLjAtWvXaNiwIQDjxo0jJCSE8+fPJ5+TK1culixZAkCfPn04ceIEJUqUoHjx4pQoUYJSpUpRuHDhh84iMq74eLOP9+jRsHKlWWSwRQvTq1mnDjzCf21xC601YVFhtxWVdYrWoVWZVhyPPE6hkYVu+hpXJ1cKZi3Is4WepZB3Id6o+sZNBWWBrAXI5ZkLgPK5yzOj1YzHyuiknPDJ7INPZh/K+N68f17Hih3pWLHjTY/FxMckX6zr83QfahWuhUbj4uSCi5MLmVxuLJA3sOZAXvV/NfmYi5MLmV0zJx//pekvxMTH4Orsmnzc3fnGSu7LOywnOj6auIQ44hLjiEuIu+nv7aQWk4iKjUo+di3hGr6evsnHv6rzFZGxkcnH4hLj8PPxSz7e5IkmRMVGEZsQS2xCLNcSriVPh9FacyLqhDkWH5v8MZdnLlqVaUV0XDSfrPsEgEp5Kj100Xk3UnQKkYKTE9SsaW7ffQfLlpnC85df4IcfoFgxM/y2fXvw87v/8wmRFmmt2X5qO+P+Gse00GlExkZSPHtxvqzzJZ0qdqJA1gJWR3RoFy9eZPfu3cmFZWhoKG3btqVnz55cvXqVN998k5w5c1K+fHk6d+5MuXLlqFWrFgDly5dn/vz5JCQk3HSrVKkSABUrVmTs2LG3HS9WrBgAFSpU4LPPPkt+/PLly1y4cIHs2bMDcPToURYvXsyFCxeIjb3Rw3Ds2DG8vLwYNWoUAwcORClFtmzZkovSVatW4e3tzW+//camTZu4cuVKctEYExPDmjVrAOjYsSNTpky56fvh4+NDeHg4ACtXrmTNmjXJz5srVy5KlCiRfK5SioMHD7JixQpiYmIAqFmzJuvXrwegZ8+euLi4JBekxYsXp1ixYmTOnBkhwsLg55/N3+yTJ6FAAfj8c7Nnd758VqdzPHvD93Lk4pHkwvJY1DEq563Mm9XeJEEnUGRUERJ1YvL52T2yky+L+UbnzZKXYfWGUThbYQp5F6KQdyHyeOXBSZmK39fTl6H1h1ry77obDxeP5M+r5K9ClfxV7nruU3mfuudzFc527wtl2TNlJzvZ73r86fxP3/Prby2Yb/Vto2/vekwpRUjPu49SyOKehbiBpphNSEy463kPS4pOIe7C3d3M7Wze3Cw0MG+eKUC/+goGD4annjLFZ5s28sdMOIZzV88x+Z/JjN85nn/O/IOHiwetyrTilSdfoWbhmslvBsSDuXr1Knv37iU0NJQcOXLQtGlTYmNj8fHxISHB/KH28vKiXLlyuLmZK9gFCxbk1KlT5M6d+47DX319fWnevPldX7NIkSL07NnzrscrVKhAhQoV7nq8a9eudO3aFSB5qO6FCxfImzcvAA0bNsTT05MLFy4kHzt//nxyUbdhwwZGjhyJl5dXcuGYI0cOEhMTcXJy4uWXX6ZixYo39aLmyHFjDvD06dPvmg3gxx9/BCAxMZFTp05x8ODBm3pc9+/fzz///MPFixeTH2vfvj2TJ09Ga02fPn0oUKAAxYsXTy5MrxfciYmJXL58mfj4eOLi4pJvOXLkwNvbm+joaPbs2UNcXNxN55QpU4YCBQoQERHBqlWriI+PJ2/evHfskRapLzERVqwwczV/+81sf9Kokbn//PMyPeZBJSQmsP3Uds5eOUuTkk0AM0T0xKUTADgrZwpkLUA+L/OGx8XJhQnNJ+Cb2ZfC2QpTMGvBm4a9uji50L9G/9T/hwibuN47a0syp1OIh3TqFMyYYQrQkBAz3LZOHdMD2qIF5JA1Vh6KzHOyr4TEBFYdWsX4neOZv28+1xKu4Z/Pn1eefIW25drK6rOPoEePHqxfv54DBw5w/W/o888/nzxEdOzYsRQoUIBy5cpRqFAhmVtpB+fPn+fgwYMcPHiQvHnzUqtWLaKiovDz8+PUqVM3nTt48GA++ugjjh49SpEiRW57rpEjR/LGG28QGhpK+fLlbzs+btw4unXrxpYtW3jmmWeAm3/eD0raOttKTDQjkEaOhMOHzTZpr7wCPXrIVmgP6szlMyw/uJxlB5ax4uAKzkWfo7B3YQ6/cRilFCsOrsDT1ZNC3oXIlyUfzk7OVkcWDuBubZ0UnUI8hn//NXt8TZliFitwcYHataFlS1OA5sljdcK0T96I2cfhC4eZuHMiE3ZO4HjUcXJkykHHCh3p9mQ3KuS+e0+YuFlYWBhTpkxh165dTJ48GYBXXnmFqKgoypUrl3wrXrw4LtKlkiZcuXKFQ4cOcfDgQQ4cOEDlypWpXbs2ly5dIigoCFdXV1xcXHB1dcXV1ZUqVapQpkwZLl26xNq1a5Mfv37OE088Qa5cuYiOjubIkSO4urri5eVFnods4KWts51Ll6BzZzMCqWZN6NMHXnzRrEgr7i4uIY4/T/xJ9YLVcVJO9PqtF0F/BZHLMxeNSjSiUfFG1C9eH5/MssKSeHRSdAphR1rD9u0wd65Zkv3ff2/sD/rSS+aPoayDcWfyRsy2jlw8Qp/FfVh6YCkKRYPiDXjlyVdoVqoZ7i7u938CQVRUFHPmzGHy5MmsXbsWrTXPPPMMK1aswMvLy+p4wkFJW2cbhw6ZaS9798Lw4dCvnyzwdy9HLx5N7s1cdWgVl65dYkevHVTKU4l9EfuIjoumYp6KMr1C2IysXiuEHSkF/v7m9uWXsGePKT7nzoW33jK3ypVNAdqyJZQqZXVikR7N3jOb7gu7o9F8FvAZXSp1Sd7TS9xbXFwcCQkJeHh4MGvWLLp3707x4sUZNGgQ7du3v2mxGyEckVKqL9AeUMC3WusZtxx/GvgVWKC1HmBBxPtaswZatzYXepctg3r1rE6U9sTExxAbH4u3hzerD62mXrD5JhXyLkTbcm1pVKIRJXKY9izlaqdC2JsUnULYmFJQtqy5ffKJGXY7b54pQj/80NzKljXF50svma1a5CqteBzRcdG8tfwtxm4fS5X8VZj+0nSKZpdJTfejtWb79u0EBwczbdo0Bg4cyOuvv07r1q0pU6YM1apVk/mYIl1QShUHugHVAHdgq1Jqhdb6QorTKgOjgTS3NJ7WZv7mW2+ZleMXLIDixa1OlTZorfnv/H8sO7CMZQeWse7IOgY8O4BPan1C1QJVGd5gOM+XeB4/Hz9pz4SlpOgUws5KlID+/c0tLMwUoHPnmh7RL74w27BcL0CrVJH9w8TD2X12N23mtCH0bCjvVX+PwXUG4+rsanWsNE1rzZAhQwgODmbfvn24u7vTtGlTKlasCEDWrFmTF4wRIp2oAyzUWl8DrimlfgeqA4uvn6C1HqOU6kIaKzpjY+G112DcODOsNjgYsmS5/9dlBAmJCTw59kl2nd0FQMmcJenxVA/qF6sPgJebF28/87aVEYVIJkWnEKmoQAF4/XVzO3sWFi40PaCjRsE330D+/Gb+Z8uW8NxzstS7uDutNeN2jKPf0n5kcc/CsvbLaFiiodWx0qyLFy+yZcsWGjVqhFKK5cuX4+vry9tvv03r1q3Jli2b1RGFsCcfICLF/QjA9y7n3pVSqifQE6BQIfsP3T992lyQ3bwZBg6ETz/N2Bdmj148yvTQ6ew7t48JzSfg7ORMqzKt6O3fm0YlGlEsezGrIwpxV/KWVgiL5MoF3bub28WLsHixKUDHjTPDiHx8zFXdFi3MirienlYnFmlFZEwkPRf1ZObumdQrVo/gF4PJ4yVLJd/q2rVrLF26lMmTJ/Pbb7+RkJDA6dOnyZkzJytWrMDdXRZWEhnGBSBnivveSY89FK11EBAEZiEh20S7s5AQcxH2/HmYOdPM5cyIzl45y8zdM5kWOo3NxzcDUK1ANaLjosnkmolPan1icUIhHswjXy9SSnkqpX5USq1XSm1TSn1ly2BCZCTZskH79mbYbXg4zJ4NDRqYP7RNm0L27GYv0K+/hr/+MvuTCdtQSvVVSv2hlNqilAq8w/GnlVJ7lVJfW5HvVn+G/cmTY59kzp45DKk7hOUdlkvBmSQuLo6YmBgA1q9fT758+WjRogXr16+nV69e/PHHH+RI2khXCk6RwWwEGiulnJVSmYAAIEQpldXaWHc2daoZ7ePsDJs2ZbyCMzImksvXLgMwb+88Xl/6OlGxUXxZ50sO9jvIH6/8QSbXTBanFOLhPE5PpzcwTWu9USnlBOxVSn2ntT5to2xCZEienmY40UsvmbksGzbAihXm9sEH5ubjA/Xrm8K0fn0zLFc8PEdaXCNRJzJ883A+XPMh+bPkZ0PXDTxT8OZ5h1prIiMjcXd3J1OmTERHR3Pq1CmcnZ1xcXHB2dkZZ2dnvL29cXNzIyEhgYSEBJydnXFycnKoRSYSEhLYu3cvISEhbNu2jZCQEP7++2+CgoLo1KkTPj4+1K9fn44dO1K/fn1cXWWeq8i4tNahSqlFwGZAAyMwhWcg0MzCaDdJSDCL7Q0bBrVqwaxZ4PvQg4AdU3RcNIv+XcS00Gks/m8xoxqNord/bwLLBVKjUA3K5SpndUQhHssjF51a65PAyaS7nsA14KINMgkhkri7myXh69Uzf4RPn4ZVq24UodOmmfPKljUFaIMGZqPszJmtze1AbLK4hr3nOR06c4guk7uwYc8GqmWrxvctvse/oD8HDhzgnXfe4fTp05w+fZozZ84QGxvL9OnTCQwMZMuWLdSpU+e251u4cCFNmzZl8eLFNG/ePPnx68XpihUrqFmzJrNnz+bVV19NLlg9PDwoWLAgo0ePxs/Pj8OHD3Po0CGKFClCwYIFcbPTzuyJiYn8+++/hISEkC9fPurUqcPZs2cpX748AF5eXlSuXJm+fftStmxZAMqWLcu0678gQgi01kOAIbc8POWWcyba6/X/Pfcvbs5uZHHLQhb3LLg539xeXLwI7drB0qXw6qtmrYPUulYUHRfNoQuHiIyNJCo2iqtxV8mRKQflc5UnZ+ac93+CxxCfGE+3Bd2Yt28el69dJo9XHl71f5UaBWsAkM0jG9k8stk1gxCp4bHndCqlnDH7OvXXWsfccixVJ5wLkd7lyQMdOphbYiLs2nWjAP3pJ/j2W3BzM8OSGjY0RWj58hl74YX7sMniGo86zykhIYG//vrrpqLx9OnT1KtXjxYtWhAWFkZJv5JEX4lO/potbOH3/L/jX94fZ2dnjh49Su7cufHz8yNPnjzkyZOHSpUqAVC6dGkmTZpEfHw8CQkJyR/LlTNXzP38/Bg8eHByj+f14wUKFABMu926devkY1evXuX48eNkymSGdc2ZM4f+/fsDoJQib968FC5cmPnz55MrVy7++ecfTp48SeHChSlcuDCZH/JqyEcffcTmzZvZvn07ly5dAqBt27bUqVOHvHnzMmPGDCpUqEDJkiVxkv/kQqRptSbW4vTlG4PhXJ1c6VihI+Oaj2P/fnhqZF2ifZ2oNCQLl0t58eaKLNQuWptWZVqhtWbCzgl4uXmRxS2L+eiehXxZ8pHLMxcx8TGsP7KeqNio5FtkbCT1itXj2ULPcizyGD1+62Eej4lMPue757+jS6Uu/H3mb54Zd/uK1dNfmk5guUDWH1lPu7ntyO2Zm9xeucntmZtcnrno8VQPnsj5BOeunuPEpRPk9syNT2YfnJ2c7/p9SNSJbDi6gT3he3j16VdxcXLhXPQ5AssG0q58O2oVrnXPrxfCUT1W0amUcsUUnDO01stuPZ6aE86FyGicnKBiRXPr3x+uXr15KO5775lb7tw3D8XNI9P/UrLJ4hqPKj4+nipVqtz0WI4cOShQoABxCXGM+mcU0eWj8c3tS786/Xi61NPkyZOHIkWKAFC0aFF27tx51+fPkycPnTp1uuvxkiVL8tFHH931eJUqVW7Ll1KnTp2oXLkyR48e5ciRI8kfvb29ARg3bhzfffdd8vk+Pj4ULVqUzZs34+LiwoYNG7hw4QLx8fHJQ2Q9PT2ZP38+AOvWrSMhIYFOnTrh7++Pv78/fn43NjN/+eWX75pNCJG2jH5hNOejz3Mp9hKXr13m0rVLlM9VnqVLoW1biGvpSplKUcR7nGbDsctcir1EJtdMtCrTitiEWF5Z+MptzzmgxgCG1BtCVGwUjaY0uu24p6snzxZ6FmflTGRMJFnds5I/S3683b3J6p6VUjlLAVAqZylmtJpBVveseLt7k8k1E+ejz1PW14ycyOaRjYbFG3LmyhnOXD5D6NlQzl45S/NSzXki5xMs+W8JneabttZJOeGT2YdcnrmY1XoWfj5+bD2xlbWH1xJ+NZwZu2cQFhVGdo/sdH2yKx4uHixut/i27EKkN0rrR6sFlVJuwDRgttb6vmOY/P39dUhIyCO9lhDi4Z04cfNQ3Iik/rwKFUwBWquW2Rc0Vy5rcyqltmut/S167XLAWKAm4AZsAxoCl7TWUSnO6wL4aa0H3O85H7atW7p0KT4+PuTJk4dcuXLh7u7OkYtHaDenHX+E/UGPp3owstFIMrs63pjps2fP8t9//91UlF68eJEZM2YA0LJlS+bNmweAi4sLFSpUoFatWowYMQIwc1QdaZ6pEPdiZVtnD4/7vk5rs1XY+++bi6fz50Phwnc+N1EncjzyeHKxevnaZS5fu0zx7MUpn7s88YnxbD2xNblozOqeFS83L7v2GGqt0WiclBPHI4+zJWxLclF69spZzlw5w08v/ES+LPkYtmkY7696H1cnVxqVaETbcm1pWqopXm5edssnhFXu1tY9TtHZB/gC2JXi4Xe01tvvdL4UnUJYJzERdu68UYBu3AhxceZY4cKm+Lx+e+op8ErFv4NWvxFTSn0AtMAsrhEExAKBWutmKc7pgp2KzlvN2TOHVxa+gkYT1CSIwHK3LaibbkRERHDo0CGUUpQvXx4PDw+rIwlhN1a3dbb2OG1ddDT06AFTpsDLL8P48el/W7Ar166g0VJoinTP5kXnw5KiU4i048oVs/XK1q03bkeOmGNOTmZhoqefvlGIlitnvwUd5I2YER0XzdvL32bM9jFUyV+FaS9Nk42+hUhHpK0zwsLM/tN//QWDB5sV2WVAgxDpx93ausdeSEgI4Xg8Pc1iQ889d+Oxs2dh27YbReiCBebqM4CHh+kBTdkjWqyYvFGwlT3hewicHUjo2VD6V+/P4DqDb1vZUQghHN3mzdCypVmDYMECsw+1ECJjkKJTCAGYuZ0vvGBuYObbHD58c2/omDEwcqQ5niOHKT5T9ohaPT/U0WitGbdjHP2W9sPLzYtl7ZfRsERDq2MJIYTNjRtntkIpVAjWrIEyZaxOJIRITVJ0CiHuSCnTm1msGLRpYx6Li4Pdu00Ber1X9MsvzZxRMPNDR42CFFs/irvQWtNhXgem7ppK3aJ1CX4xmLxZ8lodSwghbO7dd2H4cLOC+vTp5qKlECJjkaJTCPHAXF2hUiVz69nTPHbr/FDZkuXBKKUomaMkX9X5iveffR8nJftMCiHSp5Il4a23YNgwcJF3nkJkSPKrL4R4LHeaHyoezKCAQVZHEEIIu7t+kVIIkXHJpXUhhBBCCCGEEHYjRacQQgghhBBCCLuRolMIIYQQQgghhN1I0SmEEEIIIYQQwm6k6BRCCCGEEEIIYTdSdAohhBBCCCGEsBspOoUQQgghhBBC2I0UnUIIIYQQQggh7EZprVPnhZQKB44+xJf4ABF2imNrjpQVHCuvZLWftJK3sNba1+oQtiJtXZriSHkdKSs4Vt60klXaurTxc3hQjpRXstqPI+VNK1nv2NalWtH5sJRSIVprf6tzPAhHygqOlVey2o+j5U2vHOnn4EhZwbHyOlJWcKy8jpQ1PXO0n4Mj5ZWs9uNIedN6VhleK4QQQgghhBDCbqToFEIIIYQQQghhN2m56AyyOsBDcKSs4Fh5Jav9OFre9MqRfg6OlBUcK68jZQXHyutIWdMzR/s5OFJeyWo/jpQ3TWdNs3M6hRBCCCGEEEI4vrTc0ymEEEIIIYQQwsFJ0SmEEEIIIYQQwm7SXNGplOqrlPpDKbVFKRVodZ57UUp5KqV+VEqtV0ptU0p9ZXWm+1HGSqXURKuz3I9SqrBSarVSarNSaqNSysPqTHeilMqklJqqlNqU9P/gc6sz3UopVSrp+zg9xWNfJj32h1IqwLp0GZejtHfS1tmXtHW2I21d2iRtnf1IW2d7jtDWgeO1dy5WB0hJKVUc6AZUA9yBrUqpFVrrC9YmuytvYJrWeqNSygnYq5T6Tmt92upg99AHCAWyWx3kXpRSzsAMoKvWeq9SyllrnWB1rrvoAlzQWrdLyr1ZKTVPa73D4lwpVQW+A1oAKKXqAJW01tWVUvmANUqpclrreAszZigO1t5JW2cn0tbZnLR1aYy0dXYnbZ3tdSHtt3XgYO1dWuvprAMs1Fpf01pfAn4Hqluc6a601ie11huT7noC14CL1iW6N6VUEeAF4HuLozyI54H9wJdKqU3AqxbnuZfTQLakhikz4AykqT+mWutfMTmvqwvMSjp2EjgKlLIgWkbmMO2dtHV2JW2dDUlblyZJW2cn0tbZTZpv68Dx2ru0VnT6ABEp7kcAvhZleWBJ/yl/BfprrWOsznMnSimFuRryOpBocZwH4QeUBjoDDYCuSqkK1ka6M631PMz/1UPAf8BPWusjloa6P4f8XUtnHO5nIG2dXUhbZ18O93uWDjncz0DaOruQts7+0vTvWlorOi9ghjZc500avLKQklLKFZgMzNBaL7M6zz30BpZrrQ9aHeQBJWCujF7SWl8BVgEVLc50R0qpXoACigFFgKZKqQaWhro/h/tdS4cc6mcgbZ3dSFtnXw71e5ZOOdTPQNo6u5G2zv7S9O9aWis6NwKNlVLOSqlMQACw1dpId6eUcgOmY36Jpt/vfIs9DdRMmmw8BqillPrE4kz3shEISPq/4ALUAHZZnOluSgHHtNYJSVdET5OGhjPcxUagGYBSygeTd7+liTIeh2nvpK2zK2nr7EvaOutJW2cf0tbZjyO2dZDG27s0tZCQ1jpUKbUI2AxoYITW+pTFse6lO6bxzJl0VQTgHa31dusi3ZnWutv1z5NWs+qitU6Tq3EBaK23KaVWAiFALDBda73T2lR39T9gglLqRczv1BFggqWJ7m8J0EAptRlz8emNtDqEKL1ysPZO2jo7kbbO7qSts5i0dfYhbZ1dOWJbB2m8vVNaa6szCCGEEEIIIYRIp9La8FohhBBCCCGEEOmIFJ1CCCGEEEIIIexGik4hhBBCCCGEEHYjRacQQgghhBBCCLuRolMIIYQQQgghhN1I0SmEEEIIIYQQwm6k6BSPRSlVRCkVpZRal+L2tg2ed6JSqpEtMgohxOOStk4IkRFIWyfsxcXqACJd2KO1DrA6hBBC2Jm0dUKIjEDaOmFz0tMpbE4pFaCUWq2UmqKU2qCU+k0plVUp5Z302Jqkx7snne+jlJqe9Ni6FFfCnkt6nl1KqXYW/pOEEOI20tYJITICaeuELSittdUZhANTShUB/gH+SvHwWqALUFFrHaWUGgZEADmBMK3190opV2A90BX4ANiitR6T9Jw5geFAota6m1KqILBEa10+lf5ZQghxE2nrhBAZgbR1wl5keK2whZuGYSilAoDiWuuopIf+AOoDRYBpAFrrOKXUNqAC4A+8c/3rtdbnlFIAc5PuH1dK+dj7HyGEEPchbZ0QIiOQtk7YnAyvFfbip5S6flGjDrAr6VYTIOmK2NMpHm92/QuVUr5Jn8aneD5l78BCCPEIpK0TQmQE0taJxyI9ncIWyiil1qW4vxDTmMxTSuUADgMTAHdgtFKqBeAGTNRa71NKvZP0+CuYBmloaoYXQogHJG2dECIjkLZO2JzM6RQ2lzQMo7fWuo3FUYQQwm6krRNCZATS1glbkOG1QgghhBBCCCHsRopOIYQQQgghhBB2I8NrhRBCCCGEEELYjfR0CiGEEEIIIYSwGyk6hRBCCCGEEELYjRSdQgghhBBCCCHsRopOIYQQQgghhBB2I0WnEEIIIYQQQgi7+T8npKA8p2zhHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 시각화\n",
    "# training result\n",
    "plt.figure(figsize=(16, 4))\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(history.history['loss'], 'b-', label='loss')\n",
    "plt.plot(history.history['val_loss'], 'r--', label='val_loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(history.history['start_accuracy'], 'g-', label='start_accuracy')\n",
    "plt.plot(history.history['val_start_accuracy'], 'k--', label='val_start_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(history.history['end_accuracy'], 'b-', label='end_accuracy')\n",
    "plt.plot(history.history['val_end_accuracy'], 'g--', label='val_end_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. BERT\n",
    "- Transformer Encoder 구조만을 활용\n",
    "- decoder가 없다면 어떻게 학습시키는걸까?\n",
    "  - Mask LM\n",
    "  - NSP\n",
    "- Mask LM\n",
    "  - 입력 데이터가 `나는 <mask> 먹었다` 일 때 BERT 모델이 `<mask>`가 `밥을` 임을 맞출 수 있도록 하는 언어 모델\n",
    "- NSP\n",
    "  - Next Sentence Prediction\n",
    "  - 입력데이터가 `나는 밥을 먹었다. <SEP> 그래서 지금 배가 부르다.`가 주어졌을 때 `<SEP>`를 경계로 좌우 두 문장이 순서대로 이어지는 문장이 맞는지를 맞추는 문제\n",
    "  - BERT 모델은 이 두 문장을 입력으로 받았을 때 첫 번째 바이트에 NSP 결과값을 리턴하게 된다.\n",
    "- 실제로 입력되는 것은 `Token, Segment, Position Embedding`의 3가지가 더해진 형태\n",
    "  - 실제로는 그 이후 layer normalization과 dropout이 추가로 적용된다.\n",
    "  - Token Embedding\n",
    "  - Segment Embedding\n",
    "  - Position Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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)  # token이 i_pad와 같다면 float형으로 변환\n",
    "    mask = tf.expand_dims(mask, axis=1)  # axis로 지정된 차원을 추가\n",
    "    return mask\n",
    "\n",
    "\n",
    "def get_ahead_mask(tokens, i_pad=0):\n",
    "    \"\"\"\n",
    "    ahead mask 계산하는 함수 : 다음 단어를 예측할 때, 그 다음 단어 및 뒤에 나오는 문장을 가림\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": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mode == \"embedding\" 일 경우 Token Embedding Layer 로 사용되는 layer 클래스 \n",
    "\n",
    "class SharedEmbedding(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Weighed Shared Embedding Class\n",
    "    \"\"\"\n",
    "    def __init__(self, config, name=\"weight_shared_embedding\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param config: Config 객체\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "\n",
    "        self.n_vocab = config.n_vocab\n",
    "        self.d_model = config.d_model\n",
    "    \n",
    "    def build(self, input_shape):\n",
    "        \"\"\"\n",
    "        shared weight 생성\n",
    "        :param input_shape: Tensor Shape (not used)\n",
    "        \"\"\"\n",
    "        with tf.name_scope(\"shared_embedding_weight\"):\n",
    "            self.shared_weights = self.add_weight(\n",
    "                \"weights\",\n",
    "                shape=[self.n_vocab, self.d_model],\n",
    "                initializer=kernel_initializer()\n",
    "            )\n",
    "\n",
    "    def call(self, inputs, mode=\"embedding\"):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param inputs: 입력\n",
    "        :param mode: 실행 모드\n",
    "        :return: embedding or linear 실행 결과\n",
    "        \"\"\"\n",
    "        # mode가 embedding일 경우 embedding lookup 실행\n",
    "        if mode == \"embedding\":\n",
    "            return self._embedding(inputs)\n",
    "        # mode가 linear일 경우 linear 실행\n",
    "        elif mode == \"linear\":\n",
    "            return self._linear(inputs)\n",
    "        # mode가 기타일 경우 오류 발생\n",
    "        else:\n",
    "            raise ValueError(f\"mode {mode} is not valid.\")\n",
    "    \n",
    "    def _embedding(self, inputs):\n",
    "        \"\"\"\n",
    "        embedding lookup\n",
    "        :param inputs: 입력\n",
    "        \"\"\"\n",
    "        embed = tf.gather(self.shared_weights, tf.cast(inputs, tf.int32))\n",
    "        return embed\n",
    "\n",
    "    def _linear(self, inputs):  # (bs, n_seq, d_model)\n",
    "        \"\"\"\n",
    "        linear 실행\n",
    "        :param inputs: 입력\n",
    "        \"\"\"\n",
    "        n_batch = tf.shape(inputs)[0]\n",
    "        n_seq = tf.shape(inputs)[1]\n",
    "        inputs = tf.reshape(inputs, [-1, self.d_model])  # (bs * n_seq, d_model)\n",
    "        outputs = tf.matmul(inputs, self.shared_weights, transpose_b=True)\n",
    "        outputs = tf.reshape(outputs, [n_batch, n_seq, self.n_vocab])  # (bs, n_seq, n_vocab)\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionalEmbedding(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Positional Embedding Class\n",
    "    \"\"\"\n",
    "    def __init__(self, config, name=\"position_embedding\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param config: Config 객체\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "        \n",
    "        self.embedding = tf.keras.layers.Embedding(config.n_seq, config.d_model, embeddings_initializer=kernel_initializer())\n",
    "\n",
    "    def call(self, inputs):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param inputs: 입력\n",
    "        :return embed: positional embedding lookup 결과\n",
    "        \"\"\"\n",
    "        position = tf.cast(tf.math.cumsum(tf.ones_like(inputs), axis=1, exclusive=True), tf.int32)\n",
    "        embed = self.embedding(position)\n",
    "        return embed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ScaleDotProductAttention(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Scale Dot Product Attention Class\n",
    "    \"\"\"\n",
    "    def __init__(self, name=\"scale_dot_product_attention\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "\n",
    "    def call(self, Q, K, V, attn_mask):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param Q: Q value\n",
    "        :param K: K value\n",
    "        :param V: V value\n",
    "        :param attn_mask: 실행 모드\n",
    "        :return attn_out: attention 실행 결과\n",
    "        \"\"\"\n",
    "        attn_score = tf.matmul(Q, K, transpose_b=True)\n",
    "        scale = tf.math.sqrt(tf.cast(tf.shape(K)[-1], tf.float32))\n",
    "        attn_scale = tf.math.divide(attn_score, scale)\n",
    "        attn_scale -= 1.e9 * attn_mask\n",
    "        attn_prob = tf.nn.softmax(attn_scale, axis=-1)\n",
    "        attn_out = tf.matmul(attn_prob, V)\n",
    "        return attn_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttention(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Multi Head Attention Class\n",
    "    \"\"\"\n",
    "    def __init__(self, config, name=\"multi_head_attention\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param config: Config 객체\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "\n",
    "        self.d_model = config.d_model\n",
    "        self.n_head = config.n_head\n",
    "        self.d_head = config.d_head\n",
    "\n",
    "        # Q, K, V input dense layer\n",
    "        self.W_Q = tf.keras.layers.Dense(config.n_head * config.d_head, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "        self.W_K = tf.keras.layers.Dense(config.n_head * config.d_head, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "        self.W_V = tf.keras.layers.Dense(config.n_head * config.d_head, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "        # Scale Dot Product Attention class\n",
    "        self.attention = ScaleDotProductAttention(name=\"self_attention\")\n",
    "        # output dense layer\n",
    "        self.W_O = tf.keras.layers.Dense(config.d_model, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "\n",
    "    def call(self, Q, K, V, attn_mask):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param Q: Q value\n",
    "        :param K: K value\n",
    "        :param V: V value\n",
    "        :param attn_mask: 실행 모드\n",
    "        :return attn_out: attention 실행 결과\n",
    "        \"\"\"\n",
    "        # reshape Q, K, V, attn_mask\n",
    "        batch_size = tf.shape(Q)[0]\n",
    "        Q_m = tf.transpose(tf.reshape(self.W_Q(Q), [batch_size, -1, self.n_head, self.d_head]), [0, 2, 1, 3])  # (bs, n_head, Q_len, d_head)\n",
    "        K_m = tf.transpose(tf.reshape(self.W_K(K), [batch_size, -1, self.n_head, self.d_head]), [0, 2, 1, 3])  # (bs, n_head, K_len, d_head)\n",
    "        V_m = tf.transpose(tf.reshape(self.W_V(V), [batch_size, -1, self.n_head, self.d_head]), [0, 2, 1, 3])  # (bs, n_head, K_len, d_head)\n",
    "        attn_mask_m = tf.expand_dims(attn_mask, axis=1)\n",
    "        # Scale Dot Product Attention with multi head Q, K, V, attn_mask\n",
    "        attn_out = self.attention(Q_m, K_m, V_m, attn_mask_m)  # (bs, n_head, Q_len, d_head)\n",
    "        # transpose and liner\n",
    "        attn_out_m = tf.transpose(attn_out, perm=[0, 2, 1, 3])  # (bs, Q_len, n_head, d_head)\n",
    "        attn_out = tf.reshape(attn_out_m, [batch_size, -1, config.n_head * config.d_head])  # (bs, Q_len, d_model)\n",
    "        attn_out = self.W_O(attn_out) # (bs, Q_len, d_model)\n",
    "\n",
    "        return attn_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionWiseFeedForward(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    Position Wise Feed Forward Class\n",
    "    \"\"\"\n",
    "    def __init__(self, config, name=\"feed_forward\"):\n",
    "        \"\"\"\n",
    "        생성자\n",
    "        :param config: Config 객체\n",
    "        :param name: layer name\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "\n",
    "        self.W_1 = tf.keras.layers.Dense(config.d_ff, activation=gelu, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "        self.W_2 = tf.keras.layers.Dense(config.d_model, kernel_initializer=kernel_initializer(), bias_initializer=bias_initializer())\n",
    "\n",
    "    def call(self, inputs):\n",
    "        \"\"\"\n",
    "        layer 실행\n",
    "        :param inputs: inputs\n",
    "        :return ff_val: feed forward 실행 결과\n",
    "        \"\"\"\n",
    "        ff_val = self.W_2(self.W_1(inputs))\n",
    "        return ff_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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",
   "metadata": {},
   "source": [
    "위까지는 BERT를 구성하는 레이어.  \n",
    "아래는 BERT 모델 구현"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "BERT 레이어에 Fully Connected layer를 붙어 KorQuAD용으로 finetune하기 위한 모델 클래스"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'d_model': 256,\n",
       " 'n_head': 4,\n",
       " 'd_head': 64,\n",
       " 'dropout': 0.1,\n",
       " 'd_ff': 1024,\n",
       " 'layernorm_epsilon': 0.001,\n",
       " 'n_layer': 3,\n",
       " 'n_seq': 384,\n",
       " 'n_vocab': 32007,\n",
       " 'i_pad': 0}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = Config({\"d_model\": 256, \"n_head\": 4, \"d_head\": 64, \"dropout\": 0.1, \"d_ff\": 1024, \"layernorm_epsilon\": 0.001, \"n_layer\": 3, \"n_seq\": 384, \"n_vocab\": 0, \"i_pad\": 0})\n",
    "config.n_vocab = len(vocab)\n",
    "config.i_pad = vocab.pad_id()\n",
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모델의 크기가 다르고, 사용할 수 있는 배치 사이즈가 달라지므로, 배치 구성만 다시 진행하겠습니다. 메모리 한계 때문에 배치 사이즈는 32 정도가 적당할 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "bert_batch_size = 32 \n",
    "\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((train_inputs, train_labels)).shuffle(10000).batch(bert_batch_size)\n",
    "dev_dataset = tf.data.Dataset.from_tensor_slices((dev_inputs, dev_labels)).batch(bert_batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = BERT4KorQuAD(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_epoch(model, dataset, loss_fn, acc_fn):\n",
    "    metric_start_loss = tf.keras.metrics.Mean(name='start_loss')\n",
    "    metric_end_loss = tf.keras.metrics.Mean(name='end_loss')\n",
    "    metric_start_acc = tf.keras.metrics.Mean(name='start_acc')\n",
    "    metric_end_acc = tf.keras.metrics.Mean(name='end_acc')\n",
    "\n",
    "    for batch, ((enc_tokens, segments), (start_labels, end_labels)) in enumerate(dataset):\n",
    "        start_outputs, end_outputs = model(enc_tokens, segments)\n",
    "\n",
    "        start_loss = loss_fn(start_labels, start_outputs)\n",
    "        end_loss = loss_fn(end_labels, end_outputs)\n",
    "\n",
    "        start_acc = acc_fn(start_labels, start_outputs)\n",
    "        end_acc = acc_fn(end_labels, end_outputs)\n",
    "\n",
    "        metric_start_loss(start_loss)\n",
    "        metric_end_loss(end_loss)\n",
    "        metric_start_acc(start_acc)\n",
    "        metric_end_acc(end_acc)\n",
    "\n",
    "    return metric_start_loss.result(), metric_end_loss.result(), metric_start_acc.result(), metric_end_acc.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8309292c61cb42ab88ca25d575646756",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "|          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 0 >> loss: 4.6135, 5.1685, acc: 0.0270, 0.0121\n",
      "save best model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bba229ba8c85487794aafd9f8259ec30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "|          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 1 >> loss: 4.0921, 4.6083, acc: 0.0794, 0.0586\n",
      "save best model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32a63e5f887f4610b0a7e80e3478e319",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "|          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 2 >> loss: 3.8846, 4.4139, acc: 0.1104, 0.0995\n",
      "save best model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91904e708f104c6d9f2ce9d414c8f7a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "|          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 3 >> loss: 3.8413, 4.3405, acc: 0.1283, 0.1117\n",
      "save best model\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ee49a0c23ec4c7f8f78bca982df771b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "|          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 4 >> loss: 3.8481, 4.3960, acc: 0.1203, 0.1146\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9e1c45764754516b60e451341d40bcf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "|          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 5 >> loss: 3.9427, 4.4814, acc: 0.1183, 0.1166\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "240aa1ae5d9d4cdd82bc7dd6b09b72c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "|          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 6 >> loss: 4.4002, 5.1656, acc: 0.1211, 0.1073\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb70f920e06045cca134aa5de1cfa399",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "|          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 7 >> loss: 4.6285, 5.4918, acc: 0.1097, 0.1094\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ebc8fd973c24ef7a7b19c76e9f4dfe1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "|          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval 8 >> loss: 4.7980, 5.6372, acc: 0.1080, 0.1074\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(20):\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 5 <= patience:\n",
    "        print(f'early stopping')\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 모델에는 수많은 코퍼스를 통해 정교하게 얻어진 Word Embedding이 반영되지 않았기 때문에 성능이 크게 차이나지 않는다.\n",
    "- 그렇다면 pretrained model을 활용하여 finetuning했을 때의 결과는?"
   ]
  }
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