en-ner-conll-2003/RNN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-Oe-UjzqiGr"
      },
      "source": [
        "## Uczenie głębokie – przetwarzanie tekstu – laboratoria\n",
        "# 3. RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4rlpU7EIqiGs"
      },
      "source": [
        "RNN - rekurencyjna sieć neuronowa \\\n",
        "NEP - oznaczanie jednostek nazewniczych / części mowy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQJof_SFqiGt"
      },
      "source": [
        "IOB\n",
        "- O - brak jednostki nazewniczej\n",
        "- I - kontynuacja jednostki\n",
        "- B - początek nowej jednostki"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m9n1wZgqqiGt"
      },
      "source": [
        "Typy jednostek\n",
        "- LOC - lokalizacja\n",
        "- PER - osoba\n",
        "- ORG - organizacja"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vR-pMPpVqiGt"
      },
      "source": [
        "Jeśli jest to jednoznaczne to nawet na początku jednostki daje się \"I\" \\\n",
        "\"B\" używa się zazwyczaj przy sytuacjach w których mogło być to niejednoznaczne"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Zadanie 3\n",
        "\n",
        "Sklonuj repozytorium https://git.wmi.amu.edu.pl/kubapok/en-ner-conll-2003\n",
        "\n",
        "Stwórz model *sequence labelling* realizujący zadanie NER, oparty o dowolną rekurencyjną sieć neuronową (możesz wzorować się na przykładzie z zajęć).\n",
        "\n",
        "W plikach dev-0/out.tsv oraz test-A/out.tsv umieść wyniki predykcji dla dev-0/in.tsv i test-A/in.tsv odpowiednio.\n",
        "Do ewaluacji wykorzystaj narzędzie GEval (https://gitlab.com/filipg/geval):\n",
        "\n",
        "    wget https://gonito.net/get/bin/geval\n",
        "    chmod u+x geval\n",
        "    ./geval --help\n",
        "\n",
        "Liczba punktów uzyskanych za zadanie zależy od uzyskanej wartości accuracy na zbiorze `test-A` (wynik zaokrąglony w górę):\n",
        "\n",
        "    points = math.ceil(accuracy * 7.0)\n",
        "\n",
        "⚠️ W systemie Moodle proszę załączyć plik `test-A/out.tsv` oraz link do repozytorium z rozwiązaniem zadania.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_gkW9-KqiGt"
      },
      "source": [
        "### Podejście softmax z embeddingami na przykładzie NER"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 337,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HiaQylI7qiGt",
        "outputId": "a04b79b5-29ea-4493-96c6-a12036ef9e74"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using pip 24.0 from C:\\Users\\Karol\\AppData\\Roaming\\Python\\Python311\\site-packages\\pip (python 3.11)\n",
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Looking in indexes: https://download.pytorch.org/whl/cu118\n",
            "Requirement already satisfied: torch in c:\\users\\karol\\appdata\\roaming\\python\\python311\\site-packages (2.2.0+cu118)\n",
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            "Note: you may need to restart the kernel to use updated packages.\n",
            "Using pip 24.0 from C:\\Users\\Karol\\AppData\\Roaming\\Python\\Python311\\site-packages\\pip (python 3.11)\n",
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: datasets in c:\\users\\karol\\appdata\\roaming\\python\\python311\\site-packages (2.19.1)\n",
            "Requirement already satisfied: filelock in c:\\users\\karol\\appdata\\roaming\\python\\python311\\site-packages (from datasets) (3.13.1)\n",
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            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install -v torch torchtext --index-url https://download.pytorch.org/whl/cu118\n",
        "%pip install -v datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 338,
      "metadata": {
        "id": "ri4H3CAKqiGu"
      },
      "outputs": [],
      "source": [
        "from collections import Counter\n",
        "\n",
        "import torch\n",
        "from torchtext.vocab import vocab\n",
        "from tqdm.notebook import tqdm\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 340,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['rare', 'hendrix', 'song', 'draft', 'sells', 'for', 'almost', '$', '17,000', '.', '</s>', 'london', '1996-08-22', '</s>', 'a', 'rare', 'early', 'handwritten', 'draft', 'of', 'a', 'song', 'by', 'u.s.', 'guitar', 'legend', 'jimi', 'hendrix', 'was', 'sold', 'for', 'almost', '$', '17,000', 'on', 'thursday', 'at', 'an', 'auction', 'of', 'some', 'of', 'the', 'late', 'musician', \"'s\", 'favourite', 'possessions', '.', '</s>', 'a', 'florida', 'restaurant', 'paid', '10,925', 'pounds', '(', '$', '16,935', ')', 'for', 'the', 'draft', 'of', '\"', 'ai', \"n't\", 'no', 'telling', '\"', ',', 'which', 'hendrix', 'penned', 'on', 'a', 'piece', 'of', 'london', 'hotel', 'stationery', 'in', 'late', '1966', '.', '</s>', 'at', 'the', 'end', 'of', 'a', 'january', '1967', 'concert', 'in', 'the', 'english', 'city', 'of', 'nottingham', 'he', 'threw', 'the', 'sheet', 'of', 'paper', 'into', 'the', 'audience', ',', 'where', 'it', 'was', 'retrieved', 'by', 'a', 'fan', '.', '</s>', 'buyers', 'also', 'snapped', 'up', '16', 'other', 'items', 'that', 'were', 'put', 'up', 'for', 'auction', 'by', 'hendrix', \"'s\", 'former', 'girlfriend', 'kathy', 'etchingham', ',', 'who', 'lived', 'with', 'him', 'from', '1966', 'to', '1969', '.', '</s>', 'they', 'included', 'a', 'black', 'lacquer', 'and', 'mother', 'of', 'pearl', 'inlaid', 'box', 'used', 'by', 'hendrix', 'to', 'store', 'his', 'drugs', ',', 'which', 'an', 'anonymous', 'australian', 'purchaser', 'bought', 'for', '5,060', 'pounds', '(', '$', '7,845', ')', '.', '</s>', 'the', 'guitarist', 'died', 'of', 'a', 'drugs', 'overdose', 'in', '1970', 'aged', '27', '.', '</s>']\n",
            "197\n",
            "['O', 'B-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']\n",
            "197\n"
          ]
        }
      ],
      "source": [
        "train_data = pd.read_csv(\"train/train.tsv\", sep=\"\\t\", header=None, names=[\"y\", \"x\"])\n",
        "training_data = []\n",
        "training_labels = []\n",
        "for (labels,data) in train_data.iterrows():\n",
        "    rowtokens = data[\"x\"].split(\" \")\n",
        "    rowlabels = data[\"y\"].split(\" \")\n",
        "    for i in range(len(rowtokens)):\n",
        "        rowtokens[i] = rowtokens[i].lower()\n",
        "    training_data.append(rowtokens)\n",
        "    training_labels.append(rowlabels)\n",
        "\n",
        "print(training_data[1])\n",
        "print(len(training_data[1]))\n",
        "print(training_labels[1])\n",
        "print(len(training_labels[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 341,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['rugby', 'union', '-', 'cuttitta', 'back', 'for', 'italy', 'after', 'a', 'year', '.', '</s>', 'rome', '1996-12-06', '</s>', 'italy', 'recalled', 'marcello', 'cuttitta', '</s>', 'on', 'friday', 'for', 'their', 'friendly', 'against', 'scotland', 'at', 'murrayfield', 'more', 'than', 'a', 'year', 'after', 'the', '30-year-old', 'wing', 'announced', 'he', 'was', 'retiring', 'following', 'differences', 'over', 'selection', '.', '</s>', 'cuttitta', ',', 'who', 'trainer', 'george', 'coste', 'said', 'was', 'certain', 'to', 'play', 'on', 'saturday', 'week', ',', 'was', 'named', 'in', 'a', '21-man', 'squad', 'lacking', 'only', 'two', 'of', 'the', 'team', 'beaten', '54-21', 'by', 'england', 'at', 'twickenham', 'last', 'month', '.', '</s>', 'stefano', 'bordon', 'is', 'out', 'through', 'illness', 'and', 'coste', 'said', 'he', 'had', 'dropped', 'back', 'row', 'corrado', 'covi', ',', 'who', 'had', 'been', 'recalled', 'for', 'the', 'england', 'game', 'after', 'five', 'years', 'out', 'of', 'the', 'national', 'team', '.', '</s>', 'cuttitta', 'announced', 'his', 'retirement', 'after', 'the', '1995', 'world', 'cup', ',', 'where', 'he', 'took', 'issue', 'with', 'being', 'dropped', 'from', 'the', 'italy', 'side', 'that', 'faced', 'england', 'in', 'the', 'pool', 'stages', '.', '</s>', 'coste', 'said', 'he', 'had', 'approached', 'the', 'player', 'two', 'months', 'ago', 'about', 'a', 'comeback', '.', '</s>', '\"', 'he', 'ended', 'the', 'world', 'cup', 'on', 'the', 'wrong', 'note', ',', '\"', 'coste', 'said', '.', '</s>', '\"', 'i', 'thought', 'it', 'would', 'be', 'useful', 'to', 'have', 'him', 'back', 'and', 'he', 'said', 'he', 'would', 'be', 'available', '.', '</s>', 'i', 'think', 'now', 'is', 'the', 'right', 'time', 'for', 'him', 'to', 'return', '.', '\"', '</s>', 'squad', ':', 'javier', 'pertile', ',', 'paolo', 'vaccari', ',', 'marcello', 'cuttitta', ',', 'ivan', 'francescato', ',', 'leandro', 'manteri', ',', 'diego', 'dominguez', ',', 'francesco', 'mazzariol', ',', 'alessandro', 'troncon', ',', 'orazio', 'arancio', ',', 'andrea', 'sgorlon', ',', 'massimo', 'giovanelli', ',', 'carlo', 'checchinato', ',', 'walter', 'cristofoletto', ',', 'franco', 'properzi', 'curti', ',', 'carlo', 'orlandi', ',', 'massimo', 'cuttitta', ',', 'giambatista', 'croci', ',', 'gianluca', 'guidi', ',', 'nicola', 'mazzucato', ',', 'alessandro', 'moscardi', ',', 'andrea', 'castellani', '.', '</s>']\n",
            "281\n"
          ]
        }
      ],
      "source": [
        "test_data = pd.read_csv(\"test-A/in.tsv\", sep=\"\\t\", header=None, names=[\"x\"])\n",
        "testing_data = []\n",
        "for (labels,data) in test_data.iterrows():\n",
        "    rowtokens = data[\"x\"].split(\" \")\n",
        "    for i in range(len(rowtokens)):\n",
        "        rowtokens[i] = rowtokens[i].lower()\n",
        "    testing_data.append(rowtokens)\n",
        "\n",
        "print(testing_data[1])\n",
        "print(len(testing_data[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 342,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['cricket', '-', 'english', 'county', 'championship', 'scores', '.', '</s>', 'london', '1996-08-30', '</s>', 'result', 'and', 'close', 'of', 'play', 'scores', 'in', 'english', 'county', 'championship', 'matches', 'on', 'friday', ':', '</s>', 'leicester', ':', 'leicestershire', 'beat', 'somerset', 'by', 'an', 'innings', 'and', '39', 'runs', '.', '</s>', 'somerset', '83', 'and', '174', '(', 'p.', 'simmons', '4-38', ')', ',', 'leicestershire', '296', '.', '</s>', 'leicestershire', '22', 'points', ',', 'somerset', '4', '.', '</s>', 'chester-le-street', ':', 'glamorgan', '259', 'and', '207', '(', 'a.', 'dale', '69', ',', 'h.', 'morris', '69', ';', 'd.', 'blenkiron', '4-43', ')', ',', 'durham', '114', '(', 's.', 'watkin', '4-28', ')', 'and', '81-3', '.', '</s>', 'tunbridge', 'wells', ':', 'nottinghamshire', '214', '(', 'p.', 'johnson', '84', ';', 'm.', 'mccague', '4-55', ')', ',', 'kent', '108-3', '.', '</s>', 'london', '(', 'the', 'oval', ')', ':', 'warwickshire', '195', ',', 'surrey', '429-7', '(', 'c.', 'lewis', '80', 'not', 'out', ',', 'm.', 'butcher', '70', ',', 'g.', 'kersey', '63', ',', 'j.', 'ratcliffe', '63', ',', 'd.', 'bicknell', '55', ')', '.', '</s>', 'hove', ':', 'sussex', '363', '(', 'w.', 'athey', '111', ',', 'v.', 'drakes', '52', ';', 'i.', 'austin', '4-37', ')', ',', 'lancashire', '197-8', '(', 'w.', 'hegg', '54', ')', '</s>', 'portsmouth', ':', 'middlesex', '199', 'and', '426', '(', 'j.', 'pooley', '111', ',', 'm.', 'ramprakash', '108', ',', 'm.', 'gatting', '83', ')', ',', 'hampshire', '232', 'and', '109-5', '.', '</s>', 'chesterfield', ':', 'worcestershire', '238', 'and', '133-5', ',', 'derbyshire', '471', '(', 'j.', 'adams', '123', ',', \"t.o'gorman\", '109', 'not', 'out', ',', 'k.', 'barnett', '87', ';', 't.', 'moody', '6-82', ')', '</s>', 'bristol', ':', 'gloucestershire', '183', 'and', '185-6', '(', 'j.', 'russell', '56', 'not', 'out', ')', ',', 'northamptonshire', '190', '(', 'k.', 'curran', '52', ';', 'a.', 'smith', '5-68', ')', '.', '</s>']\n"
          ]
        }
      ],
      "source": [
        "validate_data = pd.read_csv(\"dev-0/in.tsv\", sep=\"\\t\", header=None, names=[\"x\"])\n",
        "validation_data = []\n",
        "for (labels,data) in validate_data.iterrows():\n",
        "    rowtokens = data[\"x\"].split(\" \")\n",
        "    for i in range(len(rowtokens)):\n",
        "        rowtokens[i] = rowtokens[i].lower()\n",
        "    validation_data.append(rowtokens)\n",
        "\n",
        "print(validation_data[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 343,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'I-LOC', 'O', 'O', 'B-ORG', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O']\n"
          ]
        }
      ],
      "source": [
        "validate_labels = pd.read_csv(\"dev-0/expected.tsv\", sep=\"\\t\", header=None, names=[\"y\"])\n",
        "validation_labels = []\n",
        "for (labels,data) in validate_labels.iterrows():\n",
        "    rowlabels = data[\"y\"].split(\" \")\n",
        "    validation_labels.append(rowlabels)\n",
        "\n",
        "print(validation_labels[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 344,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'B-ORG': 0, 'O': 1, 'B-MISC': 2, 'B-PER': 3, 'I-PER': 4, 'B-LOC': 5, 'I-ORG': 6, 'I-MISC': 7, 'I-LOC': 8}\n",
            "{0: 'B-ORG', 1: 'O', 2: 'B-MISC', 3: 'B-PER', 4: 'I-PER', 5: 'B-LOC', 6: 'I-ORG', 7: 'I-MISC', 8: 'I-LOC'}\n"
          ]
        }
      ],
      "source": [
        "NER_labels = {}\n",
        "i = 0\n",
        "for labels in training_labels:\n",
        "    for label in labels:\n",
        "        if label not in NER_labels:\n",
        "            NER_labels[label] = i\n",
        "            i += 1\n",
        "\n",
        "NER_labels_reverse = {v: k for k, v in NER_labels.items()}\n",
        "print(NER_labels)\n",
        "print(NER_labels_reverse)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MPgW07rxqiGv"
      },
      "source": [
        "Poniżej funkcja, która tworzy słownik (https://pytorch.org/text/stable/vocab.html).\n",
        "\n",
        "Parametr `special` określa symbole specjalne:\n",
        "* `<unk>` – nieznany token\n",
        "* `<pad>` – wypełnienie\n",
        "* `<bos>` – początek zdania\n",
        "* `<eos>` – koniec zdania"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 346,
      "metadata": {
        "id": "ndRQ9YhNqiGv"
      },
      "outputs": [],
      "source": [
        "def build_vocab(dataset):\n",
        "    counter = Counter()\n",
        "    for document in dataset:\n",
        "        counter.update(document)\n",
        "    return vocab(counter, specials=[\"<unk>\", \"<pad>\", \"<bos>\", \"<eos>\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 347,
      "metadata": {
        "id": "0JpASxiDqiGw"
      },
      "outputs": [],
      "source": [
        "v = build_vocab(training_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 348,
      "metadata": {
        "id": "0QNEgTyHqiGw"
      },
      "outputs": [],
      "source": [
        "itos = v.get_itos()  # mapowanie indeksów na tokeny"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 349,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ct_mtUBWqiGw",
        "outputId": "26bd46e4-03e1-4f74-cd8b-0b330516d179"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['<unk>', '<pad>', '<bos>', '<eos>', 'eu', 'rejects', 'german', 'call', 'to', 'boycott', 'british', 'lamb', '.', '</s>', 'peter', 'blackburn', 'brussels', '1996-08-22', 'the', 'european', 'commission', 'said', 'on', 'thursday', 'it', 'disagreed', 'with', 'advice', 'consumers', 'shun', 'until', 'scientists', 'determine', 'whether', 'mad', 'cow', 'disease', 'can', 'be', 'transmitted', 'sheep', 'germany', \"'s\", 'representative', 'union', 'veterinary', 'committee', 'werner', 'zwingmann', 'wednesday', 'should', 'buy', 'sheepmeat', 'from', 'countries', 'other', 'than', 'britain', 'scientific', 'was', 'clearer', '\"', 'we', 'do', \"n't\", 'support', 'any', 'such', 'recommendation', 'because', 'see', 'grounds', 'for', ',', 'chief', 'spokesman', 'nikolaus', 'van', 'der', 'pas', 'told', 'a', 'news', 'briefing', 'he', 'further', 'study', 'required', 'and', 'if', 'found', 'that', 'action', 'needed', 'taken', 'by', 'proposal', 'last', 'month', 'farm', 'commissioner', 'franz', 'fischler', 'ban', 'brains', 'spleens', 'spinal', 'cords', 'human', 'animal', 'food', 'chains', 'highly', 'specific', 'precautionary', 'move', 'protect', 'health', 'proposed', 'eu-wide', 'measures', 'after', 'reports', 'france', 'under', 'laboratory', 'conditions', 'could', 'contract', 'bovine', 'spongiform', 'encephalopathy', '(', 'bse', ')', '--', 'but', 'agreed', 'review', 'his', 'standing', 'mational', 'officials', 'questioned', 'justified', 'as', 'there', 'only', 'slight', 'risk', 'spanish', 'minister', 'loyola', 'de', 'palacio', 'had', 'earlier', 'accused', 'at', 'an', 'ministers', \"'\", 'meeting', 'of', 'causing', 'unjustified', 'alarm', 'through', 'dangerous', 'generalisation', 'backed', 'multidisciplinary', 'committees', 'are', 'due', 're-examine', 'issue', 'early', 'next', 'make', 'recommendations', 'senior', 'have', 'long', 'been', 'known', 'scrapie', 'brain-wasting', 'similar', 'which', 'is', 'believed', 'transferred', 'cattle', 'feed', 'containing', 'waste', 'farmers', 'denied', 'danger', 'their', 'expressed', 'concern', 'government', 'avoid', 'might', 'influence', 'across', 'europe', 'what', 'extremely', 'careful', 'how', 'going', 'take', 'lead', 'welsh', 'national', 'nfu', 'chairman', 'john', 'lloyd', 'jones', 'bbc', 'radio', 'bonn', 'has', 'led', 'efforts', 'public', 'consumer', 'confidence', 'collapsed', 'in', 'march', 'report', 'suggested', 'humans', 'illness', 'eating', 'contaminated', 'beef', 'imported', '47,600', 'year', 'nearly', 'half', 'total', 'imports', 'brought', '4,275', 'tonnes', 'mutton', 'some', '10', 'percent', 'overall', 'rare', 'hendrix', 'song', 'draft', 'sells', 'almost', '$', '17,000', 'london', 'handwritten', 'u.s.', 'guitar', 'legend', 'jimi', 'sold', 'auction', 'late', 'musician', 'favourite', 'possessions', 'florida', 'restaurant', 'paid', '10,925', 'pounds', '16,935', 'ai', 'no', 'telling', 'penned', 'piece', 'hotel', 'stationery', '1966', 'end', 'january', '1967', 'concert', 'english', 'city', 'nottingham', 'threw', 'sheet', 'paper', 'into', 'audience', 'where', 'retrieved', 'fan', 'buyers', 'also', 'snapped', 'up', '16', 'items', 'were', 'put', 'former', 'girlfriend', 'kathy', 'etchingham', 'who', 'lived', 'him', '1969', 'they', 'included', 'black', 'lacquer', 'mother', 'pearl', 'inlaid', 'box', 'used', 'store', 'drugs', 'anonymous', 'australian', 'purchaser', 'bought', '5,060', '7,845', 'guitarist', 'died', 'overdose', '1970', 'aged', '27', 'china', 'says', 'taiwan', 'spoils', 'atmosphere', 'talks', 'beijing', 'taipei', 'spoiling', 'resumption', 'strait', 'visit', 'ukraine', 'taiwanese', 'vice', 'president', 'lien', 'chan', 'this', 'week', 'infuriated', 'speaking', 'hours', 'chinese', 'state', 'media', 'time', 'right', 'engage', 'political', 'foreign', 'ministry', 'shen', 'guofang', 'reuters', ':', 'necessary', 'opening', 'disrupted', 'authorities', 'quoted', 'top', 'negotiator', 'tang', 'shubei', 'visiting', 'group', 'rivals', 'hold', 'now', 'two', 'sides', '...', 'hostility', 'overseas', 'edition', 'people', 'daily', 'saying', 'television', 'interview', 'read', 'comments', 'gave', 'details', 'why', 'considered', 'considers', 'renegade', 'province', 'opposed', 'all', 'gain', 'greater', 'international', 'recognition', 'rival', 'island', 'practical', 'steps', 'towards', 'goal', 'consultations', 'held', 'set', 'format', 'official', 'xinhua', 'agency', 'executive', 'association', 'relations', 'straits', 'july', 'car', 'registrations', '14.2', 'pct', 'yr', '/', 'frankfurt', 'first-time', 'motor', 'vehicles', 'jumped', 'year-earlier', 'period', 'federal', 'office', '356,725', 'new', 'cars', 'registered', '1996', '304,850', 'passenger', '15,613', 'trucks', 'figures', 'represent', '13.6', 'increase', '2.2', 'decline', '1995', 'motor-bike', 'registration', 'rose', '32.7', 'growth', 'partly', 'increased', 'number', 'germans', 'buying', 'abroad', 'while', 'manufacturers', 'domestic', 'demand', 'weak', 'posted', 'gains', 'numbers', 'volkswagen', 'ag', 'won', '77,719', 'slightly', 'more', 'quarter', 'opel', 'together', 'general', 'motors', 'came', 'second', 'place', '49,269', '16.4', 'figure', 'third', 'ford', '35,563', 'or', '11.7', 'seat', 'porsche', 'fewer', 'compared', '3,420', '5522', 'fell', '554', '643', 'greek', 'socialists', 'give', 'green', 'light', 'pm', 'elections', 'athens', 'socialist', 'party', 'bureau', 'prime', 'costas', 'simitis', 'snap', 'its', 'secretary', 'skandalidis', 'reporters', 'announcement', 'cabinet', 'later', 'dimitris', 'kontogiannis', 'newsroom', '+301', '3311812-4', 'bayervb', 'sets', 'c$', '100', 'million', 'six-year', 'bond', 'following', 'announced', 'manager', 'toronto', 'dominion', 'borrower', 'bayerische', 'vereinsbank', 'amt', 'mln', 'coupon', '6.625', 'maturity', '24.sep.02', 'type', 'straight', 'iss', 'price', '100.92', 'pay', 'date', '24.sep.96', 'full', 'fees', '1.875', 'reoffer', '99.32', 'spread', '+20', 'bp', 'moody', 'aa1', 'listing', 'lux', 'freq', '=', 's&p', 'denoms', 'k', '1-10-100', 'sale', 'limits', 'us', 'uk', 'ca', 'neg', 'plg', 'crs', 'deflt', 'force', 'maj', 'gov', 'law', 'home', 'ctry', 'tax', 'provs', 'standard', 'mgt', 'und', '0.275', 'sell', 'conc', '1.60', 'praecip', 'underlying', 'govt', '7.0', 'sept', '2001', 'notes', 'joint', '+44', '171', '542', '7658', 'venantius', '300', '1999', 'frn', 'floating-rate', 'lehman', 'brothers', 'ab', 'swedish', 'mortgage', '-', '12.5', '21.jan.99', 'base', '3m', 'libor', 's23.sep.96', 'aa3', '99.956', 'aa+', 's', 'short', 'first', 'jp', 'fr', 'yes', 'ipma', '2', 'sweden', '5', 'issued', 'off', 'emtn', 'programme', '8863', 'port', 'update', 'syria', 'lloyds', 'shipping', 'intelligence', 'service', 'lattakia', 'aug', 'waiting', 'tartous', 'presently', '24', 'israel', 'plays', 'down', 'fears', 'war', 'colleen', 'siegel', 'jerusalem', 'outgoing', 'peace', 'current', 'tensions', 'between', 'appeared', 'storm', 'teacup', 'itamar', 'rabinovich', 'ambassador', 'washington', 'conducted', 'unfruitful', 'negotiations', 'looked', 'like', 'damascus', 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'massoud', 'rajavi', 'met', 'secretary-general', 'kurdistan', 'democratic', 'kdpi', 'rastegar', 'voiced', 'rebel', 'kurds', 'emphasised', 'resistance', 'continue', 'stand', 'side', 'compatriots', 'movement', 'signals', 'level', 'cooperation', 'oppositions', 'heavily', 'bombarded', 'targets', 'northern', 'pursuit', 'guerrillas', 'iraqi', 'areas', 'outside', 'control', 'bordering', 'patriotic', 'puk', 'kdp', 'main', 'factions', 'forces', 'ousted', 'kuwait', 'gulf', 'clashes', 'parties', 'broke', 'weekend', 'most', 'serious', 'fighting', 'u.s.-sponsored', 'ceasefire', 'shelling', 'positions', 'qasri', 'region', 'suleimaniya', 'near', 'border', 'days', 'killed', 'wounded', 'attack', 'both', 'turkey', 'mount', 'air', 'land', 'strikes', 'own', 'u.s.-led', 'southern', 'protects', 'possible', 'attacks', 'saudi', 'riyal', 'rates', 'steady', 'quiet', 'summer', 'trade', 'manama', 'spot', 'dollar', 'interbank', 'deposit', 'mainly', 'dealers', 'kingdom', 'changes', 'market', 'holidays', 'dealer', 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'4,658,000', '7,047,000', '4,534,000', '5,486,000', '6,688,000', '10,119,000', '6,512,000', '0.37', '0.68', '0.44', '14,775,000', 'oasis', 'gallagher', 'obscene', 'swore', 'hate', 'f...', 'supermodel', 'noel', 'laryngitis', 'house-hunting', 'patsy', 'kensit', 'tickets', 'slough', 'upbeat', 'pretax', '14p', '1009', '12-1', '2p', '468p', '691p', 'hammerson', '8p', '390', 'warburg', 'stances', 'maintaining', 'rental', 'incomes', 'shaved', 'nigel', 'mobbs', 'birt', 'shrinks', '26.5', 'seasonally', '715', '973', 'canadians', '3.72', 'burea', '613', '235-6745', 'palestinian-ruled', 'shaken', 'backdrop', 'four-hour', '0900', 'espionage', 'rings', 'jomhuri', 'eslami', 'photographing', 'pan-turkism', 'exiles', 'fallahiyan', 'distillates', 'kerosene', '99', 'sort', 'noticeable', '45-50', 'differential', 'ports', 'exceed', '27.40', '27.70', '27.22', 'cargoes', '24.00', '24.20', '24.10', '24.24', 'lowest', 'suspicious', 'overhang', 'sulphur', '70-75', 'manuscript', 'thieves', 'testament', 'al-akhbar', 'undated', 'pretended', 'wyche', 'invoked', 'recess', 'confirming', 'predecessor', 'mabus', 'shops', 'shutters', 'exporters', 'purchasing', '1997/98', '199,900', '149,100-a', '74,600', '55,000-b', '161,600', 'a-', '54,600', 'b-', '55,000', 'destinations', 'southland', 'mainframe', 'delivering', 'real-time', 'constitutes', 'contends', 'misappropriating', 'nba', 'continually', 'baxter', 'immuno', 'miilion', 'secures', '747s', 'cos', 'chipset', 'hotels', 'doubletree', 'five-point', 'toxic', 'stock-trade', 'h&r', 'compuserve', 'impasse', 'authorised', 'trashed', 'nudge', 'gop', 'reporter', 're-nomination', 'state-by-state', 'beachcomber', 'highlands', 'foot-long', 'debris', 'bearing', 'markings', 'conducting', 'fireball', 'wallets', 'shoes', 'mechanical', 'leila', 'corcoran', 'writes', 'still-enslaved', 'pleads', 'abraham', 'maimed', 'poses', 'volunteers', 'preserved', 'microfilm', 'stowed', 'rarely', 'enlistment', 'discharge', 'certificates', 'unveiling', 'memorial', '37,000', 'shall', 'oportunity', 'enjoyment', 'sic', 'slavery', 'oppreses', 'liberty', 'outpouring', 'coloured', 'curse', 'separated', '1865', 'well-to-do', 'matron', 'plead', 'dishonourable', 'prisoner', 'thoughtless', 'folly', 'notation', 'regiment', 'pardon', 'anguish', 'amputee', 'terrible', 'haunted', 'posed', 'bare-chested', '1864', 'insight', 'rhythms', 'carbondale', 'editor', 'gen', 'ulysses', 'writings', 'poetic', 'linguistic', 'treasure', 'trove', 'sociology', 'seared', 'consciousness', 'confederacy', 'retirees', 'budge', 'weidman', 'shepherded', 'predicts', 'databases', 'battlefields', 'discharged', 'incapacity', 'inebetude', 'arise', 'masturbation', 're-nominate', '559-mile', 'vow', 'sylvie', 'ajaccio', 'mediterranean', 'defused', 'get-tough', 'jean-louis', 'debre', 'corse', 'lax', 'widely-reported', 'powerless', 'france-soir', 'beauvau', 'nightly', 'figaro', 'shaky', 'seven-month', 'racked', 'low-level', 'separatist-inspired', 'principally', 'backlash', 'om', 'gruppen', 'omlx', 'pulpex', 'pulp', 'timber', 'recycled', 'complements', 'softs', 'cocoa', 'unpredictable', 'risky', 'hedge', '+46-8-700', '1006', 'amsterdam-rotterdam-antwerp', 'tankage', 'week-ago', '29/8/96', '22/8/96', '1/9/95', '400-425', '425', 'naphtha', '50-75', '75-100', '1,650', '1,850-1,900', '325-350', 'kero', '15-20', 'throughput', 'straight-run', 'bunkering', 'blenkinsop', 'anti-nuclear', 'pantomime', 're-enacting', 'brandished', 'theatre-prop', 'gorleben', 'depot', 'mafia', 'catania', 'sicily', 'slaying', '14-year-old', 'nephew', 'puglisi', 'salvatore', 'botta', 'cemetery', 'sicilian', 'hardened', 'anti-mafia', 'tip-off', 'consciences', 'nitto', 'santapaola', 'knelt', 'ambush', 'cemetary', 'canteen', 'limerick', 'warders', 'takings', 'italians', 'hiv-pensioner', 'harassing', 'hookers', '61-year-old', 'aids', 'pensioner', 'pietro', 'hiv', 'cruising', 'red-light', 'hurling', 'blanks', 'sighted', 'arctic', 'spitzbergen', 'rune', 'ntb', 'vnukovo', 'tupolev', 'longyearbyen', 'airstrip', 'rescuers', 'three-engine', 'opera', 'mountainside', '0815', 'coal-mining', 'barentsburg', '1920s', 'galeforce', 'meteorological', 'cellars', 'trees', 'uprooted', 'roofs', 'trains', 'cm', '2.24', '7.4', '2.96', 'communes', 'rmt', 'turnhout', 'scouts', 'camping', 'meadow', 'hindered', 'excavations', 'jumet', 'sex-and-murder', 'lightly', 'tides', 'pesetas', 'chemicals', '4,150', '61.45', '61.94', '4,175', 'madness', 'overreacted', '+34', '585', '2161', 'nahar', 'liberte', 'leveilly', 'finns', 'sex-abuse', 'captive', 'tampere', 'luxury', 'ilkka', 'laasonen', 'iltalehti', 'audi', 'ingolstadt', 'demel', 'hedged', 'kekkila', 'full-yr', 'fertilisers', 'saplings', 'trend', 'fluctuations', 'end-year', 'interview-t&n', 'untroubled', 't&n', 'bumble', 'predict', 'echoing', 'automotive', 'equally', 'predicting', 'rationalised', 'destocked', '9.5', 'destocking', 'relaxed', 'cycle', 'fraction', 'commenting', 'piston', 'kolbenschmidt', 'obstacles', 'huddart', '8716', 'jolts', 'zealands', 'waiau', 'spa', 'hanmer', 'prone', 'earthquakes', 'smooth', 'trevelyan', 'half-yearly', 'absolutely', 'predicated', 'precepts', 'trough', 'draconian', 'rentals', 'reclamation', 'narrowly', 'downtrend', 'hover', 't$', '27.482', '27.495', '275', '2-5080815', '20,500', '20,605', 'touching', 'intraday', '20,530', '20,725', '19,560', 'technically', '20,300', '21,000', 'doreen', 'siow', '65-8703092', 'siam', 'banker', '8.46', '662', '652-0642', \"m'bishi\", '7-year', 'tokyo-mitsubishi', '2.95', 'sep.03', 'sep.96', 'int', 'mar.97', 'sub', 'jul-18.jul', 'jcr', 'jbri', 'daewoo', 'dacom', '822', '727', '5644', 'commem', 'manufactures', 'ski', 'antara', 'evident', 'happening', 'motivated', 'major-general', 'utomo', 'indonesias', 'extinguished', 'injures', 'exaggerate', '11.38', '11.45', '918', '490', '538', 'rents', 'haneda', 'itami', 'beijing-backed', 'post-handover', 'contesting', 'stymies', '400-strong', 'dismantle', 'fully-elected', 'considerable', 'directorate-grade', 'bureaucrats', '33,000', 'tier', 'mandarin', 'secretaries', 'src', 'barrel-per-day', 'distillation', 'stuck', 'x-ray', 'no.3', 'varied', 'exchanger', '285,000', 'co-owned', 'petrolem', 'joint-venture', 'chevron', 'texaco', '+65-8703086', 'loxley', '332.66', 'reviewed', '6.66', '266.37', '662-252-9950', 'mth', 'pvs', 'yr-ago', '+0.1', '+0.0', '119.3**', '+3.7', '+3.6', '+4.7', '119.3', '+0,2', '+3.5', '+5.2', 'jobless', 'inem', '63,913', '33,149', '65,345', '2.17m', '13.67', '14.15', '15.19', '196.8', '180.6', '279.9', 'cur', '110.4', '+1,161', '+400.9', '+310.4', '54,703.0', '+0.2', '119.6**', '+1.2', '+1.5', '+7.1', '119.6', 'prod', '+1.0', '+9.8', '108.4**', 'm4', '+2.6', '+4.2r', '+10.8', 'adj', 'trln', '75.912', '1,100.7', '1,164.1', '988.2', '1,315.7', '1,433.4', '1,236.5', '215.0', '269.3', '248.3', '1,334.0', '1,119.0', '1,420.9', 'govt.budget', 'govt.fcast', '+282.1', '380.6', '+230.4', 'def', '1,184.0', '1,466.1', '1,456.7', 'qtr', 'epa', '+168,130', '+31,230', '+167,330', 'yr-yr', '+1.9', '+2.3r', '+3.4', '18.1', '16.9', '69.7', '7.25', '7.50', '04/06/96', 'customs-cleared', 'annualised', 'ine', 'variation', 'total-', 'corresponds', 'relate', 'finances', '**general', '100=1992', '100=1990', 'obstructing', 'lasa-zabala', 'gal', 'prescriptions', 'gomez', 'liano', 'aznar', 'bch', 'hive', 'coopers', 'lybrand', 'emigrates', 'lenzing', 'viscose', 'fibre', 'preview', '84.5', 'schillings', 'fibres', 'economies', '+431', '53112', 'fundamentalists', 'masterminds', 'ideologists', 'financers', 'clarifications', 'clarification', '1676/96', 'amending', '2454/93', '2913/92', 'donna', 'topic', 'pundits', 'debated', 'forgo', 'unflagging', 'sidestepped', 'strong-dollar', 'bentsen', 'viewed', 'distinctively', 'widness', 'timely', 'instances', 'post-second', '1.3438', '79.75', 'stands', '1.48', 'expertise', 'co-chairman', 'faust', 'bailard', 'biehl', 'kaiser', 'awfully', 'bentsen-era', 'greenspan', 'reappointment', 'perelstein', 'mainstay', 'amstetten', 'ewald', 'brenner', 'stieglmair', 'brunmayr', '43rd', 'marino', 'hartson', 'best-of-three', 'ex-world', '24-kms', 'samokhalova', 'spirited', 'quigley', 'numerical', 'superiority', 'goulnara', 'fatkoullina', 'snatching', 'lapped', 'ingrid', 'haringa', 'lancien', '12.341', '12.348', '12.130', '12.124', '12.112', '12.246', '11.959', '12.225', '24-km', '31.081', 'tatiana', 'stiajkina', 'arndt', 'vikstedt-nyman', 'boyden', 'godras', 'kombouare', 'abderdeen', '467,000', 'aitken', 'affection', 'ginola', 'influential', 'influenced', 'imola', 'doohan', '50.250', 'bayle', '1:50.727', 'norifumi', 'abe', '1:50.858', 'luca', 'cadalora', '1:51.006', 'criville', '1:51.075', '1:51.287', 'tadayuki', 'okada', '1:51.528', 'checa', '1:51.588', '1:51.784', 'shinichi', 'itoh', '1:51.857', '216', '217', 'swe', 'bradley', 'hughes']\n"
          ]
        }
      ],
      "source": [
        "print(itos)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 350,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zY2c7LTWqiGw",
        "outputId": "ed99b0fb-2c13-4a61-f8fa-01da621621c1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "21014"
            ]
          },
          "execution_count": 350,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(itos)  # liczba różnych tokenów w słowniku"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 351,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9c2CpUmHqiGw",
        "outputId": "1a2c99d3-bddc-4651-e0b2-e2fa258d8a45"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "execution_count": 351,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "v[\"rejects\"]  # indeks tokenu `on`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 352,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qo1O1sjqiGw",
        "outputId": "f52c5dcd-79d0-46ff-96a8-0edb7b52a90d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 352,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "v[\"<unk>\"]  # indeks nieznanego tokenu"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ej1VUlaIqiGx"
      },
      "source": [
        "W przypadku, gdy w analizowanym tekście znajdzie się token, którego nie ma w słowniku, będzie reprezentowany przez indeks domyślny (*default index*). Ustawiamy, żeby był taki sam, jak indeks „nieznanego tokenu”:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 353,
      "metadata": {
        "id": "A54BTVxTqiGx"
      },
      "outputs": [],
      "source": [
        "v.set_default_index(v[\"<unk>\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 354,
      "metadata": {
        "id": "BvEU0ngDtIaI"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 355,
      "metadata": {
        "id": "gmjodlv-qiGx"
      },
      "outputs": [],
      "source": [
        "def data_process(dt):\n",
        "    # Wektoryzacja dokumentów tekstowych.\n",
        "    return [\n",
        "        torch.tensor(\n",
        "            [v[\"<bos>\"]] + [v[token] for token in document] + [v[\"<eos>\"]],\n",
        "            dtype=torch.long,\n",
        "            device=device\n",
        "        )\n",
        "        for document in dt\n",
        "    ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 356,
      "metadata": {
        "id": "IsF3-tbOqiGx"
      },
      "outputs": [],
      "source": [
        "def labels_process(dt):\n",
        "    # Wektoryzacja etykiet (NER)\n",
        "    return [torch.tensor([0] + document + [0], dtype=torch.long, device=device) for document in dt]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 357,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_NER_tags(labels_array):\n",
        "    NER_tags = []\n",
        "    for labels in labels_array:\n",
        "        NER_tags.append([NER_labels[label] for label in labels])\n",
        "    return NER_tags\n",
        "\n",
        "def get_labels(NER_tags_array):\n",
        "    labels = []\n",
        "    for NER_tags in NER_tags_array:\n",
        "        labels.append([NER_labels_reverse[tag] for tag in NER_tags])\n",
        "    return labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 358,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 3, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 7, 7, 7, 1, 1, 1, 3, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 3, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "['O', 'B-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']\n",
            "['O', 'B-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']\n",
            "197\n",
            "197\n"
          ]
        }
      ],
      "source": [
        "training_NER_tags = get_NER_tags(training_labels)\n",
        "training_labels_from_NER_tags = get_labels(training_NER_tags)\n",
        "print(training_NER_tags[1])\n",
        "print(training_labels[1])\n",
        "print(training_labels_from_NER_tags[1])\n",
        "print(len(training_NER_tags[1]))\n",
        "print(len(training_labels[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 359,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1, 1, 2, 7, 7, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 5, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 3, 4, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 5, 1, 0, 1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 0, 1, 1, 3, 4, 1, 1, 1, 1, 1, 1, 5, 8, 1, 0, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 0, 1, 1, 1, 5, 1, 5, 8, 1, 1, 0, 1, 1, 0, 1, 1, 3, 4, 1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 1, 5, 1, 0, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 0, 1, 1, 3, 4, 1, 1, 1, 5, 1, 0, 1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 0, 1, 1, 1, 1, 1, 5, 1, 0, 1, 1, 1, 1, 0, 1, 1, 3, 4, 1, 1, 3, 1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 5, 1, 0, 1, 1, 1, 1, 3, 4, 1, 1, 1, 1, 1, 0, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 1]\n",
            "['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'I-LOC', 'O', 'O', 'B-ORG', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O']\n",
            "254\n",
            "254\n"
          ]
        }
      ],
      "source": [
        "validation_NER_tags = get_NER_tags(validation_labels)\n",
        "print(validation_NER_tags[1])\n",
        "print(validation_labels[1])\n",
        "print(len(validation_NER_tags[1]))\n",
        "print(len(validation_labels[1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYKGlMyVqiGx"
      },
      "source": [
        "Teraz wektoryzujemy wszystkie dane:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 360,
      "metadata": {
        "id": "YQ2hjEmEqiGy"
      },
      "outputs": [],
      "source": [
        "train_tokens_ids = data_process(training_data)\n",
        "test_tokens_ids = data_process(testing_data)\n",
        "validation_tokens_ids = data_process(validation_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 361,
      "metadata": {
        "id": "0t9pla2UqiGy"
      },
      "outputs": [],
      "source": [
        "train_labels = labels_process(training_NER_tags)\n",
        "validation_labels = labels_process(validation_NER_tags)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X9ACw_qhqiGy"
      },
      "source": [
        "Przykład, jak wyglądają dane po zwektoryzowaniu:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 362,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([  2, 257, 258, 259, 260, 261,  72, 262, 263, 264,  12,  13, 265,  17,\n",
            "         13,  81, 257, 177, 266, 260, 163,  81, 259,  95, 267, 268, 269, 270,\n",
            "        258,  59, 271,  72, 262, 263, 264,  22,  23, 158, 159, 272, 163, 253,\n",
            "        163,  18, 273, 274,  42, 275, 276,  12,  13,  81, 277, 278, 279, 280,\n",
            "        281, 132, 263, 282, 134,  72,  18, 260, 163,  61, 283,  64, 284, 285,\n",
            "         61,  73, 189, 258, 286,  22,  81, 287, 163, 265, 288, 289, 233, 273,\n",
            "        290,  12,  13, 158,  18, 291, 163,  81, 292, 293, 294, 233,  18, 295,\n",
            "        296, 163, 297,  84, 298,  18, 299, 163, 300, 301,  18, 302,  73, 303,\n",
            "         24,  59, 304,  95,  81, 305,  12,  13, 306, 307, 308, 309, 310,  55,\n",
            "        311,  91, 312, 313, 309,  72, 272,  95, 258,  42, 314, 315, 316, 317,\n",
            "         73, 318, 319,  26, 320,  53, 290,   8, 321,  12,  13, 322, 323,  81,\n",
            "        324, 325,  88, 326, 163, 327, 328, 329, 330,  95, 258,   8, 331, 139,\n",
            "        332,  73, 189, 159, 333, 334, 335, 336,  72, 337, 281, 132, 263, 338,\n",
            "        134,  12,  13,  18, 339, 340, 163,  81, 332, 341, 233, 342, 343, 344,\n",
            "         12,  13,   3], device='cuda:0')\n",
            "['rare', 'hendrix', 'song', 'draft', 'sells', 'for', 'almost', '$', '17,000', '.', '</s>', 'london', '1996-08-22', '</s>', 'a', 'rare', 'early', 'handwritten', 'draft', 'of', 'a', 'song', 'by', 'u.s.', 'guitar', 'legend', 'jimi', 'hendrix', 'was', 'sold', 'for', 'almost', '$', '17,000', 'on', 'thursday', 'at', 'an', 'auction', 'of', 'some', 'of', 'the', 'late', 'musician', \"'s\", 'favourite', 'possessions', '.', '</s>', 'a', 'florida', 'restaurant', 'paid', '10,925', 'pounds', '(', '$', '16,935', ')', 'for', 'the', 'draft', 'of', '\"', 'ai', \"n't\", 'no', 'telling', '\"', ',', 'which', 'hendrix', 'penned', 'on', 'a', 'piece', 'of', 'london', 'hotel', 'stationery', 'in', 'late', '1966', '.', '</s>', 'at', 'the', 'end', 'of', 'a', 'january', '1967', 'concert', 'in', 'the', 'english', 'city', 'of', 'nottingham', 'he', 'threw', 'the', 'sheet', 'of', 'paper', 'into', 'the', 'audience', ',', 'where', 'it', 'was', 'retrieved', 'by', 'a', 'fan', '.', '</s>', 'buyers', 'also', 'snapped', 'up', '16', 'other', 'items', 'that', 'were', 'put', 'up', 'for', 'auction', 'by', 'hendrix', \"'s\", 'former', 'girlfriend', 'kathy', 'etchingham', ',', 'who', 'lived', 'with', 'him', 'from', '1966', 'to', '1969', '.', '</s>', 'they', 'included', 'a', 'black', 'lacquer', 'and', 'mother', 'of', 'pearl', 'inlaid', 'box', 'used', 'by', 'hendrix', 'to', 'store', 'his', 'drugs', ',', 'which', 'an', 'anonymous', 'australian', 'purchaser', 'bought', 'for', '5,060', 'pounds', '(', '$', '7,845', ')', '.', '</s>', 'the', 'guitarist', 'died', 'of', 'a', 'drugs', 'overdose', 'in', '1970', 'aged', '27', '.', '</s>']\n"
          ]
        }
      ],
      "source": [
        "print(train_tokens_ids[1])\n",
        "print(training_data[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 363,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-_BVuACqiGy",
        "outputId": "82b28db7-8d23-494a-e164-75bbc75c2653"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([  2,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  13,\n",
              "         16,  17,  13,  18,  19,  20,  21,  22,  23,  24,  25,  26,   6,  27,\n",
              "          8,  28,   8,  29,  10,  11,  30,  31,  32,  33,  34,  35,  36,  37,\n",
              "         38,  39,   8,  40,  12,  13,  41,  42,  43,   8,  18,  19,  44,  42,\n",
              "         45,  46,  47,  48,  21,  22,  49,  28,  50,  51,  52,  53,  54,  55,\n",
              "         56,  57,  30,  18,  58,  27,  59,  60,  12,  13,  61,  62,  63,  64,\n",
              "         65,  66,  67,  68,  69,  62,  63,  64,  70,  66,  71,  72,  24,  73,\n",
              "         61,  18,  20,  42,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
              "         12,  13,  84,  21,  85,  58,  86,  59,  87,  88,  89,  24,  59,  90,\n",
              "         91,  92,  59,  93,  24,  50,  38,  94,  95,  18,  19,  44,  12,  13,\n",
              "         84,  21,  81,  96,  97,  98,  95,   4,  99, 100, 101, 102,   8, 103,\n",
              "         40, 104,  73, 105,  88, 106, 107,  53,  18, 108,  88, 109, 110, 111,\n",
              "         59,  81, 112, 113,  88, 114, 115,   8, 116, 108, 117,  12,  13, 102,\n",
              "        118, 119, 120, 121, 122,  53,  57,  88, 123,  91, 124, 125, 126,  40,\n",
              "        127, 128, 129, 130, 131, 132, 133, 134, 135,  34,  35,  36,  12,  13,\n",
              "        136, 102, 137,   8, 138, 139,  96, 121,  18,   4,  42, 140,  45,  46,\n",
              "         73, 141, 109, 117, 142,  73, 143,  89,  67,  92,  59, 144, 145, 146,\n",
              "         59, 147,  81, 148, 149,   8, 108, 117,  12,  13, 150,  99, 151, 152,\n",
              "        153, 154, 155, 156, 157, 102, 158, 159,   4,  99, 160, 161, 162, 163,\n",
              "        164, 165, 166, 167,  61, 168, 169,  12,  61,  13,  12,  13, 147, 123,\n",
              "         88,  57, 170, 102,  42,  96,  12,  13,  18,   4,  42,  58,  45,  88,\n",
              "        171, 172, 173, 174,   8, 175,  18, 176, 177, 178,  98,  88, 179, 180,\n",
              "          8,  18, 181,  45, 142,  12,  13,  40, 182, 183, 184, 185,   8, 128,\n",
              "        186,  73,  81, 187,  36, 188,   8, 133, 189, 190, 191,   8, 182, 184,\n",
              "        192,   8, 193, 167, 194, 195, 109, 196,  12,  13,  10, 197, 198,  22,\n",
              "         23, 146,  59,  66, 199,   8, 108, 117,  53, 200,  40,  73, 136, 201,\n",
              "        202,  91,   6, 203,  27,   8,  28,   8, 204,  10,  11, 205, 206,  28,\n",
              "        207, 208,  12,  13,  61, 209,  62, 182,   8,  38, 210, 211, 163, 190,\n",
              "        212,  55,  54, 173, 213,   8, 214,  41,  42, 215,  73,  61, 216, 217,\n",
              "        197, 161,  44, 132, 218, 134, 219, 220, 221, 222,  21,  22, 223, 224,\n",
              "         12,  13, 225, 226, 227, 228,   8, 116, 229, 117, 121, 230, 231, 232,\n",
              "        233, 234, 121,  81,  10, 235, 236, 237, 127, 128, 159, 238, 188,   8,\n",
              "         34,  35,  36,  95, 239, 240, 241,  12,  13,  41, 242, 243,  40,  53,\n",
              "         57,  97, 244,  73, 245, 246, 163, 247, 248,  12,  13,  24, 249, 233,\n",
              "        250, 251, 163,  10, 252,  73, 253, 254, 255, 163, 256, 248,  12,  13,\n",
              "          3], device='cuda:0')"
            ]
          },
          "execution_count": 363,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_tokens_ids[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 364,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNjf5FYZqiGz",
        "outputId": "04a64703-00b7-4d16-f090-476659626384",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0, 0, 1, 2, 1, 1, 1, 2, 1, 1, 1, 3, 4, 1, 5, 1, 1, 1, 0, 6, 1, 1, 1, 1,\n",
              "        1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        5, 1, 1, 1, 1, 0, 6, 1, 1, 1, 3, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 0, 1, 1, 1, 3, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 6, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 0, 1, 1, 3, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 2, 1, 1, 1, 1, 5, 1, 5, 1,\n",
              "        1, 1, 1, 1, 1, 1, 2, 7, 7, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1,\n",
              "        1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 3, 4, 4, 1, 1, 1, 3, 1, 1, 0, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 5, 1, 3, 1, 1, 1, 1,\n",
              "        1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 0, 6, 6, 6,\n",
              "        6, 1, 0, 1, 1, 3, 4, 4, 1, 1, 0, 6, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 5, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], device='cuda:0')"
            ]
          },
          "execution_count": 364,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_labels[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lfoh9ViiqiGz"
      },
      "source": [
        "Funkcja, której użyjemy do ewaluacji:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 365,
      "metadata": {
        "id": "foChdSdqqiGz"
      },
      "outputs": [],
      "source": [
        "def get_scores(y_true, y_pred):\n",
        "    # Funkcja zwraca precyzję, pokrycie i F1\n",
        "    acc_score = 0\n",
        "    tp = 0\n",
        "    fp = 0\n",
        "    selected_items = 0\n",
        "    relevant_items = 0\n",
        "\n",
        "    for p, t in zip(y_pred, y_true):\n",
        "        if p == t:\n",
        "            acc_score += 1\n",
        "\n",
        "        if p > 0 and p == t:\n",
        "            tp += 1\n",
        "\n",
        "        if p > 0:\n",
        "            selected_items += 1\n",
        "\n",
        "        if t > 0:\n",
        "            relevant_items += 1\n",
        "\n",
        "    if selected_items == 0:\n",
        "        precision = 1.0\n",
        "    else:\n",
        "        precision = tp / selected_items\n",
        "\n",
        "    if relevant_items == 0:\n",
        "        recall = 1.0\n",
        "    else:\n",
        "        recall = tp / relevant_items\n",
        "\n",
        "    if precision + recall == 0.0:\n",
        "        f1 = 0.0\n",
        "    else:\n",
        "        f1 = 2 * precision * recall / (precision + recall)\n",
        "\n",
        "    return precision, recall, f1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWJdBPG3qiGz"
      },
      "source": [
        "Ile mamy różnych tagów NER?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 366,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L3eWXMdoqiGz",
        "outputId": "9de7811f-f1e2-430b-8adf-a7ce1b620645"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "9\n"
          ]
        }
      ],
      "source": [
        "num_tags = max([max(x) for x in training_NER_tags]) + 1\n",
        "print(num_tags)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BoHNlvksqiG0"
      },
      "source": [
        "Implementacja rekurencyjnej sieci neuronowej LSTM:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 367,
      "metadata": {
        "id": "ggcORNh4qiG0"
      },
      "outputs": [],
      "source": [
        "class LSTM(torch.nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(LSTM, self).__init__()\n",
        "        self.emb = torch.nn.Embedding(len(v.get_itos()), 100, device=device)\n",
        "        self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True, device=device)\n",
        "        self.fc1 = torch.nn.Linear(256, num_tags, device=device)\n",
        "\n",
        "    def forward(self, x):\n",
        "        emb = torch.relu(self.emb(x))\n",
        "        lstm_output, (h_n, c_n) = self.rec(emb)\n",
        "        out_weights = self.fc1(lstm_output)\n",
        "        return out_weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1Nx6hyEqiG1"
      },
      "source": [
        "Stworzenie modelu:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 368,
      "metadata": {
        "id": "OT8r_v8KqiG1"
      },
      "outputs": [],
      "source": [
        "lstm = LSTM()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OzNAjAxwqiG2"
      },
      "source": [
        "Definicja funkcji kosztu:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 369,
      "metadata": {
        "id": "qEKt-MjzqiG2"
      },
      "outputs": [],
      "source": [
        "criterion = torch.nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpj3EcFgqiG2"
      },
      "source": [
        "Definicja optymalizatora:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 370,
      "metadata": {
        "id": "sngCZN-3qiG2"
      },
      "outputs": [],
      "source": [
        "optimizer = torch.optim.Adam(lstm.parameters())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vv47s08HqiG2"
      },
      "source": [
        "Funkcja do ewaluacji modelu:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 371,
      "metadata": {
        "id": "DA4CnjisqiG2"
      },
      "outputs": [],
      "source": [
        "def eval_model(dataset_tokens, dataset_labels, model):\n",
        "    Y_true = []\n",
        "    Y_pred = []\n",
        "    for i in tqdm(range(len(dataset_labels))):\n",
        "        batch_tokens = dataset_tokens[i].unsqueeze(0)\n",
        "        tags = list(dataset_labels[i].cpu().numpy())\n",
        "        Y_true += tags\n",
        "\n",
        "        Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
        "        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
        "        Y_pred += list(Y_batch_pred.cpu().numpy())\n",
        "\n",
        "    return get_scores(Y_true, Y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 372,
      "metadata": {},
      "outputs": [],
      "source": [
        "def predict(dataset_tokens, model):\n",
        "    Y_pred = []\n",
        "    for i in tqdm(range(len(dataset_tokens))):\n",
        "        batch_tokens = dataset_tokens[i]\n",
        "\n",
        "        Y_batch_pred_weights = model(batch_tokens)\n",
        "        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
        "        Y_pred.append(list(Y_batch_pred.cpu().numpy()))\n",
        "\n",
        "    return Y_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGH6AJ1GqiG3"
      },
      "source": [
        "Uczenie modelu:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 373,
      "metadata": {
        "id": "slWIOgP4qiG3"
      },
      "outputs": [],
      "source": [
        "NUM_EPOCHS = 15"
      ]
    },
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            "(0.9368163172288059, 0.9291930379746836, 0.9329891057648662)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8fff2a56b5874c5ea43bd0cb587066ef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/945 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9227ca88a05f4790aee025b1acb40cdb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/215 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(0.9347377976134194, 0.9310578661844484, 0.9328942029122275)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c58661cd0496406483f6c53112260d83",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/945 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d284340cac14e24ba44495e5511f6a4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/215 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(0.9295474678176301, 0.9290046714888487, 0.9292759903905036)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a455f4ab7f34765a8e04ed3f4fc2dd0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/945 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6faf4eb684f1476c80b9ae8029ac9679",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/215 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(0.9291776688042248, 0.9279874924653405, 0.9285821992686696)\n"
          ]
        }
      ],
      "source": [
        "for i in range(NUM_EPOCHS):\n",
        "    lstm.train()\n",
        "    # for i in tqdm(range(500)):\n",
        "    for i in tqdm(range(len(train_labels))):\n",
        "        batch_tokens = train_tokens_ids[i].unsqueeze(0)\n",
        "        tags = train_labels[i].unsqueeze(1)\n",
        "\n",
        "        predicted_tags = lstm(batch_tokens)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    lstm.eval()\n",
        "    print(eval_model(validation_tokens_ids, validation_labels, lstm))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Ip_RkVHqiG3"
      },
      "source": [
        "Ewaluacja:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 375,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "8b76a0a3dd6640d0b57de904eed6fa03",
            "15b661c0b4ff451c842e13295286c221",
            "5bb50d0d240b4f67992f73e1ef1b9f4c",
            "ac899d12a1f84487949c531c8cdccc92",
            "1e0834904cef4a91826df2455511c1aa",
            "c03281bc939c442bbb4293844c62ef0f",
            "ce92caacb82b466da35275939f87b2c2",
            "b124d35db3034dc6badaeb2f05f0b3d1",
            "1d6ea318568f41f9945ff9e8c0f04ee1",
            "16993eeb3e894c2380f710d7a779aeaa",
            "3f239a3e75164567a729b6fb2ca7cdcc"
          ]
        },
        "id": "ZS8IufsjqiG3",
        "outputId": "b29f3304-5ee2-4d18-b7ae-76d9316d22e3",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f8d843a96b34326bf36d0a94f5fabb4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/215 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(0.9291776688042248, 0.9279874924653405, 0.9285821992686696)"
            ]
          },
          "execution_count": 375,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "eval_model(validation_tokens_ids, validation_labels, lstm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 376,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50ba9788614f4b229d914d72c4d8d396",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/215 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0, 1, 1, 2, 7, 7, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 7, 1, 1, 1, 1, 1, 5, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 6, 1, 5, 4, 6, 1, 1, 0, 6, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 5, 1, 0, 1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 4, 1, 1, 0, 1, 1, 3, 4, 1, 1, 1, 1, 1, 1, 3, 8, 1, 0, 6, 1, 3, 4, 1, 1, 3, 4, 4, 1, 1, 0, 6, 1, 1, 0, 1, 6, 8, 1, 1, 0, 1, 1, 3, 4, 1, 3, 4, 1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 1, 5, 1, 0, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 4, 1, 1, 0, 6, 1, 5, 8, 1, 1, 1, 5, 1, 0, 1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 0, 1, 1, 3, 1, 1, 5, 1, 0, 1, 1, 3, 1, 3, 4, 1, 3, 4, 4, 1, 3, 1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 4, 1, 1, 5, 1, 0, 1, 1, 3, 1, 3, 4, 1, 1, 1, 1, 1, 0, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 1, 0]\n",
            "tensor([0, 0, 1, 1, 2, 7, 7, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 7, 1,\n",
            "        1, 1, 1, 1, 5, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 6, 1, 5, 4,\n",
            "        6, 1, 1, 0, 6, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 5, 1, 0, 1, 1, 1, 1, 3, 4,\n",
            "        1, 1, 3, 4, 1, 1, 3, 4, 4, 1, 1, 0, 1, 1, 3, 4, 1, 1, 1, 1, 1, 1, 3, 8,\n",
            "        1, 0, 6, 1, 3, 4, 1, 1, 3, 4, 4, 1, 1, 0, 6, 1, 1, 0, 1, 6, 8, 1, 1, 0,\n",
            "        1, 1, 3, 4, 1, 3, 4, 1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3,\n",
            "        4, 1, 1, 1, 1, 5, 1, 0, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4, 4, 1, 1, 0,\n",
            "        6, 1, 5, 8, 1, 1, 1, 5, 1, 0, 1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 3, 4,\n",
            "        1, 1, 1, 0, 1, 1, 3, 1, 1, 5, 1, 0, 1, 1, 3, 1, 3, 4, 1, 3, 4, 4, 1, 3,\n",
            "        1, 1, 1, 1, 3, 4, 1, 1, 3, 4, 4, 1, 1, 5, 1, 0, 1, 1, 3, 1, 3, 4, 1, 1,\n",
            "        1, 1, 1, 0, 1, 1, 3, 4, 1, 1, 3, 4, 1, 1, 1, 1, 0, 0], device='cuda:0')\n",
            "['B-ORG', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'I-MISC', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'I-ORG', 'O', 'B-LOC', 'I-PER', 'I-ORG', 'O', 'O', 'B-ORG', 'I-ORG', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'I-PER', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PER', 'I-LOC', 'O', 'B-ORG', 'I-ORG', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'I-PER', 'O', 'O', 'B-ORG', 'I-ORG', 'O', 'O', 'B-ORG', 'O', 'I-ORG', 'I-LOC', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'I-PER', 'O', 'O', 'B-ORG', 'I-ORG', 'O', 'B-LOC', 'I-LOC', 'O', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'O', 'B-PER', 'I-PER', 'O', 'B-PER', 'I-PER', 'I-PER', 'O', 'B-PER', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'I-PER', 'O', 'O', 'B-LOC', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'B-ORG']\n"
          ]
        }
      ],
      "source": [
        "validation_NER_tags_predicted = predict(validation_tokens_ids, lstm)\n",
        "print(validation_NER_tags_predicted[1])\n",
        "validation_labels_predicted = labels_process(validation_NER_tags_predicted)\n",
        "print(validation_labels_predicted[1])\n",
        "validation_labels_from_NER_tags = get_labels(validation_NER_tags_predicted)\n",
        "print(validation_labels_from_NER_tags[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 377,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open(\"dev-0/out.tsv\", \"w\") as f:\n",
        "    for row in validation_labels_from_NER_tags:\n",
        "        f.write(\" \".join(row[1:-1]) + \"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 378,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d5d5a69256ee437d95de3d47d5f446fb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/230 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "testing_NER_tags = predict(test_tokens_ids, lstm)\n",
        "testing_labels_from_NER_tags = get_labels(testing_NER_tags)\n",
        "test_labels = labels_process(testing_NER_tags)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 379,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "5ed138f531d2431186dc43b4278761ec",
            "a65d0eb58f62421bb8f1ffa7067284c4",
            "9f9d62890d74497da924d440db59e43a",
            "7cbf24d7d45246848c2938ba365bd6c8",
            "2f4c16d3c82c466aae39964018d066c9",
            "3b07776062ca4d5f8876048282c7bb2d",
            "90a7328ac315497281a8c6f030e2dbdb",
            "c113c881abb849d89dd85fee6b673675",
            "44c7e9b5694d4c62adf9d116b0b2b795",
            "d7bc16c9a1be4074b8145a5880547ccf",
            "8233dd8a05de43ce8d3068efedde9a53"
          ]
        },
        "id": "zUku4vFyqiG4",
        "outputId": "c9f4cced-63da-4e59-fc96-ba6dd3d902f0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7838f209829345b0bb639b1065794d04",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/230 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(0.6708165354166231, 0.6767538406423198, 0.6737721083439986)"
            ]
          },
          "execution_count": 379,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "eval_model(test_tokens_ids, test_labels, lstm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 380,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open(\"test-A/out.tsv\", \"w\") as f:\n",
        "    for row in testing_labels_from_NER_tags:\n",
        "        f.write(\" \".join(row[1:-1]) + \"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 381,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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          ]
        }
      ],
      "source": [
        "print(testing_data[0])\n",
        "print(testing_NER_tags[0][1:-1])\n",
        "print(testing_labels_from_NER_tags[0][1:-1])"
      ]
    }
  ],
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    "accelerator": "GPU",
    "author": "Jakub Pokrywka",
    "colab": {
      "gpuType": "T4",
      "provenance": []
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    "email": "kubapok@wmi.amu.edu.pl",
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      "nbconvert_exporter": "python",
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