{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelowanie języka – laboratoria\n",
    "### 10 kwietnia 2024\n",
    "# 6. Biblioteki do statystycznych modeli językowych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KENLM\n",
    "\n",
    "W praktyce korzysta się z gotowych bibliotek do statystycznych modeli językowych. Najbardziej popularną biblioteką jest KENLM ( https://kheafield.com/papers/avenue/kenlm.pdf ). Repozytorium znajduje się https://github.com/kpu/kenlm a dokumentacja https://kheafield.com/code/kenlm/\n",
    "\n",
    "Na komputerach wydziałowych nie powinno być problemu ze skompilowaniem biblioteki.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instalacja\n",
    "\n",
    "(Zob. też dokumentacja)\n",
    "\n",
    "    sudo apt-get install build-essential libboost-all-dev cmake zlib1g-dev libbz2-dev liblzma-dev\n",
    "    wget -O - https://kheafield.com/code/kenlm.tar.gz | tar xz\n",
    "    mkdir kenlm/build\n",
    "    cd kenlm/build\n",
    "    cmake ..\n",
    "    make -j2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Najprostszy scenariusz użycia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "KENLM_BUILD_PATH='/home/pawel/kenlm/build'  # ścieżka, w której jest zainstalowany KenLM (zob. dokumentacja - link powyżej)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-04-10 12:13:27--  https://wolnelektury.pl/media/book/txt/lalka-tom-pierwszy.txt\n",
      "Resolving wolnelektury.pl (wolnelektury.pl)... 51.83.143.148, 2001:41d0:602:3294::\n",
      "Connecting to wolnelektury.pl (wolnelektury.pl)|51.83.143.148|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 860304 (840K) [text/plain]\n",
      "Saving to: ‘lalka-tom-pierwszy.txt.1’\n",
      "\n",
      "lalka-tom-pierwszy. 100%[===================>] 840.14K  3.59MB/s    in 0.2s    \n",
      "\n",
      "2024-04-10 12:13:27 (3.59 MB/s) - ‘lalka-tom-pierwszy.txt.1’ saved [860304/860304]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://wolnelektury.pl/media/book/txt/lalka-tom-pierwszy.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-04-10 12:13:30--  https://wolnelektury.pl/media/book/txt/lalka-tom-drugi.txt\n",
      "Resolving wolnelektury.pl (wolnelektury.pl)... 51.83.143.148, 2001:41d0:602:3294::\n",
      "Connecting to wolnelektury.pl (wolnelektury.pl)|51.83.143.148|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 949497 (927K) [text/plain]\n",
      "Saving to: ‘lalka-tom-drugi.txt.1’\n",
      "\n",
      "lalka-tom-drugi.txt 100%[===================>] 927.24K  3.39MB/s    in 0.3s    \n",
      "\n",
      "2024-04-10 12:13:30 (3.39 MB/s) - ‘lalka-tom-drugi.txt.1’ saved [949497/949497]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://wolnelektury.pl/media/book/txt/lalka-tom-drugi.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### budowa modelu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 1/5 Counting and sorting n-grams ===\n",
      "Reading /home/pawel/moj-2024/lab/lalka-tom-pierwszy.txt\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************************************************************************\n",
      "Unigram tokens 122871 types 33265\n",
      "=== 2/5 Calculating and sorting adjusted counts ===\n",
      "Chain sizes: 1:399180 2:2261987584 3:4241227008 4:6785963520\n",
      "Statistics:\n",
      "1 33265 D1=0.737356 D2=1.15675 D3+=1.59585\n",
      "2 93948 D1=0.891914 D2=1.20314 D3+=1.44945\n",
      "3 115490 D1=0.964904 D2=1.40636 D3+=1.66751\n",
      "4 116433 D1=0.986444 D2=1.50367 D3+=1.9023\n",
      "Memory estimate for binary LM:\n",
      "type      kB\n",
      "probing 7800 assuming -p 1.5\n",
      "probing 9157 assuming -r models -p 1.5\n",
      "trie    3902 without quantization\n",
      "trie    2378 assuming -q 8 -b 8 quantization \n",
      "trie    3649 assuming -a 22 array pointer compression\n",
      "trie    2125 assuming -a 22 -q 8 -b 8 array pointer compression and quantization\n",
      "=== 3/5 Calculating and sorting initial probabilities ===\n",
      "Chain sizes: 1:399180 2:1503168 3:2309800 4:2794392\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "####################################################################################################\n",
      "=== 4/5 Calculating and writing order-interpolated probabilities ===\n",
      "Chain sizes: 1:399180 2:1503168 3:2309800 4:2794392\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "####################################################################################################\n",
      "=== 5/5 Writing ARPA model ===\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "****************************************************************************************************\n",
      "Name:lmplz\tVmPeak:13142592 kB\tVmRSS:7564 kB\tRSSMax:2623832 kB\tuser:0.28374\tsys:1.02734\tCPU:1.3111\treal:1.25256\n"
     ]
    }
   ],
   "source": [
    "!$KENLM_BUILD_PATH/bin/lmplz -o 4 < lalka-tom-pierwszy.txt > lalka_tom_pierwszy_lm.arpa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plik arpa\n",
    "\n",
    "Powyższa komenda tworzy model językowy z wygładzaniem i zapisuje go do pliku tekstowego arpa. Parametr -o 4 odpowiada za maksymalną ilość n-gramów w modelu: 4-gramy.\n",
    "\n",
    "Plik arpa zawiera w sobie prawdopodobieństwa dla poszczególnych n-gramów. W zasadzie są to logarytmy prawdopodbieństw o podstawie 10.\n",
    "\n",
    "Podejrzyjmy plik arpa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\data\\\n",
      "ngram 1=33265\n",
      "ngram 2=93948\n",
      "ngram 3=115490\n",
      "ngram 4=116433\n",
      "\n",
      "\\1-grams:\n",
      "-5.0133595\t<unk>\t0\n",
      "0\t<s>\t-0.99603957\n",
      "-1.4302719\t</s>\t0\n",
      "-4.7287908\tBolesław\t-0.049677044\n",
      "-4.9033437\tPrus\t-0.049677044\n",
      "-4.9033437\tLalka\t-0.049677044\n",
      "-4.9033437\tISBN\t-0.049677044\n",
      "-4.9033437\t978-83-288-2673-1\t-0.049677044\n",
      "-4.9033437\tTom\t-0.049677044\n",
      "-3.0029354\tI\t-0.17544968\n",
      "-4.9033437\tI.\t-0.049677044\n",
      "-3.5526814\tJak\t-0.1410632\n",
      "-3.8170912\twygląda\t-0.16308141\n",
      "-4.608305\tfirma\t-0.049677044\n",
      "-4.33789\tJ.\t-0.3295009\n",
      "-3.9192266\tMincel\t-0.12910372\n",
      "-1.624716\ti\t-0.20128249\n",
      "-4.1086636\tS.\t-0.098223634\n",
      "-2.6843808\tWokulski\t-0.19202113\n",
      "-2.8196363\tprzez\t-0.15214005\n",
      "-4.9033437\tszkło\t-0.049677044\n",
      "-4.9033437\tbutelek?\t-0.049677044\n",
      "-2.848008\tW\t-0.19964235\n"
     ]
    }
   ],
   "source": [
    "!head -n 30 lalka_tom_pierwszy_lm.arpa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linijka to kolejno: prawdopodobieństwo (log10), n-gram, waga back-off (log10).\n",
    "\n",
    "Aby spradzić prawdopodobieństwo sekwencji (a także PPL modelu) należy użyć komendy query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_str=!(head -n 17 lalka-tom-drugi.txt  | tail -n 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_str = test_str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sytuacja polityczna jest tak niepewna, że wcale by mnie nie zdziwiło, gdyby około grudnia wybuchła wojna.'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sytuacja polityczna jest tak niepewna, że wcale by mnie nie zdziwiło, gdyby około grudnia wybuchła wojna.'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sytuacja=0 1 -6.009399\tpolityczna=21766 1 -4.9033437\tjest=123 1 -2.6640298\ttak=231 2 -1.7683144\tniepewna,=0 1 -5.1248584\tże=122 1 -2.1651394\twcale=5123 1 -4.167491\tby=1523 1 -3.55168\tmnie=2555 2 -1.6694618\tnie=127 2 -1.4439836\tzdziwiło,=0 1 -5.2158937\tgdyby=814 1 -3.2300434\tokoło=1462 1 -3.7384818\tgrudnia=0 1 -5.123236\twybuchła=0 1 -5.0133595\twojna.=1285 1 -4.9033437\t</s>=2 2 -0.8501559\tTotal: -61.54222 OOV: 5\n",
      "Perplexity including OOVs:\t4169.948113875898\n",
      "Perplexity excluding OOVs:\t834.2371454470355\n",
      "OOVs:\t5\n",
      "Tokens:\t17\n"
     ]
    }
   ],
   "source": [
    "!echo $test_str |  $KENLM_BUILD_PATH/bin/query lalka_tom_pierwszy_lm.arpa 2> /dev/null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zgodnie z dokumentacją polecenia query, format wyjściowy to dla każdego słowa:\n",
    "    \n",
    "word=vocab_id ngram_length log10(p(word|context))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A co jeśli trochę zmienimy początek zdania?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "test2_str = \"Lubię placki i wcale by mnie nie zdziwiło, gdyby około grudnia wybuchła wojna.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lubię=17813 1 -5.899383\tplacki=0 1 -5.0630364\ti=16 1 -1.624716\twcale=5123 2 -3.2397003\tby=1523 1 -3.6538217\tmnie=2555 2 -1.6694618\tnie=127 2 -1.4439836\tzdziwiło,=0 1 -5.2158937\tgdyby=814 1 -3.2300434\tokoło=1462 1 -3.7384818\tgrudnia=0 1 -5.123236\twybuchła=0 1 -5.0133595\twojna.=1285 1 -4.9033437\t</s>=2 2 -0.8501559\tTotal: -50.668617 OOV: 4\n",
      "Perplexity including OOVs:\t4160.896818387522\n",
      "Perplexity excluding OOVs:\t1060.0079770155185\n",
      "OOVs:\t4\n",
      "Tokens:\t14\n"
     ]
    }
   ],
   "source": [
    "!echo $test2_str |  $KENLM_BUILD_PATH/bin/query lalka_tom_pierwszy_lm.arpa 2> /dev/null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trochę bardziej zaawansowane użycie "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pierwsza rzecz, która rzuca się w oczy: tokeny zawierają znaki interpunkcyjne. Użyjemy zatem popularnego tokenizera i detokenizera moses z https://github.com/moses-smt/mosesdecoder\n",
    "        \n",
    "https://github.com/moses-smt/mosesdecoder/tree/master/scripts/tokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tokenizacja i lowercasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "TOKENIZER_SCRIPTS='/home/pawel/mosesdecoder/scripts/tokenizer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sytuacja polityczna jest tak niepewna, że wcale by mnie nie zdziwiło, gdyby około grudnia wybuchła wojna.\n"
     ]
    }
   ],
   "source": [
    "!echo $test_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenizer Version 1.1\n",
      "Language: en\n",
      "Number of threads: 1\n",
      "Sytuacja polityczna jest tak niepewna , że wcale by mnie nie zdziwiło , gdyby około grudnia wybuchła wojna .\n"
     ]
    }
   ],
   "source": [
    "!echo $test_str | $TOKENIZER_SCRIPTS/tokenizer.perl --language pl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "W łatwy sposób można odzyskać tekst źródłowy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detokenizer Version $Revision: 4134 $\n",
      "Language: en\n",
      "Tokenizer Version 1.1\n",
      "Language: en\n",
      "Number of threads: 1\n",
      "Sytuacja polityczna jest tak niepewna, że wcale by mnie nie zdziwiło, gdyby około grudnia wybuchła wojna.\n"
     ]
    }
   ],
   "source": [
    "!echo $test_str | $TOKENIZER_SCRIPTS/tokenizer.perl --language pl | $TOKENIZER_SCRIPTS/detokenizer.perl --language pl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "W naszym przykładzie stworzymy model językowy lowercase. Można osobno wytrenować też truecaser (osobny model do przywracania wielkości liter), jeżeli jest taka potrzeba."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenizer Version 1.1\n",
      "Language: en\n",
      "Number of threads: 1\n",
      "sytuacja polityczna jest tak niepewna , że wcale by mnie nie zdziwiło , gdyby około grudnia wybuchła wojna .\n"
     ]
    }
   ],
   "source": [
    "!echo $test_str | $TOKENIZER_SCRIPTS/tokenizer.perl --language pl | $TOKENIZER_SCRIPTS/lowercase.perl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenizer Version 1.1\n",
      "Language: en\n",
      "Number of threads: 1\n"
     ]
    }
   ],
   "source": [
    "!cat  lalka-tom-pierwszy.txt | $TOKENIZER_SCRIPTS/tokenizer.perl --language pl | $TOKENIZER_SCRIPTS/lowercase.perl > lalka-tom-pierwszy-tokenized-lowercased.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenizer Version 1.1\n",
      "Language: en\n",
      "Number of threads: 1\n"
     ]
    }
   ],
   "source": [
    "!cat  lalka-tom-drugi.txt | $TOKENIZER_SCRIPTS/tokenizer.perl --language pl | $TOKENIZER_SCRIPTS/lowercase.perl > lalka-tom-drugi-tokenized-lowercased.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 1/5 Counting and sorting n-grams ===\n",
      "Reading /home/pawel/moj-2024/lab/lalka-tom-pierwszy-tokenized-lowercased.txt\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "****************************************************************************************************\n",
      "Unigram tokens 149285 types 22230\n",
      "=== 2/5 Calculating and sorting adjusted counts ===\n",
      "Chain sizes: 1:266760 2:2262010112 3:4241268992 4:6786030592\n",
      "Statistics:\n",
      "1 8857/22230 D1=0.664486 D2=1.14301 D3+=1.57055\n",
      "2 14632/86142 D1=0.838336 D2=1.2415 D3+=1.40935\n",
      "3 8505/128074 D1=0.931027 D2=1.29971 D3+=1.54806\n",
      "4 3174/138744 D1=0.967887 D2=1.35058 D3+=1.70692\n",
      "Memory estimate for binary LM:\n",
      "type     kB\n",
      "probing 822 assuming -p 1.5\n",
      "probing 993 assuming -r models -p 1.5\n",
      "trie    480 without quantization\n",
      "trie    343 assuming -q 8 -b 8 quantization \n",
      "trie    459 assuming -a 22 array pointer compression\n",
      "trie    322 assuming -a 22 -q 8 -b 8 array pointer compression and quantization\n",
      "=== 3/5 Calculating and sorting initial probabilities ===\n",
      "Chain sizes: 1:106284 2:234112 3:170100 4:76176\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "**##################################################################################################\n",
      "=== 4/5 Calculating and writing order-interpolated probabilities ===\n",
      "Chain sizes: 1:106284 2:234112 3:170100 4:76176\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "####################################################################################################\n",
      "=== 5/5 Writing ARPA model ===\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "****************************************************************************************************\n",
      "Name:lmplz\tVmPeak:13142612 kB\tVmRSS:7392 kB\tRSSMax:2624428 kB\tuser:0.229863\tsys:0.579255\tCPU:0.809192\treal:0.791505\n"
     ]
    }
   ],
   "source": [
    "!$KENLM_BUILD_PATH/bin/lmplz -o 4 --prune 1 1 1 1  < lalka-tom-pierwszy-tokenized-lowercased.txt > lalka_tom_pierwszy_lm.arpa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_str=!(head -n 17 lalka-tom-drugi-tokenized-lowercased.txt  | tail -n 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_str=test_str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sytuacja polityczna jest tak niepewna , że wcale by mnie nie zdziwiło , gdyby około grudnia wybuchła wojna .'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model binarny\n",
    "\n",
    "Konwertując model do postaci binarnej, inferencja będzie szybsza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading lalka_tom_pierwszy_lm.arpa\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "****************************************************************************************************\n",
      "SUCCESS\n"
     ]
    }
   ],
   "source": [
    "!$KENLM_BUILD_PATH/bin/build_binary   lalka_tom_pierwszy_lm.arpa lalka_tom_pierwszy_lm.binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This binary file contains probing hash tables.\n",
      "sytuacja=0 1 -5.568051\tpolityczna=0 1 -4.4812803\tjest=91 1 -2.6271343\ttak=175 2 -1.7584295\tniepewna=0 1 -4.603079\t,=22 1 -1.2027187\tże=90 2 -1.2062931\twcale=375 1 -4.0545278\tby=995 1 -3.5268068\tmnie=1491 2 -1.6614945\tnie=94 2 -1.4855772\tzdziwiło=0 1 -4.708499\t,=22 1 -1.2027187\tgdyby=555 2 -2.4179027\tokoło=957 1 -3.7740536\tgrudnia=0 1 -4.605748\twybuchła=0 1 -4.4812803\twojna=849 1 -4.213117\t.=42 1 -1.3757544\t</s>=2 2 -0.46293145\tTotal: -59.417397 OOV: 6\n",
      "Perplexity including OOVs:\t935.1253434773644\n",
      "Perplexity excluding OOVs:\t162.9687064350829\n",
      "OOVs:\t6\n",
      "Tokens:\t20\n",
      "Name:query\tVmPeak:8864 kB\tVmRSS:4504 kB\tRSSMax:5328 kB\tuser:0.002388\tsys:0\tCPU:0.0024207\treal:0.000614597\n"
     ]
    }
   ],
   "source": [
    "!echo $test_str |  $KENLM_BUILD_PATH/bin/query lalka_tom_pierwszy_lm.binary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sprawdzanie dokumentacji\n",
    "\n",
    "Najłatwiej sprawdzić wywołując bezpośrednio komendę"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Builds unpruned language models with modified Kneser-Ney smoothing.\n",
      "\n",
      "Please cite:\n",
      "@inproceedings{Heafield-estimate,\n",
      "  author = {Kenneth Heafield and Ivan Pouzyrevsky and Jonathan H. Clark and Philipp Koehn},\n",
      "  title = {Scalable Modified {Kneser-Ney} Language Model Estimation},\n",
      "  year = {2013},\n",
      "  month = {8},\n",
      "  booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics},\n",
      "  address = {Sofia, Bulgaria},\n",
      "  url = {http://kheafield.com/professional/edinburgh/estimate\\_paper.pdf},\n",
      "}\n",
      "\n",
      "Provide the corpus on stdin.  The ARPA file will be written to stdout.  Order of\n",
      "the model (-o) is the only mandatory option.  As this is an on-disk program,\n",
      "setting the temporary file location (-T) and sorting memory (-S) is recommended.\n",
      "\n",
      "Memory sizes are specified like GNU sort: a number followed by a unit character.\n",
      "Valid units are % for percentage of memory (supported platforms only) and (in\n",
      "increasing powers of 1024): b, K, M, G, T, P, E, Z, Y.  Default is K (*1024).\n",
      "This machine has 16611971072 bytes of memory.\n",
      "\n",
      "Language model building options:\n",
      "  -h [ --help ]                         Show this help message\n",
      "  -o [ --order ] arg                    Order of the model\n",
      "  --interpolate_unigrams [=arg(=1)] (=1)\n",
      "                                        Interpolate the unigrams (default) as \n",
      "                                        opposed to giving lots of mass to <unk>\n",
      "                                        like SRI.  If you want SRI's behavior \n",
      "                                        with a large <unk> and the old lmplz \n",
      "                                        default, use --interpolate_unigrams 0.\n",
      "  --skip_symbols                        Treat <s>, </s>, and <unk> as \n",
      "                                        whitespace instead of throwing an \n",
      "                                        exception\n",
      "  -T [ --temp_prefix ] arg (=/tmp/)     Temporary file prefix\n",
      "  -S [ --memory ] arg (=80%)            Sorting memory\n",
      "  --minimum_block arg (=8K)             Minimum block size to allow\n",
      "  --sort_block arg (=64M)               Size of IO operations for sort \n",
      "                                        (determines arity)\n",
      "  --block_count arg (=2)                Block count (per order)\n",
      "  --vocab_estimate arg (=1000000)       Assume this vocabulary size for \n",
      "                                        purposes of calculating memory in step \n",
      "                                        1 (corpus count) and pre-sizing the \n",
      "                                        hash table\n",
      "  --vocab_pad arg (=0)                  If the vocabulary is smaller than this \n",
      "                                        value, pad with <unk> to reach this \n",
      "                                        size. Requires --interpolate_unigrams\n",
      "  --verbose_header                      Add a verbose header to the ARPA file \n",
      "                                        that includes information such as token\n",
      "                                        count, smoothing type, etc.\n",
      "  --text arg                            Read text from a file instead of stdin\n",
      "  --arpa arg                            Write ARPA to a file instead of stdout\n",
      "  --intermediate arg                    Write ngrams to intermediate files.  \n",
      "                                        Turns off ARPA output (which can be \n",
      "                                        reactivated by --arpa file).  Forces \n",
      "                                        --renumber on.\n",
      "  --renumber                            Renumber the vocabulary identifiers so \n",
      "                                        that they are monotone with the hash of\n",
      "                                        each string.  This is consistent with \n",
      "                                        the ordering used by the trie data \n",
      "                                        structure.\n",
      "  --collapse_values                     Collapse probability and backoff into a\n",
      "                                        single value, q that yields the same \n",
      "                                        sentence-level probabilities.  See \n",
      "                                        http://kheafield.com/professional/edinb\n",
      "                                        urgh/rest_paper.pdf for more details, \n",
      "                                        including a proof.\n",
      "  --prune arg                           Prune n-grams with count less than or \n",
      "                                        equal to the given threshold.  Specify \n",
      "                                        one value for each order i.e. 0 0 1 to \n",
      "                                        prune singleton trigrams and above.  \n",
      "                                        The sequence of values must be \n",
      "                                        non-decreasing and the last value \n",
      "                                        applies to any remaining orders. \n",
      "                                        Default is to not prune, which is \n",
      "                                        equivalent to --prune 0.\n",
      "  --limit_vocab_file arg                Read allowed vocabulary separated by \n",
      "                                        whitespace. N-grams that contain \n",
      "                                        vocabulary items not in this list will \n",
      "                                        be pruned. Can be combined with --prune\n",
      "                                        arg\n",
      "  --discount_fallback [=arg(=0.5 1 1.5)]\n",
      "                                        The closed-form estimate for Kneser-Ney\n",
      "                                        discounts does not work without \n",
      "                                        singletons or doubletons.  It can also \n",
      "                                        fail if these values are out of range. \n",
      "                                        This option falls back to \n",
      "                                        user-specified discounts when the \n",
      "                                        closed-form estimate fails.  Note that \n",
      "                                        this option is generally a bad idea: \n",
      "                                        you should deduplicate your corpus \n",
      "                                        instead.  However, class-based models \n",
      "                                        need custom discounts because they lack\n",
      "                                        singleton unigrams.  Provide up to \n",
      "                                        three discounts (for adjusted counts 1,\n",
      "                                        2, and 3+), which will be applied to \n",
      "                                        all orders where the closed-form \n",
      "                                        estimates fail.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!$KENLM_BUILD_PATH/bin/lmplz "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### wrapper pythonowy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting https://github.com/kpu/kenlm/archive/master.zip\n",
      "  Downloading https://github.com/kpu/kenlm/archive/master.zip\n",
      "\u001b[2K     \u001b[32m-\u001b[0m \u001b[32m553.6 kB\u001b[0m \u001b[31m851.1 kB/s\u001b[0m \u001b[33m0:00:00\u001b[0m\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hBuilding wheels for collected packages: kenlm\n",
      "  Building wheel for kenlm (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for kenlm: filename=kenlm-0.2.0-cp310-cp310-linux_x86_64.whl size=3184348 sha256=c9da9a754aa07ffa26f8983ced2910a547d665006e39fd053d365b802b4135e9\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-e8zp2xqd/wheels/a5/73/ee/670fbd0cee8f6f0b21d10987cb042291e662e26e1a07026462\n",
      "Successfully built kenlm\n",
      "Installing collected packages: kenlm\n",
      "Successfully installed kenlm-0.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip install https://github.com/kpu/kenlm/archive/master.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-59.417396545410156\n"
     ]
    }
   ],
   "source": [
    "import kenlm\n",
    "model = kenlm.Model('lalka_tom_pierwszy_lm.binary')\n",
    "print(model.score(test_str, bos = True, eos = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-5.568050861358643, 1, True)\n",
      "(-4.481280326843262, 1, True)\n",
      "(-2.627134323120117, 1, False)\n",
      "(-1.7584295272827148, 2, False)\n",
      "(-4.603078842163086, 1, True)\n",
      "(-1.202718734741211, 1, False)\n",
      "(-1.2062931060791016, 2, False)\n",
      "(-4.054527759552002, 1, False)\n",
      "(-3.5268068313598633, 1, False)\n",
      "(-1.661494493484497, 2, False)\n",
      "(-1.4855772256851196, 2, False)\n",
      "(-4.708498954772949, 1, True)\n",
      "(-1.202718734741211, 1, False)\n",
      "(-2.417902708053589, 2, False)\n",
      "(-3.7740535736083984, 1, False)\n",
      "(-4.605748176574707, 1, True)\n",
      "(-4.481280326843262, 1, True)\n",
      "(-4.2131171226501465, 1, False)\n",
      "(-1.3757543563842773, 1, False)\n",
      "(-0.46293145418167114, 2, False)\n"
     ]
    }
   ],
   "source": [
    "for i in model.full_scores(test_str):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadanie \n",
    "\n",
    "Stworzyć model językowy za pomocą gotowej biblioteki (KenLM lub inna)\n",
    "\n",
    "Rozwiązanie proszę umieścić na https://gonito.csi.wmi.amu.edu.pl/challenge/challenging-america-word-gap-prediction\n",
    "\n",
    "Warunki zaliczenia:\n",
    "- wynik widoczny na platformie zarówno dla dev i dla test\n",
    "- wynik dla dev i test lepszy (niższy) niż 1024.00 (liczone przy pomocy geval)\n",
    "- deadline: **24 kwietnia 2024**\n",
    "- commitując rozwiązanie proszę również umieścić rozwiązanie w pliku /run.py  (czyli na szczycie katalogu). Można przekonwertować jupyter do pliku python przez File → Download as → Python. Rozwiązanie nie musi być w pythonie, może być w innym języku.\n",
    "- zadania wykonujemy samodzielnie\n",
    "- w nazwie commita podaj nr indeksu\n",
    "- w tagach podaj kenlm!\n",
    "- uwaga na specjalne znaki \\\\n w pliku 'in.tsv' oraz pierwsze kolumny pliku in.tsv (które należy usunąć)\n",
    "\n",
    "\n",
    "Punktacja:\n",
    "- podstawa: 40 punktów\n",
    "- dodatkowo 50 (czyli 40 + 50 = 90) punktów z najlepszy wynik\n",
    "- dodatkowo 20 (czyli 40 + 20 = 60) punktów za znalezienie się w pierwszej połowie, ale poza najlepszym wynikiem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "author": "Jakub Pokrywka",
  "email": "kubapok@wmi.amu.edu.pl",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "version": "3.10.12"
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  "subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
  "title": "Ekstrakcja informacji",
  "year": "2021"
 },
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}