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264 lines
6.1 KiB
264 lines
6.1 KiB
{
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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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"outputs": [],
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"source": [
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"import SentSeg\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": 3,
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"metadata": {},
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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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"[[['Hallo', 'was', 'gehe', 'denn', 'hier', 'so.']], [['Ich', 'habe', 'echt', 'keine', 'Ahnung', 'verdammt.']], [['I.', 'd.', 'R.', 'gibt', 'es', 'keine', 'Abschiebungen.']], [['Ende', 'd.', 'J.', 'wird', 'alles', 'problematisch.']], [['Sie', 'gingen', 'nach', 'Hause,', 'weil', 'es', 'in', 'Strömen', 'regnete.']], [['Heute', 'war', 'die', 'Straße', 'blau', 'angemalt,', 'damit', 'der', 'Marathon', 'funktionierte.']], [['Er', 'habe', 'es', 'sehr', 'schwer.']], [['Es', 'war', 'die', 'Hose', 'des', 'Gauners.']], [['Bliblablub.']], [['Sie', 'ist', 'nicht', 'schön', 'heute.']], [['Oleoleole.']], [['Mannoman.']], [['Er', 'ginge', 'nicht', 'schnell.']], [['Die', 'Hühner', 'lieben', 'sich', 'nicht.']]]\n"
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]
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}
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],
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"source": [
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"sent_seg = SentSeg.SentSeg('de')\n",
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"\n",
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" \n",
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"sentences = sent_seg.ReadDoc2Sent('atest1')\n",
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"print(sentences)"
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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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"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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"initializing the gs utils..\n",
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"loading spacy..\n",
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"done\n",
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"done\n",
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"loading the Stochastic Gradient models..\n",
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"done\n",
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"initializing the SGM..\n",
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"loading vectorizer..\n",
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"done\n",
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"loading the SGD model..\n",
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"done\n",
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"loading spacy..\n",
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"done\n",
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"done\n",
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"importing spacy..\n",
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"done\n",
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"importing german model..\n",
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"done\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'done'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sent_seg.LoadSentGlueSGDandGSUtils()"
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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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"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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"creating array of comma or not..\n",
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"done\n"
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]
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}
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],
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"source": [
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"sentences = sent_seg.CommaSentenceOrNot(sentences)\n",
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"print(sentences)"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"sentences = sent_seg.GetUtteranceNumber(sentences)"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"sentences = sent_seg.GetQuestionOrNot(sentences)"
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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": 8,
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"metadata": {},
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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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"importing spacy..\n",
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"done\n"
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]
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}
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],
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"source": [
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"sentences1 = sent_seg.SplitSentencesIntoHauptNebenTuple(sentences)"
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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": 9,
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"metadata": {},
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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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"['es', 'regnete', 'in Strömen']\n",
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"['deswegen', 'Sie', 'gingen', 'nach Hause']\n",
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"['Heute', 'war', 'blau', 'angemalt', 'die Straße']\n",
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"100\n",
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"['dann', 'funktionierte', 'der Marathon']\n"
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]
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}
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],
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"source": [
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"outsentences = sent_seg.SplitCommatas(sentences1)"
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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": 18,
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"metadata": {},
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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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"[['Hallo', 'was', 'gehe', 'denn', 'hier', 'so'], ['Ich', 'habe', 'echt', 'keine', 'Ahnung', 'verdammt'], ['I.', 'd.', 'R.', 'gibt', 'es', 'keine', 'Abschiebungen'], ['Ende', 'd.', 'J.', 'wird', 'alles', 'problematisch'], ['in', 'Strömen', 'regnete', 'e'], ['deswegen', 'gingen', 'Sie', 'nach', 'Haus'], ['angemalt', 'war', 'die', 'Straße', 'blau', 'Heut'], ['dann', 'der', 'Marathon', 'funktioniert'], ['Er', 'habe', 'es', 'sehr', 'schwer'], ['Es', 'war', 'die', 'Hose', 'des', 'Gauners'], ['Bliblablub'], ['Oleoleole'], ['Mannoman'], ['Er', 'ginge', 'nicht', 'schnell'], ['Der', 'Satz', 'davor', 'funktioniert', 'nicht', 'im', 'Modul', 'Konjunktsolve'], ['Weil', 'er', 'zu', 'viele', 'verben', 'hat']]\n"
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]
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}
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],
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"source": [
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"print(outsentences)"
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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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"punctuations = []\n",
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"for n in range(len(outsentences)):\n",
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" punctuations.append('.')\n",
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" if outsentences[n][-1][-1] == '.':\n",
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" outsentences[n][-1] = outsentences[n][-1][:-1]"
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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": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"from oi import *\n",
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"oi = oi()"
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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": 21,
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"metadata": {},
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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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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n",
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".\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'OK'"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"oi.PrintSplitSentencesToTextFile(punctuations, outsentences, 'test1out')\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": null,
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"metadata": {},
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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": "3.5.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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