{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "improved-register",
   "metadata": {},
   "source": [
    "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<h1> Komputerowe wspomaganie tłumaczenia </h1>\n",
    "<h2> 8. <i>Wykorzystanie tłumaczenia automatycznego we wspomaganiu tłumaczenia</i> [laboratoria]</h2> \n",
    "<h3>Rafał Jaworski (2021)</h3>\n",
    "</div>\n",
    "\n",
    "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hungarian-davis",
   "metadata": {},
   "source": [
    "W dzisiejszych czasach, niezwykle ważną techniką wspomagania tłumaczenia jest użycie tłumaczenia maszynowego. Tekst źródłowy do tłumaczenia jest najpierw tłumaczony całkowicie autommatycznie, a następnie tłumacz ludzki dokonuje korekty wyniku. Technologia tłumaczenia maszynowego jest już na tyle dojrzała, że oferuje bardzo wysoką jakość wyników. Coraz częstsze stają się scenariusze, w których ludzka korekta to niemal całkowicie machinalne (sic!) zatwierdzanie wyników tłumaczenia maszynowego. Na dzisiejszych zajęciach poznamy techniki ewaluacji tłumaczenia maszynowego oraz sprawdzania jego przydatności w procesie wspomagania tłumaczenia ludzkiego."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "posted-commons",
   "metadata": {},
   "source": [
    "Jakość tłumaczenia maszynowego możemy oceniać na dwóch niezależnych płaszczyznach: dokładność i płynność. Płynność jest subiektywnie odbieranym odczuciem, że czytany tekst jest napisany językiem naturalnym i zrozumiałym. Systemy tłumaczenia maszynowego oparte na uczeniu głębokim z wykorzystaniem sieci neuronowych osiągają duży stopień płynności tłumaczenia. Niestety jednak ich dokładność nie zawsze jest równie wysoka."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "referenced-implement",
   "metadata": {},
   "source": [
    "Dokładność tłumaczenia maszynowego jest parametrem, który łatwiej zmierzyć. Wartość takich pomiarów daje obraz tego, jaka jest faktyczna jakość tłumaczenia maszynowego i jaka jest jego potencjalna przydatność we wspomaganiu tłumaczenia."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disturbed-january",
   "metadata": {},
   "source": [
    "Najczęściej stosowaną techniką oceny tłumaczenia maszynowego jest ocena BLEU. Do obliczenia tej oceny potrzebny jest wynik tłumaczenia maszynowego oraz referencyjne tłumaczenie ludzkie wysokiej jakości."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dental-combination",
   "metadata": {},
   "source": [
    "### Ćwiczenie 1: Zaimplementuj program do obliczania oceny BLEU dla korpusu w folderze data. Użyj implementacji BLEU z pakietu nltk. Dodatkowe wymaganie techniczne - napisz program tak, aby nie musiał rozpakwowywać pliku zip z korpusem na dysku."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "moving-clothing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BLEU Score: 3.984587822441638e-156\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "/Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import zipfile\n",
    "import nltk.translate.bleu_score as bleu\n",
    "import string\n",
    "\n",
    "def remove_punctuation(text):\n",
    "    no_punctuation_text = text.translate(str.maketrans('', '', string.punctuation))\n",
    "    sentences = no_punctuation_text.split('\\n')\n",
    "    return [[word.lower() for word in sentence.split()] for sentence in sentences if sentence != '']\n",
    "\n",
    "def extract_zip(input_zip):\n",
    "    input_zip = zipfile.ZipFile(input_zip)\n",
    "    return {name: remove_punctuation(input_zip.read(name).decode('utf-8'))\n",
    "            for name in input_zip.namelist()}\n",
    "\n",
    "def calculate_bleu(data_folder):\n",
    "    if not os.path.exists(data_folder):\n",
    "        output = extract_zip(data_folder + '.zip')  # keys: 'corpus_de_human.txt', 'corpus_de_nmt.txt', 'corpus_en.txt'\n",
    "        human, machine = output['corpus_de_human.txt'], output['corpus_de_nmt.txt']\n",
    "    else:\n",
    "        with open(data_folder + '/corpus_de_human.txt', 'r') as file:\n",
    "            human = [line.strip() for line in file]\n",
    "        with open(data_folder + '/corpus_de_nmt.txt', 'r') as file:\n",
    "            machine = [line.strip() for line in file]\n",
    "    \n",
    "    return bleu.corpus_bleu(human, machine)\n",
    "\n",
    "print(\"BLEU Score:\", calculate_bleu('data/corpus_corrected'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jewish-ethics",
   "metadata": {},
   "source": [
    "### Ćwiczenie 2: Oblicz wartość bleu na różnych fragmentach przykładowego korpusu (np. na pierwszych 100 zdaniach, zdaniach 500-600). Czy w jakimś fragmencie korpusu jakość tłumaczenia znacząco odbiega od średniej?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "lasting-rolling",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "/Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "/Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average BLEU Score: 8.234832237953338e-157\n",
      "Standard Deviation of BLEU Scores: 2.470449671386047e-156\n",
      "Values that are outside of std: []\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def moving_window_indexes(n, window_size, step):\n",
    "    return [(i, i+window_size) for i in range(0, n - window_size + 1, step)]\n",
    "\n",
    "def analyze_bleu(data_folder):\n",
    "    bleu_score = []\n",
    "    output = extract_zip(data_folder + '.zip')\n",
    "    human, machine = output['corpus_de_human.txt'], output['corpus_de_nmt.txt']\n",
    "    idx = moving_window_indexes(len(human), 100, 100)\n",
    "    for i in idx:\n",
    "        bleu_score.append(bleu.corpus_bleu(human[i[0]:i[1]], machine[i[0]:i[1]]))\n",
    "    avg_bleu = np.average(bleu_score)\n",
    "    std_bleu = np.std(bleu_score)\n",
    "    outside_bleu = [i for i, score in enumerate(bleu_score)\n",
    "                    if score < avg_bleu - std_bleu]\n",
    "    return bleu_score, avg_bleu, std_bleu, outside_bleu\n",
    "\n",
    "bleu_score, avg_bleu, std_bleu, outside_bleu = analyze_bleu('data/corpus_corrected')\n",
    "\n",
    "print(\"Average BLEU Score:\", avg_bleu)\n",
    "print(\"Standard Deviation of BLEU Scores:\", std_bleu)\n",
    "print(\"Values that are outside of std:\", outside_bleu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "listed-bikini",
   "metadata": {},
   "source": [
    "Inną metodą oceny jakości tłumaczenia maszynowego jest parametr WER - Word Error Rate. Definiuje się on w następujący sposób:\n",
    "\n",
    "$WER = \\frac{S+D+I}{N}=\\frac{S+D+I}{S+D+C}$\n",
    "\n",
    "gdzie:\n",
    " * S - liczba substytucji (słów)\n",
    " * D - liczba usunięć\n",
    " * I - liczba wstawień\n",
    " * C - liczba poprawnych śłów\n",
    " * N - liczba słów w tłumaczeniu referencyjnym (N=S+D+C)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conscious-cookbook",
   "metadata": {},
   "source": [
    "Miara ta jest zwykle używana w do oceny systemów automatycznego rozpoznawania mowy, jednak w kontekście wspomagania tłumaczenia może być rozumiana jako wielkość nakładu pracy tłumacza nad poprawieniem tłumaczenia maszynowego."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "split-palace",
   "metadata": {},
   "source": [
    "### Ćwiczenie 3: Oblicz wartość WER dla przykładowego korpusu. Skorzystaj z gotowej implementacji WER."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7faff7f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting jiwer\n",
      "  Downloading jiwer-3.0.3-py3-none-any.whl (21 kB)\n",
      "Requirement already satisfied: click<9.0.0,>=8.1.3 in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from jiwer) (8.1.7)\n",
      "Requirement already satisfied: rapidfuzz<4,>=3 in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from jiwer) (3.8.1)\n",
      "Installing collected packages: jiwer\n",
      "Successfully installed jiwer-3.0.3\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install jiwer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "occupied-swing",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jiwer import wer\n",
    "\n",
    "\n",
    "def remove_punctuation(text):\n",
    "    no_punctuation_text = text.translate(str.maketrans('', '', string.punctuation))\n",
    "    return no_punctuation_text\n",
    "\n",
    "def extract_zip(input_zip):\n",
    "    input_zip = zipfile.ZipFile(input_zip)\n",
    "    return {name: remove_punctuation(input_zip.read(name).decode('utf-8'))\n",
    "            for name in input_zip.namelist()}\n",
    "\n",
    "def calculate_wer(data_folder):\n",
    "    output = extract_zip(data_folder + '.zip')\n",
    "    human, machine = output['corpus_de_human.txt'].lower(), output['corpus_de_nmt.txt'].lower()\n",
    "    avg = []\n",
    "    for s_h, s_m in zip(human.split('\\n'), machine.split('\\n')):\n",
    "        if s_h == '':\n",
    "            continue\n",
    "        avg.append(wer(s_h, s_m))\n",
    "    return wer(human, machine), np.average(avg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "b98eb250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.17324274888946956, 0.1735521656930835)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_wer('data/corpus_corrected')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stretch-wound",
   "metadata": {},
   "source": [
    "Poza wymienionymi powyżej, stosować można jeszcze inne miary oparte na porównywaniu tłumaczenia maszynowego z ludzkim. Przypomnijmy sobie jedną, którą stosowaliśmy wcześniej."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abstract-wilderness",
   "metadata": {},
   "source": [
    "### Ćwiczenie 4: Oblicz średnią wartość dystansu Levenshteina pomiędzy zdaniami przetłumaczonymi automatycznie oraz przez człowieka. Użyj implementacji z ćwiczeń nr 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "immediate-element",
   "metadata": {},
   "outputs": [],
   "source": [
    "#UWAGA: poniewaz zadanie mówi o dystansie, nie bede obliczal podobienstwa levenshtein'a.\n",
    "\n",
    "from Levenshtein import distance as levenshtein_distance\n",
    "\n",
    "def calculate_levenshtein(data_folder):\n",
    "    output = extract_zip(data_folder + '.zip')\n",
    "    human, machine = output['corpus_de_human.txt'].lower(), output['corpus_de_nmt.txt'].lower()\n",
    "    avg = []\n",
    "    for s_h, s_m in zip(human.split('\\n'), machine.split('\\n')):\n",
    "        if s_h == '':\n",
    "            continue\n",
    "        avg.append(levenshtein_distance(s_h, s_m))\n",
    "    return np.average(avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e1eea599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.008"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_levenshtein('data/corpus_corrected')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "filled-burton",
   "metadata": {},
   "source": [
    "A teraz sprawdźmy coś jeszcze. W danych przykładowego korpusu znajduje się także angielski tekst źródłowy. Teoretycznie, dobre tłumaczenie niemieckie powinno zawierać jak najwięcej słów z angielskiego źródła. Wykonajmy najstępujący eksperyment:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grateful-recruitment",
   "metadata": {},
   "source": [
    "### Ćwiczenie 5: Dla każdej trójki zdań z korpusu przykładowego wykonaj następujące kroki:\n",
    " * Przetłumacz każde angielskie słowo na niemiecki przy użyciu modułu PyDictionary.\n",
    " * Sprawdź, które z niemieckich tłumaczeń zawiera więcej spośród tych przetłumaczonych słów - automatyczne, czy ludzkie.\n",
    "Następnie wypisz statystyki zbiorcze. Które tłumaczenie zawiera więcej słownikowych tłumaczeń słów ze źródła?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "8ebe0ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting PyDictionary\n",
      "  Downloading PyDictionary-2.0.1-py3-none-any.whl (6.1 kB)\n",
      "Collecting bs4\n",
      "  Downloading bs4-0.0.2-py2.py3-none-any.whl (1.2 kB)\n",
      "Requirement already satisfied: click in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from PyDictionary) (8.1.7)\n",
      "Collecting goslate\n",
      "  Downloading goslate-1.5.4.tar.gz (14 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: requests in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from PyDictionary) (2.31.0)\n",
      "Collecting beautifulsoup4\n",
      "  Downloading beautifulsoup4-4.12.3-py3-none-any.whl (147 kB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.9/147.9 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting futures\n",
      "  Downloading futures-3.0.5.tar.gz (25 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from requests->PyDictionary) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from requests->PyDictionary) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from requests->PyDictionary) (2.2.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from requests->PyDictionary) (2024.2.2)\n",
      "Collecting soupsieve>1.2\n",
      "  Downloading soupsieve-2.5-py3-none-any.whl (36 kB)\n",
      "Installing collected packages: futures, soupsieve, goslate, beautifulsoup4, bs4, PyDictionary\n",
      "\u001b[33m  DEPRECATION: futures is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/8559\u001b[0m\u001b[33m\n",
      "\u001b[0m  Running setup.py install for futures ... \u001b[?25ldone\n",
      "\u001b[?25h\u001b[33m  DEPRECATION: goslate is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/8559\u001b[0m\u001b[33m\n",
      "\u001b[0m  Running setup.py install for goslate ... \u001b[?25ldone\n",
      "\u001b[?25hSuccessfully installed PyDictionary-2.0.1 beautifulsoup4-4.12.3 bs4-0.0.2 futures-3.0.5 goslate-1.5.4 soupsieve-2.5\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install PyDictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "descending-easter",
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     ]
    }
   ],
   "source": [
    "from PyDictionary import PyDictionary\n",
    "import zipfile\n",
    "import string\n",
    "from tqdm import tqdm\n",
    "\n",
    "def remove_punctuation(text):\n",
    "    no_punctuation_text = text.translate(str.maketrans('', '', string.punctuation))\n",
    "    words = no_punctuation_text.split()\n",
    "    return [word.lower() for word in words if word]\n",
    "\n",
    "def extract_zip(input_zip):\n",
    "    input_zip = zipfile.ZipFile(input_zip)\n",
    "    return {name: input_zip.read(name).decode('utf-8')\n",
    "            for name in input_zip.namelist()}\n",
    "\n",
    "def translate_to_german(word_list):\n",
    "    dictionary = PyDictionary()\n",
    "    german_translations = []\n",
    "    for word in tqdm(word_list):\n",
    "        try:\n",
    "            translations = dictionary.translate(word, 'de')\n",
    "            german_translations.extend(translations)\n",
    "        except:\n",
    "            pass\n",
    "    return german_translations\n",
    "\n",
    "def compare_translations(data_folder):\n",
    "    output = extract_zip(data_folder + '.zip')\n",
    "    human_english = remove_punctuation(output['corpus_en.txt'])\n",
    "    human_german = remove_punctuation(output['corpus_de_human.txt'])\n",
    "    automatic_german = translate_to_german(human_english)\n",
    "    unique_human_german = set(human_german)\n",
    "    unique_automatic_german = set(automatic_german)\n",
    "    summary = {\n",
    "        \"Human Translations\": len(unique_human_german),\n",
    "        \"Automatic Translations\": len(unique_automatic_german),\n",
    "        \"Common Translations\": len(unique_human_german.intersection(unique_automatic_german))\n",
    "    }\n",
    "\n",
    "    return summary\n",
    "\n",
    "summary = compare_translations('data/corpus_corrected')\n",
    "\n",
    "print(\"Summary Statistics:\")\n",
    "for key, value in summary.items():\n",
    "    print(f\"{key}: {value}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc57f168",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "author": "Rafał Jaworski",
  "email": "rjawor@amu.edu.pl",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "lang": "pl",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "subtitle": "8. Wykorzystanie tłumaczenia automatycznego we wspomaganiu tłumaczenia",
  "title": "Komputerowe wspomaganie tłumaczenia",
  "year": "2021"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}