315 lines
7.6 KiB
Text
315 lines
7.6 KiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"aug_data_path = \"/Users/minjoons/data/squad/train-v1.0-aug.json\"\n",
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"aug_data = json.load(open(aug_data_path, 'r'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(['Saint', 'Bernadette', 'Soubirous'], 'Saint Bernadette Soubirous')\n",
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"(['a', 'copper', 'statue', 'of', 'Christ'], 'a copper statue of Christ')\n",
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"(['the', 'Main', 'Building'], 'the Main Building')\n",
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"(['a', 'Marian', 'place', 'of', 'prayer', 'and', 'reflection'], 'a Marian place of prayer and reflection')\n"
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]
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}
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],
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"source": [
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"def compare_answers():\n",
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" for article in aug_data['data']:\n",
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" for para in article['paragraphs']:\n",
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" deps = para['deps']\n",
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" nodess = []\n",
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" for dep in deps:\n",
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" nodes, edges = dep\n",
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" if dep is not None:\n",
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" nodess.append(nodes)\n",
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" else:\n",
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" nodess.append([])\n",
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" wordss = [[node[0] for node in nodes] for nodes in nodess]\n",
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" for qa in para['qas']:\n",
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" for answer in qa['answers']:\n",
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" text = answer['text']\n",
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" word_start = answer['answer_word_start']\n",
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" word_stop = answer['answer_word_stop']\n",
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" answer_words = wordss[word_start[0]][word_start[1]:word_stop[1]]\n",
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" yield answer_words, text\n",
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"\n",
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"ca = compare_answers()\n",
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"print(next(ca))\n",
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"print(next(ca))\n",
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"print(next(ca))\n",
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"print(next(ca))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"x: .\n",
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"x: .\n",
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"x: .\n",
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"x: .\n",
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"q: k\n",
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"q: j\n",
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"q: n\n",
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"q: b\n",
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"q: v\n",
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"x: .\n",
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"x: :208\n",
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"x: .\n",
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"x: .\n",
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"q: dd\n",
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"q: dd\n",
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"q: dd\n",
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"q: dd\n",
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"q: d\n",
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"x: .\n",
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"x: :411\n",
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"x: .\n",
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"x: .\n",
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"x: .\n",
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"x: .\n",
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"x: .\n",
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"x: .\n",
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"x: :40\n",
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"x: .\n",
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"x: *\n",
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"x: :14\n",
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"x: .\n",
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"x: .\n",
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"x: .\n",
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"x: :131\n",
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"x: .\n",
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"x: .\n",
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"x: .\n",
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"x: .\n",
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"53 10\n"
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]
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}
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],
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"source": [
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"def nodep_counter():\n",
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" x_count = 0\n",
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" q_count = 0\n",
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" for article in aug_data['data']:\n",
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" for para in article['paragraphs']:\n",
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" deps = para['deps']\n",
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" nodess = []\n",
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" for sent, dep in zip(para['sents'], deps):\n",
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" if dep is None:\n",
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" print(\"x:\", sent)\n",
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" x_count += 1\n",
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" for qa in para['qas']:\n",
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" if qa['dep'] is None:\n",
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" print(\"q:\", qa['question'])\n",
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" q_count += 1\n",
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" print(x_count, q_count)\n",
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"nodep_counter()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0\n"
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]
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}
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],
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"source": [
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"def bad_node_counter():\n",
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" count = 0\n",
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" for article in aug_data['data']:\n",
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" for para in article['paragraphs']:\n",
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" sents = para['sents']\n",
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" deps = para['deps']\n",
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" nodess = []\n",
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" for dep in deps:\n",
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" if dep is not None:\n",
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" nodes, edges = dep\n",
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" for node in nodes:\n",
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" if len(node) != 5:\n",
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" count += 1\n",
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" print(count)\n",
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"bad_node_counter() "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"36\n"
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]
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}
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],
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"source": [
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"def noanswer_counter():\n",
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" count = 0\n",
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" for article in aug_data['data']:\n",
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" for para in article['paragraphs']:\n",
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" deps = para['deps']\n",
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" nodess = []\n",
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" for dep in deps:\n",
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" if dep is not None:\n",
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" nodes, edges = dep\n",
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" nodess.append(nodes)\n",
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" else:\n",
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" nodess.append([])\n",
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" wordss = [[node[0] for node in nodes] for nodes in nodess]\n",
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" for qa in para['qas']:\n",
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" for answer in qa['answers']:\n",
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" text = answer['text']\n",
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" word_start = answer['answer_word_start']\n",
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" word_stop = answer['answer_word_stop']\n",
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" if word_start is None:\n",
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" count += 1\n",
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" print(count)\n",
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"noanswer_counter()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"106\n"
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]
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}
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],
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"source": [
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"def mult_sent_answer_counter():\n",
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" count = 0\n",
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" for article in aug_data['data']:\n",
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" for para in article['paragraphs']:\n",
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" for qa in para['qas']:\n",
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" for answer in qa['answers']:\n",
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" text = answer['text']\n",
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" word_start = answer['answer_word_start']\n",
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" word_stop = answer['answer_word_stop']\n",
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" if word_start is not None and word_start[0] != word_stop[0]:\n",
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" count += 1\n",
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" print(count)\n",
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"mult_sent_answer_counter()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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