Wiki-Corpus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Database of all Spanish Wikipedia articles with >10000 words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_spanish/eswiki+100/AI\n",
      "data_spanish/eswiki+100/AI/wiki_13\n",
      "data_spanish/eswiki+100/AI/wiki_40\n",
      "data_spanish/eswiki+100/AI/wiki_39\n",
      "data_spanish/eswiki+100/AI/wiki_31\n",
      "data_spanish/eswiki+100/AI/wiki_65\n",
      "data_spanish/eswiki+100/AI/wiki_95\n",
      "data_spanish/eswiki+100/AI/wiki_85\n",
      "data_spanish/eswiki+100/AI/wiki_52\n",
      "data_spanish/eswiki+100/AI/wiki_18\n",
      "data_spanish/eswiki+100/AI/wiki_84\n",
      "data_spanish/eswiki+100/AI/wiki_87\n",
      "data_spanish/eswiki+100/AI/wiki_42\n",
      "data_spanish/eswiki+100/AI/wiki_61\n",
      "data_spanish/eswiki+100/AI/wiki_47\n",
      "data_spanish/eswiki+100/AI/wiki_50\n",
      "data_spanish/eswiki+100/AI/wiki_45\n",
      "data_spanish/eswiki+100/AI/wiki_01\n",
      "data_spanish/eswiki+100/AI/wiki_35\n",
      "data_spanish/eswiki+100/AI/wiki_58\n",
      "data_spanish/eswiki+100/AI/wiki_00\n",
      "data_spanish/eswiki+100/AI/wiki_55\n",
      "data_spanish/eswiki+100/AI/wiki_08\n",
      "data_spanish/eswiki+100/AI/wiki_54\n",
      "data_spanish/eswiki+100/AI/wiki_46\n",
      "data_spanish/eswiki+100/AI/wiki_24\n",
      "data_spanish/eswiki+100/AI/wiki_38\n",
      "data_spanish/eswiki+100/AI/wiki_81\n",
      "data_spanish/eswiki+100/AI/wiki_74\n",
      "data_spanish/eswiki+100/AI/wiki_22\n",
      "data_spanish/eswiki+100/AI/wiki_56\n",
      "data_spanish/eswiki+100/AI/wiki_14\n",
      "data_spanish/eswiki+100/AI/wiki_64\n",
      "data_spanish/eswiki+100/AI/wiki_51\n",
      "data_spanish/eswiki+100/AI/wiki_76\n",
      "data_spanish/eswiki+100/AI/wiki_60\n",
      "data_spanish/eswiki+100/AI/wiki_29\n",
      "data_spanish/eswiki+100/AI/wiki_90\n",
      "data_spanish/eswiki+100/AI/wiki_66\n",
      "data_spanish/eswiki+100/AI/wiki_79\n",
      "data_spanish/eswiki+100/AI/wiki_23\n",
      "data_spanish/eswiki+100/AI/wiki_62\n",
      "data_spanish/eswiki+100/AI/wiki_78\n",
      "data_spanish/eswiki+100/AI/wiki_77\n",
      "data_spanish/eswiki+100/AI/wiki_44\n",
      "data_spanish/eswiki+100/AI/wiki_20\n",
      "data_spanish/eswiki+100/AI/wiki_93\n",
      "data_spanish/eswiki+100/AI/wiki_21\n",
      "data_spanish/eswiki+100/AI/wiki_34\n",
      "data_spanish/eswiki+100/AI/wiki_86\n",
      "data_spanish/eswiki+100/AI/wiki_43\n",
      "data_spanish/eswiki+100/AI/wiki_72\n",
      "data_spanish/eswiki+100/AI/wiki_48\n",
      "data_spanish/eswiki+100/AI/wiki_67\n",
      "data_spanish/eswiki+100/AI/wiki_75\n",
      "data_spanish/eswiki+100/AI/wiki_59\n",
      "data_spanish/eswiki+100/AI/wiki_63\n",
      "data_spanish/eswiki+100/AI/wiki_57\n",
      "data_spanish/eswiki+100/AI/wiki_98\n",
      "data_spanish/eswiki+100/AI/wiki_97\n",
      "data_spanish/eswiki+100/AI/wiki_04\n",
      "data_spanish/eswiki+100/AI/wiki_05\n",
      "data_spanish/eswiki+100/AI/wiki_03\n",
      "data_spanish/eswiki+100/AI/wiki_37\n",
      "data_spanish/eswiki+100/AI/wiki_26\n",
      "data_spanish/eswiki+100/AI/wiki_06\n",
      "data_spanish/eswiki+100/AI/wiki_89\n",
      "data_spanish/eswiki+100/AI/wiki_53\n",
      "data_spanish/eswiki+100/AI/wiki_41\n",
      "data_spanish/eswiki+100/AI/wiki_99\n",
      "data_spanish/eswiki+100/AI/wiki_33\n",
      "data_spanish/eswiki+100/AI/wiki_80\n",
      "data_spanish/eswiki+100/AI/wiki_28\n",
      "data_spanish/eswiki+100/AI/wiki_02\n",
      "data_spanish/eswiki+100/AI/wiki_27\n",
      "data_spanish/eswiki+100/AI/wiki_12\n",
      "data_spanish/eswiki+100/AI/wiki_92\n",
      "data_spanish/eswiki+100/AI/wiki_30\n",
      "data_spanish/eswiki+100/AI/wiki_88\n",
      "data_spanish/eswiki+100/AI/wiki_10\n",
      "data_spanish/eswiki+100/AI/wiki_83\n",
      "data_spanish/eswiki+100/AI/wiki_09\n",
      "data_spanish/eswiki+100/AI/wiki_32\n",
      "data_spanish/eswiki+100/AI/wiki_73\n",
      "data_spanish/eswiki+100/AI/wiki_70\n",
      "data_spanish/eswiki+100/AI/wiki_11\n",
      "data_spanish/eswiki+100/AI/wiki_71\n",
      "data_spanish/eswiki+100/AI/wiki_16\n",
      "data_spanish/eswiki+100/AI/wiki_36\n",
      "data_spanish/eswiki+100/AI/wiki_15\n",
      "data_spanish/eswiki+100/AI/wiki_96\n",
      "data_spanish/eswiki+100/AI/wiki_82\n",
      "data_spanish/eswiki+100/AI/wiki_68\n",
      "data_spanish/eswiki+100/AI/wiki_91\n",
      "data_spanish/eswiki+100/AI/wiki_25\n",
      "data_spanish/eswiki+100/AI/wiki_19\n",
      "data_spanish/eswiki+100/AI/wiki_49\n",
      "data_spanish/eswiki+100/AI/wiki_69\n",
      "data_spanish/eswiki+100/AI/wiki_07\n",
      "data_spanish/eswiki+100/AI/wiki_94\n",
      "data_spanish/eswiki+100/AI/wiki_17\n",
      "data_spanish/eswiki+100/AB\n",
      "data_spanish/eswiki+100/AB/wiki_13\n",
      "data_spanish/eswiki+100/AB/wiki_40\n",
      "data_spanish/eswiki+100/AB/wiki_39\n",
      "data_spanish/eswiki+100/AB/wiki_31\n",
      "data_spanish/eswiki+100/AB/wiki_65\n",
      "data_spanish/eswiki+100/AB/wiki_95\n",
      "data_spanish/eswiki+100/AB/wiki_85\n",
      "data_spanish/eswiki+100/AB/wiki_52\n",
      "data_spanish/eswiki+100/AB/wiki_18\n",
      "data_spanish/eswiki+100/AB/wiki_84\n",
      "data_spanish/eswiki+100/AB/wiki_87\n",
      "data_spanish/eswiki+100/AB/wiki_42\n",
      "data_spanish/eswiki+100/AB/wiki_61\n",
      "data_spanish/eswiki+100/AB/wiki_47\n",
      "data_spanish/eswiki+100/AB/wiki_50\n",
      "data_spanish/eswiki+100/AB/wiki_45\n",
      "data_spanish/eswiki+100/AB/wiki_01\n",
      "data_spanish/eswiki+100/AB/wiki_35\n",
      "data_spanish/eswiki+100/AB/wiki_58\n",
      "data_spanish/eswiki+100/AB/wiki_00\n",
      "data_spanish/eswiki+100/AB/wiki_55\n",
      "data_spanish/eswiki+100/AB/wiki_08\n",
      "data_spanish/eswiki+100/AB/wiki_54\n",
      "data_spanish/eswiki+100/AB/wiki_46\n",
      "data_spanish/eswiki+100/AB/wiki_24\n",
      "data_spanish/eswiki+100/AB/wiki_38\n",
      "data_spanish/eswiki+100/AB/wiki_81\n",
      "data_spanish/eswiki+100/AB/wiki_74\n",
      "data_spanish/eswiki+100/AB/wiki_22\n",
      "data_spanish/eswiki+100/AB/wiki_56\n",
      "data_spanish/eswiki+100/AB/wiki_14\n",
      "data_spanish/eswiki+100/AB/wiki_64\n",
      "data_spanish/eswiki+100/AB/wiki_51\n",
      "data_spanish/eswiki+100/AB/wiki_76\n",
      "data_spanish/eswiki+100/AB/wiki_60\n",
      "data_spanish/eswiki+100/AB/wiki_29\n",
      "data_spanish/eswiki+100/AB/wiki_90\n",
      "data_spanish/eswiki+100/AB/wiki_66\n",
      "data_spanish/eswiki+100/AB/wiki_79\n",
      "data_spanish/eswiki+100/AB/wiki_23\n",
      "data_spanish/eswiki+100/AB/wiki_62\n",
      "data_spanish/eswiki+100/AB/wiki_78\n",
      "data_spanish/eswiki+100/AB/wiki_77\n",
      "data_spanish/eswiki+100/AB/wiki_44\n",
      "data_spanish/eswiki+100/AB/wiki_20\n",
      "data_spanish/eswiki+100/AB/wiki_93\n",
      "data_spanish/eswiki+100/AB/wiki_21\n",
      "data_spanish/eswiki+100/AB/wiki_34\n",
      "data_spanish/eswiki+100/AB/wiki_86\n",
      "data_spanish/eswiki+100/AB/wiki_43\n",
      "data_spanish/eswiki+100/AB/wiki_72\n",
      "data_spanish/eswiki+100/AB/wiki_48\n",
      "data_spanish/eswiki+100/AB/wiki_67\n",
      "data_spanish/eswiki+100/AB/wiki_75\n",
      "data_spanish/eswiki+100/AB/wiki_59\n",
      "data_spanish/eswiki+100/AB/wiki_63\n",
      "data_spanish/eswiki+100/AB/wiki_57\n",
      "data_spanish/eswiki+100/AB/wiki_98\n",
      "data_spanish/eswiki+100/AB/wiki_97\n",
      "data_spanish/eswiki+100/AB/wiki_04\n",
      "data_spanish/eswiki+100/AB/wiki_05\n",
      "data_spanish/eswiki+100/AB/wiki_03\n",
      "data_spanish/eswiki+100/AB/wiki_37\n",
      "data_spanish/eswiki+100/AB/wiki_26\n",
      "data_spanish/eswiki+100/AB/wiki_06\n",
      "data_spanish/eswiki+100/AB/wiki_89\n",
      "data_spanish/eswiki+100/AB/wiki_53\n",
      "data_spanish/eswiki+100/AB/wiki_41\n",
      "data_spanish/eswiki+100/AB/wiki_99\n",
      "data_spanish/eswiki+100/AB/wiki_33\n",
      "data_spanish/eswiki+100/AB/wiki_80\n",
      "data_spanish/eswiki+100/AB/wiki_28\n",
      "data_spanish/eswiki+100/AB/wiki_02\n",
      "data_spanish/eswiki+100/AB/wiki_27\n",
      "data_spanish/eswiki+100/AB/wiki_12\n",
      "data_spanish/eswiki+100/AB/wiki_92\n",
      "data_spanish/eswiki+100/AB/wiki_30\n",
      "data_spanish/eswiki+100/AB/wiki_88\n",
      "data_spanish/eswiki+100/AB/wiki_10\n",
      "data_spanish/eswiki+100/AB/wiki_83\n",
      "data_spanish/eswiki+100/AB/wiki_09\n",
      "data_spanish/eswiki+100/AB/wiki_32\n",
      "data_spanish/eswiki+100/AB/wiki_73\n",
      "data_spanish/eswiki+100/AB/wiki_70\n",
      "data_spanish/eswiki+100/AB/wiki_11\n",
      "data_spanish/eswiki+100/AB/wiki_71\n",
      "data_spanish/eswiki+100/AB/wiki_16\n",
      "data_spanish/eswiki+100/AB/wiki_36\n",
      "data_spanish/eswiki+100/AB/wiki_15\n",
      "data_spanish/eswiki+100/AB/wiki_96\n",
      "data_spanish/eswiki+100/AB/wiki_82\n",
      "data_spanish/eswiki+100/AB/wiki_68\n",
      "data_spanish/eswiki+100/AB/wiki_91\n",
      "data_spanish/eswiki+100/AB/wiki_25\n",
      "data_spanish/eswiki+100/AB/wiki_19\n",
      "data_spanish/eswiki+100/AB/wiki_49\n",
      "data_spanish/eswiki+100/AB/wiki_69\n",
      "data_spanish/eswiki+100/AB/wiki_07\n",
      "data_spanish/eswiki+100/AB/wiki_94\n",
      "data_spanish/eswiki+100/AB/wiki_17\n",
      "data_spanish/eswiki+100/AJ\n",
      "data_spanish/eswiki+100/AJ/wiki_13\n",
      "data_spanish/eswiki+100/AJ/wiki_40\n",
      "data_spanish/eswiki+100/AJ/wiki_39\n",
      "data_spanish/eswiki+100/AJ/wiki_31\n",
      "data_spanish/eswiki+100/AJ/wiki_65\n",
      "data_spanish/eswiki+100/AJ/wiki_95\n",
      "data_spanish/eswiki+100/AJ/wiki_85\n",
      "data_spanish/eswiki+100/AJ/wiki_52\n",
      "data_spanish/eswiki+100/AJ/wiki_18\n",
      "data_spanish/eswiki+100/AJ/wiki_84\n",
      "data_spanish/eswiki+100/AJ/wiki_87\n",
      "data_spanish/eswiki+100/AJ/wiki_42\n",
      "data_spanish/eswiki+100/AJ/wiki_61\n",
      "data_spanish/eswiki+100/AJ/wiki_47\n",
      "data_spanish/eswiki+100/AJ/wiki_50\n",
      "data_spanish/eswiki+100/AJ/wiki_45\n",
      "data_spanish/eswiki+100/AJ/wiki_01\n",
      "data_spanish/eswiki+100/AJ/wiki_35\n",
      "data_spanish/eswiki+100/AJ/wiki_58\n",
      "data_spanish/eswiki+100/AJ/wiki_00\n",
      "data_spanish/eswiki+100/AJ/wiki_55\n",
      "data_spanish/eswiki+100/AJ/wiki_08\n",
      "data_spanish/eswiki+100/AJ/wiki_54\n",
      "data_spanish/eswiki+100/AJ/wiki_46\n",
      "data_spanish/eswiki+100/AJ/wiki_24\n",
      "data_spanish/eswiki+100/AJ/wiki_38\n",
      "data_spanish/eswiki+100/AJ/wiki_81\n",
      "data_spanish/eswiki+100/AJ/wiki_74\n",
      "data_spanish/eswiki+100/AJ/wiki_22\n",
      "data_spanish/eswiki+100/AJ/wiki_56\n",
      "data_spanish/eswiki+100/AJ/wiki_14\n",
      "data_spanish/eswiki+100/AJ/wiki_64\n",
      "data_spanish/eswiki+100/AJ/wiki_51\n",
      "data_spanish/eswiki+100/AJ/wiki_76\n",
      "data_spanish/eswiki+100/AJ/wiki_60\n",
      "data_spanish/eswiki+100/AJ/wiki_29\n",
      "data_spanish/eswiki+100/AJ/wiki_90\n",
      "data_spanish/eswiki+100/AJ/wiki_66\n",
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      "data_spanish/eswiki+100/AJ/wiki_78\n",
      "data_spanish/eswiki+100/AJ/wiki_77\n",
      "data_spanish/eswiki+100/AJ/wiki_44\n",
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      "data_spanish/eswiki+100/AJ/wiki_93\n",
      "data_spanish/eswiki+100/AJ/wiki_21\n",
      "data_spanish/eswiki+100/AJ/wiki_34\n",
      "data_spanish/eswiki+100/AJ/wiki_86\n",
      "data_spanish/eswiki+100/AJ/wiki_43\n",
      "data_spanish/eswiki+100/AJ/wiki_72\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_spanish/eswiki+100/AJ/wiki_48\n",
      "data_spanish/eswiki+100/AJ/wiki_67\n",
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      "data_spanish/eswiki+100/AF/wiki_95\n",
      "data_spanish/eswiki+100/AF/wiki_85\n",
      "data_spanish/eswiki+100/AF/wiki_52\n",
      "data_spanish/eswiki+100/AF/wiki_18\n",
      "data_spanish/eswiki+100/AF/wiki_84\n",
      "data_spanish/eswiki+100/AF/wiki_87\n",
      "data_spanish/eswiki+100/AF/wiki_42\n",
      "data_spanish/eswiki+100/AF/wiki_61\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_spanish/eswiki+100/AF/wiki_47\n",
      "data_spanish/eswiki+100/AF/wiki_50\n",
      "data_spanish/eswiki+100/AF/wiki_45\n",
      "data_spanish/eswiki+100/AF/wiki_01\n",
      "data_spanish/eswiki+100/AF/wiki_35\n",
      "data_spanish/eswiki+100/AF/wiki_58\n",
      "data_spanish/eswiki+100/AF/wiki_00\n",
      "data_spanish/eswiki+100/AF/wiki_55\n",
      "data_spanish/eswiki+100/AF/wiki_08\n",
      "data_spanish/eswiki+100/AF/wiki_54\n",
      "data_spanish/eswiki+100/AF/wiki_46\n",
      "data_spanish/eswiki+100/AF/wiki_24\n",
      "data_spanish/eswiki+100/AF/wiki_38\n",
      "data_spanish/eswiki+100/AF/wiki_81\n",
      "data_spanish/eswiki+100/AF/wiki_74\n",
      "data_spanish/eswiki+100/AF/wiki_22\n",
      "data_spanish/eswiki+100/AF/wiki_56\n",
      "data_spanish/eswiki+100/AF/wiki_14\n",
      "data_spanish/eswiki+100/AF/wiki_64\n",
      "data_spanish/eswiki+100/AF/wiki_51\n",
      "data_spanish/eswiki+100/AF/wiki_76\n",
      "data_spanish/eswiki+100/AF/wiki_60\n",
      "data_spanish/eswiki+100/AF/wiki_29\n",
      "data_spanish/eswiki+100/AF/wiki_90\n",
      "data_spanish/eswiki+100/AF/wiki_66\n",
      "data_spanish/eswiki+100/AF/wiki_79\n",
      "data_spanish/eswiki+100/AF/wiki_23\n",
      "data_spanish/eswiki+100/AF/wiki_62\n",
      "data_spanish/eswiki+100/AF/wiki_78\n",
      "data_spanish/eswiki+100/AF/wiki_77\n",
      "data_spanish/eswiki+100/AF/wiki_44\n",
      "data_spanish/eswiki+100/AF/wiki_20\n",
      "data_spanish/eswiki+100/AF/wiki_93\n",
      "data_spanish/eswiki+100/AF/wiki_21\n",
      "data_spanish/eswiki+100/AF/wiki_34\n",
      "data_spanish/eswiki+100/AF/wiki_86\n",
      "data_spanish/eswiki+100/AF/wiki_43\n",
      "data_spanish/eswiki+100/AF/wiki_72\n",
      "data_spanish/eswiki+100/AF/wiki_48\n",
      "data_spanish/eswiki+100/AF/wiki_67\n",
      "data_spanish/eswiki+100/AF/wiki_75\n",
      "data_spanish/eswiki+100/AF/wiki_59\n",
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      "data_spanish/eswiki+100/AF/wiki_57\n",
      "data_spanish/eswiki+100/AF/wiki_98\n",
      "data_spanish/eswiki+100/AF/wiki_97\n",
      "data_spanish/eswiki+100/AF/wiki_04\n",
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      "data_spanish/eswiki+100/AF/wiki_03\n",
      "data_spanish/eswiki+100/AF/wiki_37\n",
      "data_spanish/eswiki+100/AF/wiki_26\n",
      "data_spanish/eswiki+100/AF/wiki_06\n",
      "data_spanish/eswiki+100/AF/wiki_89\n",
      "data_spanish/eswiki+100/AF/wiki_53\n",
      "data_spanish/eswiki+100/AF/wiki_41\n",
      "data_spanish/eswiki+100/AF/wiki_99\n",
      "data_spanish/eswiki+100/AF/wiki_33\n",
      "data_spanish/eswiki+100/AF/wiki_80\n",
      "data_spanish/eswiki+100/AF/wiki_28\n",
      "data_spanish/eswiki+100/AF/wiki_02\n",
      "data_spanish/eswiki+100/AF/wiki_27\n",
      "data_spanish/eswiki+100/AF/wiki_12\n",
      "data_spanish/eswiki+100/AF/wiki_92\n",
      "data_spanish/eswiki+100/AF/wiki_30\n",
      "data_spanish/eswiki+100/AF/wiki_88\n",
      "data_spanish/eswiki+100/AF/wiki_10\n",
      "data_spanish/eswiki+100/AF/wiki_83\n",
      "data_spanish/eswiki+100/AF/wiki_09\n",
      "data_spanish/eswiki+100/AF/wiki_32\n",
      "data_spanish/eswiki+100/AF/wiki_73\n",
      "data_spanish/eswiki+100/AF/wiki_70\n",
      "data_spanish/eswiki+100/AF/wiki_11\n",
      "data_spanish/eswiki+100/AF/wiki_71\n",
      "data_spanish/eswiki+100/AF/wiki_16\n",
      "data_spanish/eswiki+100/AF/wiki_36\n",
      "data_spanish/eswiki+100/AF/wiki_15\n",
      "data_spanish/eswiki+100/AF/wiki_96\n",
      "data_spanish/eswiki+100/AF/wiki_82\n",
      "data_spanish/eswiki+100/AF/wiki_68\n",
      "data_spanish/eswiki+100/AF/wiki_91\n",
      "data_spanish/eswiki+100/AF/wiki_25\n",
      "data_spanish/eswiki+100/AF/wiki_19\n",
      "data_spanish/eswiki+100/AF/wiki_49\n",
      "data_spanish/eswiki+100/AF/wiki_69\n",
      "data_spanish/eswiki+100/AF/wiki_07\n",
      "data_spanish/eswiki+100/AF/wiki_94\n",
      "data_spanish/eswiki+100/AF/wiki_17\n",
      "data_spanish/eswiki+100/AH\n",
      "data_spanish/eswiki+100/AH/wiki_13\n",
      "data_spanish/eswiki+100/AH/wiki_40\n",
      "data_spanish/eswiki+100/AH/wiki_39\n",
      "data_spanish/eswiki+100/AH/wiki_31\n",
      "data_spanish/eswiki+100/AH/wiki_65\n",
      "data_spanish/eswiki+100/AH/wiki_95\n",
      "data_spanish/eswiki+100/AH/wiki_85\n",
      "data_spanish/eswiki+100/AH/wiki_52\n",
      "data_spanish/eswiki+100/AH/wiki_18\n",
      "data_spanish/eswiki+100/AH/wiki_84\n",
      "data_spanish/eswiki+100/AH/wiki_87\n",
      "data_spanish/eswiki+100/AH/wiki_42\n",
      "data_spanish/eswiki+100/AH/wiki_61\n",
      "data_spanish/eswiki+100/AH/wiki_47\n",
      "data_spanish/eswiki+100/AH/wiki_50\n",
      "data_spanish/eswiki+100/AH/wiki_45\n",
      "data_spanish/eswiki+100/AH/wiki_01\n",
      "data_spanish/eswiki+100/AH/wiki_35\n",
      "data_spanish/eswiki+100/AH/wiki_58\n",
      "data_spanish/eswiki+100/AH/wiki_00\n",
      "data_spanish/eswiki+100/AH/wiki_55\n",
      "data_spanish/eswiki+100/AH/wiki_08\n",
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      "data_spanish/eswiki+100/AH/wiki_46\n",
      "data_spanish/eswiki+100/AH/wiki_24\n",
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      "data_spanish/eswiki+100/AH/wiki_81\n",
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      "data_spanish/eswiki+100/AH/wiki_23\n",
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      "data_spanish/eswiki+100/AH/wiki_20\n",
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      "data_spanish/eswiki+100/AH/wiki_94\n",
      "data_spanish/eswiki+100/AH/wiki_17\n",
      "data_spanish/eswiki+100/AD\n",
      "data_spanish/eswiki+100/AD/wiki_13\n",
      "data_spanish/eswiki+100/AD/wiki_40\n",
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      "data_spanish/eswiki+100/AD/wiki_42\n",
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      "data_spanish/eswiki+100/AD/wiki_47\n",
      "data_spanish/eswiki+100/AD/wiki_50\n",
      "data_spanish/eswiki+100/AD/wiki_45\n",
      "data_spanish/eswiki+100/AD/wiki_01\n",
      "data_spanish/eswiki+100/AD/wiki_35\n",
      "data_spanish/eswiki+100/AD/wiki_58\n",
      "data_spanish/eswiki+100/AD/wiki_00\n",
      "data_spanish/eswiki+100/AD/wiki_55\n",
      "data_spanish/eswiki+100/AD/wiki_08\n",
      "data_spanish/eswiki+100/AD/wiki_54\n",
      "data_spanish/eswiki+100/AD/wiki_46\n",
      "data_spanish/eswiki+100/AD/wiki_24\n",
      "data_spanish/eswiki+100/AD/wiki_38\n",
      "data_spanish/eswiki+100/AD/wiki_81\n",
      "data_spanish/eswiki+100/AD/wiki_74\n",
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      "data_spanish/eswiki+100/AD/wiki_14\n",
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      "data_spanish/eswiki+100/AD/wiki_51\n",
      "data_spanish/eswiki+100/AD/wiki_76\n",
      "data_spanish/eswiki+100/AD/wiki_60\n",
      "data_spanish/eswiki+100/AD/wiki_29\n",
      "data_spanish/eswiki+100/AD/wiki_90\n",
      "data_spanish/eswiki+100/AD/wiki_66\n",
      "data_spanish/eswiki+100/AD/wiki_79\n",
      "data_spanish/eswiki+100/AD/wiki_23\n",
      "data_spanish/eswiki+100/AD/wiki_62\n",
      "data_spanish/eswiki+100/AD/wiki_78\n",
      "data_spanish/eswiki+100/AD/wiki_77\n",
      "data_spanish/eswiki+100/AD/wiki_44\n",
      "data_spanish/eswiki+100/AD/wiki_20\n",
      "data_spanish/eswiki+100/AD/wiki_93\n",
      "data_spanish/eswiki+100/AD/wiki_21\n",
      "data_spanish/eswiki+100/AD/wiki_34\n",
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      "data_spanish/eswiki+100/AD/wiki_43\n",
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      "data_spanish/eswiki+100/AD/wiki_48\n",
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      "data_spanish/eswiki+100/AD/wiki_97\n",
      "data_spanish/eswiki+100/AD/wiki_04\n",
      "data_spanish/eswiki+100/AD/wiki_05\n",
      "data_spanish/eswiki+100/AD/wiki_03\n",
      "data_spanish/eswiki+100/AD/wiki_37\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_spanish/eswiki+100/AD/wiki_26\n",
      "data_spanish/eswiki+100/AD/wiki_06\n",
      "data_spanish/eswiki+100/AD/wiki_89\n",
      "data_spanish/eswiki+100/AD/wiki_53\n",
      "data_spanish/eswiki+100/AD/wiki_41\n",
      "data_spanish/eswiki+100/AD/wiki_99\n",
      "data_spanish/eswiki+100/AD/wiki_33\n",
      "data_spanish/eswiki+100/AD/wiki_80\n",
      "data_spanish/eswiki+100/AD/wiki_28\n",
      "data_spanish/eswiki+100/AD/wiki_02\n",
      "data_spanish/eswiki+100/AD/wiki_27\n",
      "data_spanish/eswiki+100/AD/wiki_12\n",
      "data_spanish/eswiki+100/AD/wiki_92\n",
      "data_spanish/eswiki+100/AD/wiki_30\n",
      "data_spanish/eswiki+100/AD/wiki_88\n",
      "data_spanish/eswiki+100/AD/wiki_10\n",
      "data_spanish/eswiki+100/AD/wiki_83\n",
      "data_spanish/eswiki+100/AD/wiki_09\n",
      "data_spanish/eswiki+100/AD/wiki_32\n",
      "data_spanish/eswiki+100/AD/wiki_73\n",
      "data_spanish/eswiki+100/AD/wiki_70\n",
      "data_spanish/eswiki+100/AD/wiki_11\n",
      "data_spanish/eswiki+100/AD/wiki_71\n",
      "data_spanish/eswiki+100/AD/wiki_16\n",
      "data_spanish/eswiki+100/AD/wiki_36\n",
      "data_spanish/eswiki+100/AD/wiki_15\n",
      "data_spanish/eswiki+100/AD/wiki_96\n",
      "data_spanish/eswiki+100/AD/wiki_82\n",
      "data_spanish/eswiki+100/AD/wiki_68\n",
      "data_spanish/eswiki+100/AD/wiki_91\n",
      "data_spanish/eswiki+100/AD/wiki_25\n",
      "data_spanish/eswiki+100/AD/wiki_19\n",
      "data_spanish/eswiki+100/AD/wiki_49\n",
      "data_spanish/eswiki+100/AD/wiki_69\n",
      "data_spanish/eswiki+100/AD/wiki_07\n",
      "data_spanish/eswiki+100/AD/wiki_94\n",
      "data_spanish/eswiki+100/AD/wiki_17\n"
     ]
    }
   ],
   "source": [
    "jsons = []\n",
    "for direc in DATA_PATH/'eswiki+100'.iterdir():\n",
    "    print (direc)\n",
    "    for file in direc.iterdir():\n",
    "        print(file)\n",
    "        for line in open(file, 'r', encoding=\"utf-8\"):\n",
    "            jsons.append(json.loads(line))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['url', 'text', 'id', 'title'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jsons[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ID = 'id'\n",
    "TITLE = 'title'\n",
    "TEXT = 'text'\n",
    "URL = 'url'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = np.random.permutation(len(jsons))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "limit_sets = int(len(jsons)*0.99)\n",
    "\n",
    "trn_set = [jsons[i] for i in idx[:limit_sets]]\n",
    "val_set = [jsons[i] for i in idx[limit_sets:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_title(texts):\n",
    "    return texts.split('\\n\\n', 1)[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "trn_ids = [exmp[ID] for exmp in trn_set]\n",
    "trn_titles = [exmp[TITLE] for exmp in trn_set]\n",
    "trn_texts = [remove_title(exmp[TEXT]) for exmp in trn_set]\n",
    "trn_urls = [exmp[URL] for exmp in trn_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_ids = [exmp[ID] for exmp in val_set]\n",
    "val_titles = [exmp[TITLE] for exmp in val_set]\n",
    "val_texts = [remove_title(exmp[TEXT]) for exmp in val_set]\n",
    "val_urls = [exmp[URL] for exmp in val_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = ['id','title','text','url']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trn = pd.DataFrame({'id':trn_ids, 'title':trn_titles, 'text':trn_texts, 'url':trn_urls}, columns=col_names)\n",
    "df_val = pd.DataFrame({'id':val_ids, 'title':val_titles, 'text':val_texts, 'url':val_urls}, columns=col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6066379</td>\n",
       "      <td>Noche de Brujas (banda)</td>\n",
       "      <td>Noche de Brujas es una agrupación musical chil...</td>\n",
       "      <td>https://es.wikipedia.org/wiki?curid=6066379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>959915</td>\n",
       "      <td>Ley de Gutenberg-Richter</td>\n",
       "      <td>La ley de Gutenberg-Richter (1958) es una fórm...</td>\n",
       "      <td>https://es.wikipedia.org/wiki?curid=959915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5698339</td>\n",
       "      <td>La leyenda de Spyro: Un nuevo comienzo</td>\n",
       "      <td>La leyenda de Spyro: Un nuevo comienzo (en ing...</td>\n",
       "      <td>https://es.wikipedia.org/wiki?curid=5698339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>343047</td>\n",
       "      <td>Estadio Luis II</td>\n",
       "      <td>El Estadio Luis II (en francés: \"Stade Louis I...</td>\n",
       "      <td>https://es.wikipedia.org/wiki?curid=343047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>366898</td>\n",
       "      <td>Blanquillo (Uruguay)</td>\n",
       "      <td>Blanquillo es una localidad uruguaya del depar...</td>\n",
       "      <td>https://es.wikipedia.org/wiki?curid=366898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                   title  \\\n",
       "0  6066379                 Noche de Brujas (banda)   \n",
       "1   959915                Ley de Gutenberg-Richter   \n",
       "2  5698339  La leyenda de Spyro: Un nuevo comienzo   \n",
       "3   343047                         Estadio Luis II   \n",
       "4   366898                    Blanquillo (Uruguay)   \n",
       "\n",
       "                                                text  \\\n",
       "0  Noche de Brujas es una agrupación musical chil...   \n",
       "1  La ley de Gutenberg-Richter (1958) es una fórm...   \n",
       "2  La leyenda de Spyro: Un nuevo comienzo (en ing...   \n",
       "3  El Estadio Luis II (en francés: \"Stade Louis I...   \n",
       "4  Blanquillo es una localidad uruguaya del depar...   \n",
       "\n",
       "                                           url  \n",
       "0  https://es.wikipedia.org/wiki?curid=6066379  \n",
       "1   https://es.wikipedia.org/wiki?curid=959915  \n",
       "2  https://es.wikipedia.org/wiki?curid=5698339  \n",
       "3   https://es.wikipedia.org/wiki?curid=343047  \n",
       "4   https://es.wikipedia.org/wiki?curid=366898  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trn.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(389072, 97268)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# len(trn_set), len(val_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_trn.to_csv(COMPL_PATH/'train+15.csv', header=False, index=False)\n",
    "# df_val.to_csv(COMPL_PATH/'val+15.csv', header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = ['title','text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trn = pd.DataFrame({'title':trn_titles, 'text':trn_texts}, columns=col_names)\n",
    "df_val = pd.DataFrame({'title':val_titles, 'text':val_texts}, columns=col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trn.to_csv(LM_PATH/'train+100.csv', header=False, index=False)\n",
    "df_val.to_csv(LM_PATH/'val+100.csv', header=False, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wiki model tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunksize=4000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "re1 = re.compile(r'  +')\n",
    "\n",
    "def fixup(x):\n",
    "    x = x.replace('#39;', \"'\").replace('amp;', '&').replace('#146;', \"'\").replace(\n",
    "        'nbsp;', ' ').replace('#36;', '$').replace('\\\\n', \"\\n\").replace('quot;', \"'\").replace(\n",
    "        '<br />', \"\\n\").replace('\\\\\"', '\"').replace('<unk>','u_n').replace(' @.@ ','.').replace(\n",
    "        ' @-@ ','-').replace('\\\\', ' \\\\ ').replace('\\xa0', ' ')\n",
    "    return re1.sub(' ', html.unescape(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_texts(df, n_lbls=0):\n",
    "    texts = f'\\n{BOS} {FLD} 1 ' + df[n_lbls].astype(str)\n",
    "    for i in range(n_lbls+1, len(df.columns)):\n",
    "        texts += f' {FLD} {i-n_lbls} ' + df[i].astype(str)\n",
    "    texts = texts.apply(fixup).values.astype(str)\n",
    "    \n",
    "    tok = Tokenizer(lang='es').proc_all_mp(partition_by_cores(texts), lang='es')\n",
    "    return tok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all(df, n_lbls):\n",
    "    tok, labels = [], []\n",
    "    l = 0\n",
    "    for i, r in enumerate(df):\n",
    "        print(i)\n",
    "        l += 1\n",
    "        tok_ = get_texts(r, n_lbls)\n",
    "        tok += tok_;\n",
    "        #if i >= 35:\n",
    "            #return tok\n",
    "    return tok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trn = pd.read_csv(LM_PATH/'train+100.csv', header=None, chunksize=chunksize)\n",
    "df_val = pd.read_csv(LM_PATH/'val+100.csv', header=None, chunksize=chunksize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "(LM_PATH/'tmp').mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n"
     ]
    }
   ],
   "source": [
    "l = 0\n",
    "for i,_ in enumerate(df_trn):\n",
    "    l+=1\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "tok_val = get_all(df_val, 0)\n",
    "np.save(LM_PATH/'tmp'/'tok_val+100.npy', tok_val)\n",
    "tok_trn = get_all(df_trn, 0)\n",
    "np.save(LM_PATH/'tmp'/'tok_trn+100.npy', tok_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-c1bdfb25c915>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmini\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmini_chunks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'tmp/tok_trn'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'+60.npy'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLM_PATH\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmini\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(file, arr, allow_pickle, fix_imports)\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m         format.write_array(fid, arr, allow_pickle=allow_pickle,\n\u001b[0;32m--> 511\u001b[0;31m                            pickle_kwargs=pickle_kwargs)\n\u001b[0m\u001b[1;32m    512\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mown_fid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/numpy/lib/format.py\u001b[0m in \u001b[0;36mwrite_array\u001b[0;34m(fp, array, version, allow_pickle, pickle_kwargs)\u001b[0m\n\u001b[1;32m    581\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpickle_kwargs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m             \u001b[0mpickle_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 583\u001b[0;31m         \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    584\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_contiguous\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_contiguous\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from math import ceil\n",
    "\n",
    "def chunks(l, n):\n",
    "    \"\"\"Yield successive n-sized chunks from l.\"\"\"\n",
    "    for i in range(0, len(l), n):\n",
    "        yield l[i:i + n]\n",
    "        \n",
    "mini_chunks = chunks(tok_trn, ceil(len(tok_trn)/4))\n",
    "for i, mini in enumerate(mini_chunks):\n",
    "    name = 'tmp/tok_trn'+str(i)+'+60.npy'\n",
    "    np.save(LM_PATH/name, mini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tok_trn0 = np.load(LM_PATH/'tmp'/'tok_trn0+100.npy')\n",
    "#tok_trn1 = np.load(LM_PATH/'tmp'/'tok_trn1+100.npy')\n",
    "#tok_trn2 = np.load(LM_PATH/'tmp'/'tok_trn2+100.npy')\n",
    "#tok_trn3 = np.load(LM_PATH/'tmp'/'tok_trn3+100.npy')\n",
    "\n",
    "#tok_trn = np.concatenate((tok_trn0, tok_trn1, tok_trn2, tok_trn3))\n",
    "tok_trn = np.load(LM_PATH/'tmp'/'tok_trn+100.npy')\n",
    "tok_val = np.load(LM_PATH/'tmp'/'tok_val+100.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We remove 3/8 of the total number of articles so that we keep ~120M tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tok_trn  = tok_trn[:(len(tok_trn)*5)//8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120137944\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for text in tok_trn:\n",
    "    for char in text:\n",
    "        i+=1\n",
    "print (i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(LM_PATH/'tmp'/'tok_trn120+100.npy', tok_trn)\n",
    "tok_trn = np.load(LM_PATH/'tmp'/'tok_trn120+100.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('de', 7858311),\n",
       " (',', 6244351),\n",
       " ('la', 4485493),\n",
       " ('.', 3683917),\n",
       " ('el', 3478795),\n",
       " ('en', 3442403),\n",
       " ('y', 2816731),\n",
       " ('que', 2097825),\n",
       " ('a', 2075629),\n",
       " ('\"', 1786621),\n",
       " ('los', 1761963),\n",
       " ('del', 1452380),\n",
       " ('se', 1338509),\n",
       " ('\\n\\n', 1151707),\n",
       " ('por', 1083461),\n",
       " ('las', 1074070),\n",
       " ('un', 1010824),\n",
       " ('con', 991543),\n",
       " ('una', 875506),\n",
       " ('su', 841859),\n",
       " ('para', 677392),\n",
       " (')', 659613),\n",
       " ('(', 655545),\n",
       " ('al', 640314),\n",
       " ('como', 630415)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = Counter(p for o in tok_trn for p in o)\n",
    "freq.most_common(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_vocab = 60000\n",
    "min_freq = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60002"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "itos2 = [o for o,c in freq.most_common(max_vocab) if c>min_freq]\n",
    "itos2.insert(0, '_pad_')\n",
    "itos2.insert(0, '_unk_')\n",
    "stoi2 = collections.defaultdict(lambda:0, {v:k for k,v in enumerate(itos2)})\n",
    "len(itos2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "trn_lm = np.array([[stoi2[o] for o in p] for p in tok_trn])\n",
    "val_lm = np.array([[stoi2[o] for o in p] for p in tok_val])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(LM_PATH/'tmp'/'trn_ids+100.npy', trn_lm)\n",
    "np.save(LM_PATH/'tmp'/'val_ids+100.npy', val_lm)\n",
    "pickle.dump(itos2, open(LM_PATH/'tmp'/'itos+100.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wiki+100 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "wd=1e-7\n",
    "bptt=70\n",
    "bs=32\n",
    "em_sz,nh,nl = 400,1150,3\n",
    "#opt_fn = partial(optim.Adam, betas=(0.8, 0.99))\n",
    "opt_fn = partial(optim.SGD, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "trn_lm = np.load(LM_PATH/'tmp'/'trn_ids+100.npy')\n",
    "val_lm = np.load(LM_PATH/'tmp'/'val_ids+100.npy')\n",
    "trn_lm = np.concatenate(trn_lm)\n",
    "val_lm = np.concatenate(val_lm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "itos = pickle.load(open(LM_PATH/'tmp'/'itos+100.pkl', 'rb'))\n",
    "vs = len(itos)\n",
    "\n",
    "trn_dl = LanguageModelLoader(trn_lm, bs, bptt)\n",
    "val_dl = LanguageModelLoader(val_lm, bs, bptt)\n",
    "md = LanguageModelData(MODEL_PATH, 1, vs, trn_dl, val_dl, bs=bs, bptt=bptt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "drops = np.array([0.25, 0.1, 0.2, 0.02, 0.15])*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner= md.get_model(opt_fn, em_sz, nh, nl, \n",
    "    dropouti=drops[0], dropout=drops[1], wdrop=drops[2], dropoute=drops[3], dropouth=drops[4])\n",
    "\n",
    "learner.metrics = [accuracy]\n",
    "learner.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2bb5eb57e674101a8433fde6e2b5f07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0%|          | 13/53632 [00:08<9:20:46,  1.59it/s, loss=10.2]    \n",
      "  0%|          | 14/53632 [00:08<8:48:36,  1.69it/s, loss=10.2]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-4:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/paperspace/anaconda3/envs/fastai/lib/python3.6/threading.py\", line 916, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/home/paperspace/anaconda3/envs/fastai/lib/python3.6/site-packages/tqdm/_tqdm.py\", line 144, in run\n",
      "    for instance in self.tqdm_cls._instances:\n",
      "  File \"/home/paperspace/anaconda3/envs/fastai/lib/python3.6/_weakrefset.py\", line 60, in __iter__\n",
      "    for itemref in self.data:\n",
      "RuntimeError: Set changed size during iteration\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1%|▏         | 800/53632 [07:24<8:09:43,  1.80it/s, loss=15.2]\n"
     ]
    }
   ],
   "source": [
    "learner.lr_find2(start_lr=1,end_lr=10, num_it=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf1b2ee400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.sched.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd64336b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.sched.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "Jupyter.beep();"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "Jupyter.beep();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=2\n",
    "lrs = lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebbd8a2f4df3437fab1aa1c176e9f05f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=10), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                           \n",
      "    0      4.243111   4.193037   0.257854  \n",
      "    1      4.01907    3.969443   0.272689                           \n",
      "    2      3.908034   3.872785   0.278616                           \n",
      "    3      3.844105   3.821897   0.282531                           \n",
      "    4      3.803215   3.780296   0.285598                           \n",
      "    5      3.7653     3.746013   0.28916                            \n",
      "    6      3.780881   3.710472   0.293218                           \n",
      "    7      3.728705   3.676514   0.296869                           \n",
      "    8      3.685521   3.627796   0.303029                           \n",
      "    9      3.641547   3.587603   0.308747                           \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([3.5876]), 0.3087470562991961]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#learner.fit(lrs/2, 1, wds=wd, use_clr=(32,2), cycle_len=1)\n",
    "learner.fit(lr, 1, cycle_len=10, use_clr_beta=(10,10,0.95,0.85))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.save('first_run_spanish')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data_spanish/eswiki+100/models'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = learner.models_path; path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Encoder and Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.save_encoder('lm1_enc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.save('lm1_weights')"
   ]
  }
 ],
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