{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uczenie głębokie – przetwarzanie tekstu – laboratoria\n",
    "# 3. RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using pip 24.0 from c:\\Python312\\Lib\\site-packages\\pip (python 3.12)\n",
      "Looking in indexes: https://download.pytorch.org/whl/cu118\n",
      "Collecting torch\n",
      "  Using cached https://download.pytorch.org/whl/cu118/torch-2.3.0%2Bcu118-cp312-cp312-win_amd64.whl (2673.0 MB)\n",
      "Collecting torchtext\n",
      "  Using cached https://download.pytorch.org/whl/torchtext-0.16.2%2Bcpu-cp312-cp312-win_amd64.whl (1.9 MB)\n",
      "Collecting filelock (from torch)\n",
      "  Using cached https://download.pytorch.org/whl/filelock-3.13.1-py3-none-any.whl (11 kB)\n",
      "Collecting typing-extensions>=4.8.0 (from torch)\n",
      "  Using cached https://download.pytorch.org/whl/typing_extensions-4.9.0-py3-none-any.whl (32 kB)\n",
      "Collecting sympy (from torch)\n",
      "  Using cached https://download.pytorch.org/whl/sympy-1.12-py3-none-any.whl (5.7 MB)\n",
      "Collecting networkx (from torch)\n",
      "  Using cached https://download.pytorch.org/whl/networkx-3.2.1-py3-none-any.whl (1.6 MB)\n",
      "Collecting jinja2 (from torch)\n",
      "  Using cached https://download.pytorch.org/whl/Jinja2-3.1.3-py3-none-any.whl (133 kB)\n",
      "Collecting fsspec (from torch)\n",
      "  Using cached https://download.pytorch.org/whl/fsspec-2024.2.0-py3-none-any.whl (170 kB)\n",
      "Collecting mkl<=2021.4.0,>=2021.1.1 (from torch)\n",
      "  Using cached https://download.pytorch.org/whl/mkl-2021.4.0-py2.py3-none-win_amd64.whl (228.5 MB)\n",
      "Collecting tqdm (from torchtext)\n",
      "  Using cached https://download.pytorch.org/whl/tqdm-4.64.1-py2.py3-none-any.whl (78 kB)\n",
      "Collecting requests (from torchtext)\n",
      "  Using cached https://download.pytorch.org/whl/requests-2.28.1-py3-none-any.whl (62 kB)\n",
      "Collecting torch\n",
      "  Using cached https://download.pytorch.org/whl/cu118/torch-2.2.0%2Bcu118-cp312-cp312-win_amd64.whl (2704.2 MB)\n",
      "Collecting numpy (from torchtext)\n",
      "  Using cached https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-win_amd64.whl (15.5 MB)\n",
      "Collecting torchdata==0.7.1 (from torchtext)\n",
      "  Using cached https://download.pytorch.org/whl/torchdata-0.7.1-py3-none-any.whl (184 kB)\n",
      "Collecting urllib3>=1.25 (from torchdata==0.7.1->torchtext)\n",
      "  Using cached https://download.pytorch.org/whl/urllib3-1.26.13-py2.py3-none-any.whl (140 kB)\n",
      "Collecting MarkupSafe>=2.0 (from jinja2->torch)\n",
      "  Using cached https://download.pytorch.org/whl/MarkupSafe-2.1.5-cp312-cp312-win_amd64.whl (17 kB)\n",
      "Collecting charset-normalizer<3,>=2 (from requests->torchtext)\n",
      "  Using cached https://download.pytorch.org/whl/charset_normalizer-2.1.1-py3-none-any.whl (39 kB)\n",
      "Collecting idna<4,>=2.5 (from requests->torchtext)\n",
      "  Using cached https://download.pytorch.org/whl/idna-3.4-py3-none-any.whl (61 kB)\n",
      "Collecting certifi>=2017.4.17 (from requests->torchtext)\n",
      "  Using cached https://download.pytorch.org/whl/certifi-2022.12.7-py3-none-any.whl (155 kB)\n",
      "Collecting mpmath>=0.19 (from sympy->torch)\n",
      "  Using cached https://download.pytorch.org/whl/mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
      "Collecting colorama (from tqdm->torchtext)\n",
      "  Using cached https://download.pytorch.org/whl/colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
      "Collecting intel-openmp==2021.* (from mkl<=2021.4.0,>=2021.1.1->torch)\n",
      "  Using cached https://download.pytorch.org/whl/intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl (3.5 MB)\n",
      "Collecting tbb==2021.* (from mkl<=2021.4.0,>=2021.1.1->torch)\n",
      "  Using cached https://download.pytorch.org/whl/tbb-2021.11.0-py3-none-win_amd64.whl (298 kB)\n",
      "Installing collected packages: mpmath, urllib3, typing-extensions, sympy, numpy, networkx, MarkupSafe, idna, fsspec, filelock, colorama, charset-normalizer, certifi, tqdm, requests, jinja2, torch, torchdata, torchtext\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not install packages due to an OSError.\n",
      "Consider using the `--user` option or check the permissions.\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python312\\Lib\\site-packages\\pip\\_internal\\commands\\install.py\", line 452, in run\n",
      "    installed = install_given_reqs(\n",
      "                ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Python312\\Lib\\site-packages\\pip\\_internal\\req\\__init__.py\", line 72, in install_given_reqs\n",
      "    requirement.install(\n",
      "  File \"c:\\Python312\\Lib\\site-packages\\pip\\_internal\\req\\req_install.py\", line 856, in install\n",
      "    install_wheel(\n",
      "  File \"c:\\Python312\\Lib\\site-packages\\pip\\_internal\\operations\\install\\wheel.py\", line 725, in install_wheel\n",
      "    _install_wheel(\n",
      "  File \"c:\\Python312\\Lib\\site-packages\\pip\\_internal\\operations\\install\\wheel.py\", line 585, in _install_wheel\n",
      "    file.save()\n",
      "  File \"c:\\Python312\\Lib\\site-packages\\pip\\_internal\\operations\\install\\wheel.py\", line 378, in save\n",
      "    os.unlink(self.dest_path)\n",
      "PermissionError: [WinError 5] Odmowa dostępu: 'c:\\\\Python312\\\\Lib\\\\site-packages\\\\numpy\\\\core\\\\_multiarray_tests.cp312-win_amd64.pyd'\n"
     ]
    }
   ],
   "source": [
    "%pip install --ignore-installed --force-reinstall -v torch torchtext --index-url https://download.pytorch.org/whl/cu118"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "partially initialized module 'charset_normalizer' has no attribute 'md__mypyc' (most likely due to a circular import)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\requests\\compat.py:11\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 11\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mchardet\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'chardet'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcollections\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Counter\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchtext\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvocab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m vocab\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mipywidgets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FloatProgress\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\torchtext\\__init__.py:12\u001b[0m\n\u001b[0;32m      8\u001b[0m _TEXT_BUCKET \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://download.pytorch.org/models/text/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     10\u001b[0m _CACHE_DIR \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexpanduser(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(_get_torch_home(), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data, datasets, prototype, functional, models, nn, transforms, utils, vocab, experimental\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     15\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__, git_version  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\torchtext\\datasets\\__init__.py:3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mimportlib\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mag_news\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AG_NEWS\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mamazonreviewfull\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AmazonReviewFull\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mamazonreviewpolarity\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AmazonReviewPolarity\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\torchtext\\datasets\\ag_news.py:5\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m partial\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Union, Tuple\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatapipes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01miter\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FileOpener, IterableWrapper\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchtext\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_download_hooks\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HttpReader\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchtext\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_internal\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodule_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_module_available\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\torchdata\\__init__.py:9\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright (c) Meta Platforms, Inc. and affiliates.\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# All rights reserved.\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# This source code is licensed under the BSD-style license found in the\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# LICENSE file in the root directory of this source tree.\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchdata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _extension  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datapipes\n\u001b[0;32m     11\u001b[0m janitor \u001b[38;5;241m=\u001b[39m datapipes\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mjanitor\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\torchdata\\datapipes\\__init__.py:9\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright (c) Meta Platforms, Inc. and affiliates.\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# All rights reserved.\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# This source code is licensed under the BSD-style license found in the\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# LICENSE file in the root directory of this source tree.\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DataChunk, functional_datapipe\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;28miter\u001b[39m, \u001b[38;5;28mmap\u001b[39m, utils\n\u001b[0;32m     11\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataChunk\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunctional_datapipe\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miter\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmap\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutils\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\torchdata\\datapipes\\iter\\__init__.py:54\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatapipes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01miter\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mload\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhuggingface\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HuggingFaceHubReaderIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m HuggingFaceHubReader\n\u001b[0;32m     48\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatapipes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01miter\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mload\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01miopath\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     49\u001b[0m     IoPathFileListerIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m IoPathFileLister,\n\u001b[0;32m     50\u001b[0m     IoPathFileOpenerIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m IoPathFileOpener,\n\u001b[0;32m     51\u001b[0m     IoPathSaverIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m IoPathSaver,\n\u001b[0;32m     52\u001b[0m )\n\u001b[1;32m---> 54\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatapipes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01miter\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mload\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01monline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     55\u001b[0m     GDriveReaderDataPipe \u001b[38;5;28;01mas\u001b[39;00m GDriveReader,\n\u001b[0;32m     56\u001b[0m     HTTPReaderIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m HttpReader,\n\u001b[0;32m     57\u001b[0m     OnlineReaderIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m OnlineReader,\n\u001b[0;32m     58\u001b[0m )\n\u001b[0;32m     59\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatapipes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01miter\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mload\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01ms3io\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     60\u001b[0m     S3FileListerIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m S3FileLister,\n\u001b[0;32m     61\u001b[0m     S3FileLoaderIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m S3FileLoader,\n\u001b[0;32m     62\u001b[0m )\n\u001b[0;32m     63\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatapipes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01miter\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransform\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbucketbatcher\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     64\u001b[0m     BucketBatcherIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m BucketBatcher,\n\u001b[0;32m     65\u001b[0m     InBatchShufflerIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m InBatchShuffler,\n\u001b[0;32m     66\u001b[0m     MaxTokenBucketizerIterDataPipe \u001b[38;5;28;01mas\u001b[39;00m MaxTokenBucketizer,\n\u001b[0;32m     67\u001b[0m )\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\torchdata\\datapipes\\iter\\load\\online.py:12\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Dict, Iterator, Optional, Tuple\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatapipes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m functional_datapipe\n\u001b[0;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatapipes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01miter\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IterDataPipe\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\requests\\__init__.py:45\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01murllib3\u001b[39;00m\n\u001b[1;32m---> 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RequestsDependencyWarning\n\u001b[0;32m     47\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     48\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcharset_normalizer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__ \u001b[38;5;28;01mas\u001b[39;00m charset_normalizer_version\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\requests\\exceptions.py:9\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;124;03mrequests.exceptions\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03m~~~~~~~~~~~~~~~~~~~\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03mThis module contains the set of Requests' exceptions.\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01murllib3\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m BaseHTTPError\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m JSONDecodeError \u001b[38;5;28;01mas\u001b[39;00m CompatJSONDecodeError\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mRequestException\u001b[39;00m(\u001b[38;5;167;01mIOError\u001b[39;00m):\n\u001b[0;32m     13\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"There was an ambiguous exception that occurred while handling your\u001b[39;00m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;124;03m    request.\u001b[39;00m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\requests\\compat.py:13\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mchardet\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m---> 13\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcharset_normalizer\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mchardet\u001b[39;00m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m# -------\u001b[39;00m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;66;03m# Pythons\u001b[39;00m\n\u001b[0;32m     19\u001b[0m \u001b[38;5;66;03m# -------\u001b[39;00m\n\u001b[0;32m     20\u001b[0m \n\u001b[0;32m     21\u001b[0m \u001b[38;5;66;03m# Syntax sugar.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\charset_normalizer\\__init__.py:24\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03mCharset-Normalizer\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;124;03m~~~~~~~~~~~~~~\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;124;03m:license: MIT, see LICENSE for more details.\u001b[39;00m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mlogging\u001b[39;00m\n\u001b[1;32m---> 24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m from_bytes, from_fp, from_path, normalize\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlegacy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     26\u001b[0m     CharsetDetector,\n\u001b[0;32m     27\u001b[0m     CharsetDoctor,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     30\u001b[0m     detect,\n\u001b[0;32m     31\u001b[0m )\n\u001b[0;32m     32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CharsetMatch, CharsetMatches\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\charset_normalizer\\api.py:7\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpath\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m basename, splitext\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, BinaryIO, List, Optional, Set\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcd\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m      8\u001b[0m     coherence_ratio,\n\u001b[0;32m      9\u001b[0m     encoding_languages,\n\u001b[0;32m     10\u001b[0m     mb_encoding_languages,\n\u001b[0;32m     11\u001b[0m     merge_coherence_ratios,\n\u001b[0;32m     12\u001b[0m )\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstant\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IANA_SUPPORTED, TOO_BIG_SEQUENCE, TOO_SMALL_SEQUENCE, TRACE\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmd\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mess_ratio\n",
      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\charset_normalizer\\cd.py:9\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01massets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FREQUENCIES\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstant\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KO_NAMES, LANGUAGE_SUPPORTED_COUNT, TOO_SMALL_SEQUENCE, ZH_NAMES\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmd\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_suspiciously_successive_range\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CoherenceMatches\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     12\u001b[0m     is_accentuated,\n\u001b[0;32m     13\u001b[0m     is_latin,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     16\u001b[0m     unicode_range,\n\u001b[0;32m     17\u001b[0m )\n",
      "\u001b[1;31mAttributeError\u001b[0m: partially initialized module 'charset_normalizer' has no attribute 'md__mypyc' (most likely due to a circular import)"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "import torch\n",
    "from torchtext.vocab import vocab\n",
    "from tqdm import tqdm\n",
    "from ipywidgets import FloatProgress\n",
    "\n",
    "import pandas as pd\n",
    "from nltk.tokenize import word_tokenize\n",
    "from unidecode import unidecode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wczytano dane treningowe\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 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",
      "podzielono dane treningowe na słowa\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": [
    "# odczytaj dane treningowe\n",
    "train = pd.read_csv('train/train.tsv', sep='\\t')\n",
    "train.columns = [\"y\", \"x\"]\n",
    "print(\"wczytano dane treningowe\")\n",
    "print(train[\"y\"][0], train[\"x\"][0])\n",
    "\n",
    "# podziel dane treningowe na słowa\n",
    "# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/\n",
    "slowa_train = []\n",
    "for tekst in train[\"x\"]:\n",
    "    pom = []\n",
    "    for slowo in tekst.split(\" \"):\n",
    "        #if slowo not in (\"<\",\"/s\",\">\",\"/S\",\"``\"):\n",
    "        pom.append(slowo.lower())\n",
    "    slowa_train.append(pom)\n",
    "print(\"podzielono dane treningowe na słowa\")\n",
    "print(slowa_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wczytano dane testowe dev-0\n",
      "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",
      "podzielono dane treningowe na słowa\n",
      "['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": [
    "# odczytaj dane testowe dev-0\n",
    "test_dev0 = pd.read_csv('dev-0/in.tsv', sep='\\t')\n",
    "test_dev0.columns = [\"x\"]\n",
    "print(\"wczytano dane testowe dev-0\")\n",
    "print(test_dev0[\"x\"][0])\n",
    "\n",
    "# podziel dane testowe na słowa\n",
    "# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/\n",
    "slowa_test_dev0 = []\n",
    "for tekst in test_dev0[\"x\"]:\n",
    "    pom = []\n",
    "    for slowo in tekst.split(\" \"):\n",
    "        #if slowo not in (\"<\",\"/s\",\">\",\"/S\",\"``\"):\n",
    "        pom.append(slowo.lower())\n",
    "    slowa_test_dev0.append(pom)\n",
    "print(\"podzielono dane treningowe na słowa\")\n",
    "print(slowa_test_dev0[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wczytano dane testowe A\n",
      "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",
      "podzielono dane treningowe na słowa\n",
      "['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"
     ]
    }
   ],
   "source": [
    "# odczytaj dane testowe A\n",
    "test_A = pd.read_csv('test-A/in.tsv', sep='\\t')\n",
    "test_A.columns = [\"x\"]\n",
    "print(\"wczytano dane testowe A\")\n",
    "print(test_A[\"x\"][0])\n",
    "\n",
    "# podziel dane testowe na słowa\n",
    "# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/\n",
    "slowa_test_A = []\n",
    "for tekst in test_A[\"x\"]:\n",
    "    pom = []\n",
    "    for slowo in tekst.split(\" \"):\n",
    "        #if slowo not in (\"<\",\"/s\",\">\",\"/S\",\"``\"):\n",
    "        pom.append(slowo.lower())\n",
    "    slowa_test_A.append(pom)\n",
    "print(\"podzielono dane treningowe na słowa\")\n",
    "print(slowa_test_A[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20998\n",
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'podujevo', 'celopek', 'revoked', 'cracked', 'moderates', 'cradle', 'meningitis', 'emanuil', 'ceausu', 'babes', 'infectious', 'virus', 'lasts', 'gastro-intestinal', 'tract', 'vukasin', 'filipovic', 'supplying', 'staging', 'mismanagement', 'finmin', 'raul', 'matos', 'azocar', '1630', 'imf-hosted', 'seminar', 'metric', 'sail', 'cartagena', 'coffee', 'non-oil', 'non-traditional', '334', '302', 'ocei', 'imf-sponsored', '2.240', '2.693', '951', 'foods', '27.4', '24.6', 'jpr', 'cops', 'racket', 'thirteen', 'lago', 'nacion', 'credibility', 'undermined', 'scandals', 'indictment', 'piotti', '3,600', 'dishonest', 'purged', 'brave', 'kandir', 'constitutionally', 'same-day', 'cetes', 'nervousness', '24.25', 'co-ordinated', 'acceptances', 'pagares', '25.10', 'longer-term', 'masked', 'oaxaca', 'assaults', 'credits', '2.209', 'oversupply', '684', 'shortfall', 'inflow', 'lezama', 'newroom', '728', '9554', '05:53', 'chilpancingo', 'pre-electoral', 'bickering', 'acatepec', 'fanned', 'intimidate', 'stirred', 'agir', 'ensemble', 'pour', 'droits', \"l'homme\", 'exerted', 'psychological', 'grabs', 'aguirre', '47-year-old', 'pessina', 'restaurants', 'travelled', 'condemn', 'day-to-day', 'punish', 'landless', 'peasants', 'countryside', 'ecumenical', 'endemic', 'thirty-six', 'massacred', 'para', 'lucas', 'moreira', 'neves', 'bishops', 'flees', 'passport', 'consular', 'anti-corruption', 'soliciting', 'icac', 'anti-graft', 'bribe', '12,940', 'scams', 'honduran', 'passports', 'thinks', 'solve', 'woes', '1,617', 'rajapat', 'warnings', 'pullouts', 'chatichai', 'choonhavan', 'chavalit', 'yongchaiyudh', 'aspiration', 'insincere', 'slowdown', 'switched', 'monarchy', '1932', 'yassin', 'ramle', '60-year-old', 'fundamentalist', 'wrecking', 'confined', 'freeing', 'skhirat', 'purely', 'vital', 'conlon', 'triumphal', '10-week', 'abruptly', '200-an-hour', 'eavesdrop', 'conversations', 'campaigns', 'invaluable', 'captivated', 'worried', 'senator', 'dianne', 'feinstein', 'bump', '50-year-old', 'dogged', 'wrongdoing', 'misconduct', 'questionable', 'judgment', 'selecting', 'advisers', 'revert', 're-elected', 'revelled', 'heartland', 'hoarse', 'voice', 'resolute', 'wit', 'stilted', 'glib', 'piper', 'jaffray', 'tax-exempt', 'compiled', '8/30', '-------------------------', 'a-rated', \"gen'l\", '4.45', '4.40', '+0.05', '4.90', '-----', '5.40', '5.35', '5.55', 'rev', 'muscular', 'dystrophy', 'gene-therapy', 'technique', 'telethon', 'method', 'muscle', 'degenerates', 'adulthood', 'non-working', 'producing', 'dystrophin', 'altered', 'common-cold', 'minimise', 'susceptibility', 'injected', 'mice', 'bred', 'genes', 'experiment', 'fibers', 'diminishing', 'test-tube', 'immune-system', 'oilseeds', 'oilseed', 'assn', '98,000', 'no.1', 'cargill', 'mitsui', 'dreyfus', '10-30', '16,300', 'dns', '212.00', '205.10', 'ww', '202.65', '19,500', '212.25', '204.74', '199.71', '23,500', '9,600', '4,900', '199.56', '18,278', '195.79', 'donation', 'taichung', 'procurement', '108,000', 'soybeans', 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'amputated', 'improperly', 'disposed', 'deformed', 'infant', 'prosthesis', 'orthopedic', 'educational', 'freezer', 'spoiled', 'crab', 'trap', 'flesh', 'cop', 'shrub', 'guys', 'bagged', 'checker', 'pines', 'drive-through', 'toting', 'earl', 'feugill', 'camouflaged', 'shaggy', 'freeze', 'marines', 'attaching', 'strips', 'burlap', 'disguise', 'staking', 'robberies', 'fast-food', '90-minute', 'stakeout', 'dla', '2.7975', 'logistics', '2.7775', 'nbc', 'consultant', 'reshaped', 'ms-nbc', 'ire', 'repositioning', 'gluten', 'meal', 'steady-higher', 'pickup', '117.00', 'pellets', '320.00', 'decatur', 'il', 'cedar', 'rapids', 'ia', '310.00', 't', '234,324', 'cereals', '105.07', 'ecus', 'provisionally', 'optional-origin', 'enjoys', 'subtracting', '34,277', '109.36', '5432', 'shipyard', '3,500', 'cherbourg', '1,800', 'yeard', 'cutback', 'indre', 'managements', 'curtail', 'estates', 'hub', 'gadoon', 'unearthed', 'police-run', 'cages', 'tando', 'allahyar', 'hyderabad', 'recorder', 'sui', 'technologists', 'sugarcane', 'pillaged', 'gujar', '3,225', 'megawatt', 'on-line', 'mohib', 'textile', 'defaulted', 'leasing', 'modarabas', 'multiply.q', '7.84', 'shahid', 'privatisation', 'sindh', 'ad-interim', 'restraining', 'javedan', 'dadabhoy', 'pvt', '9251-274757', 'directions', 'occupying', 'rebelled', 'wrecked', 'heavily-mined', 'halo', 'roadsides', 'qotbuddin', 'hilal', 'officiated', 'sprinkling', 'petrol', 'andhra', 'pradesh', 'rarest', 'beings', 'roasted', 'appellants', 'plotted', 'k.t.', 'elephant', 'tramples', 'rampaging', '72-year-old', 'trampled', 'hari', 'maya', 'poudels', 'madhumalla', 'asleep', 'beast', 'kingdoms', 'elephants', 'overrun', 'kudapokuna', 'welikanda', 'army-controlled', 'vavuniya', 'undercover', 'vicorp', 'sabourin', 'cfo', 'denver', 'bestop', 'boulder', '1610', 'socal', 'edison', 'kilovolt', 'kv', 'wildfire', 'raging', 'conroy', 'crews', 'fire-related', 'residues', 'megawatts', 'mw', '1,200', 'containment', 'arid', 'windy', 'leong', '1622', 'munis', '0.056', 'municipals', 'semiconductor', 'expectation', 'emerge', 'modem', '2-1/8', '11-3/8', 'modem-chip', 'fast-growing', 'networking', 'elias', 'moosa', 'roberston', 'pmc-sierra', 'chipsets', 'severance', 'randall', 'soundview', 'focusing', 'fastest-growing', 'much-larger', 'develops', 'shrink', 'hambrecht', 'quist', 'low-margin', 'pmc', 'slowing', '206-386-4848', 'housecall', 'sink', 'underperform', '7-3/8', '7-1/8', 'atlanta-based', '0.17', 'non-medicare', 'hospice', 'limitation', 'subsidiaries', '17,024,000', '18,174,000', '31,834,000', '24,137,000', '7,718,000', '6,828,000', '14,668,000', '13,091,000', '9,167,000', '11,175,000', '16,909,000', '10,880,000', '1.05', '1.59', '1.02', '10,650,407', 'forma', '9,306,000', '11,346,000', '17,166,000', '11,046,000', '3,820,000', '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": [
    "v = build_vocab(slowa_train)\n",
    "v.set_default_index(v[\"<unk>\"])\n",
    "itos = v.get_itos()  # mapowanie indeksów na tokeny\n",
    "print(len(itos))  # liczba różnych tokenów w słowniku\n",
    "print(itos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'O': 0, 'B-PER': 1, 'B-LOC': 2, 'I-PER': 3, 'B-MISC': 4, 'I-MISC': 5, 'I-LOC': 6, 'B-ORG': 7, 'I-ORG': 8}\n"
     ]
    }
   ],
   "source": [
    "# slownik etykiety - kody etykiet\n",
    "etykieta_na_kod = {}\n",
    "licznik = 0\n",
    "for tekst in train[\"y\"]:\n",
    "    for etykieta in tekst.split(\" \"):\n",
    "        if etykieta not in etykieta_na_kod:\n",
    "            etykieta_na_kod[etykieta] = licznik\n",
    "            licznik+=1\n",
    "print(etykieta_na_kod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 5, 5, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "# podziel etykiety\n",
    "kody_etykiet_train = []\n",
    "for tekst in train[\"y\"]:\n",
    "    pom = []\n",
    "    for etykieta in tekst.split(\" \"):\n",
    "        pom.append(etykieta_na_kod[etykieta])\n",
    "    kody_etykiet_train.append(pom)\n",
    "print(kody_etykiet_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
      "[0, 0, 4, 5, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 2, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 2, 6, 0, 7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 0, 2, 0, 2, 6, 0, 0, 7, 0, 0, 7, 0, 0, 1, 3, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 0, 2, 0, 7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 1, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "# odczytaj etykiety dev-0\n",
    "labels_dev0 = pd.read_csv('dev-0/expected.tsv', sep='\\t')\n",
    "labels_dev0.columns = [\"y\"]\n",
    "print(labels_dev0[\"y\"][0])\n",
    "\n",
    "# podziel etykiety\n",
    "kody_etykiet_dev0 = []\n",
    "for tekst in labels_dev0[\"y\"]:\n",
    "    pom = []\n",
    "    for etykieta in tekst.split(\" \"):\n",
    "        pom.append(etykieta_na_kod[etykieta])\n",
    "    kody_etykiet_dev0.append(pom)\n",
    "print(kody_etykiet_dev0[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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",
    "        )\n",
    "        for document in dt\n",
    "    ]\n",
    "\n",
    "def labels_process(dt):\n",
    "    # Wektoryzacja etykiet (NER)\n",
    "    return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_tokens_ids = data_process(slowa_train)\n",
    "test_dev0_tokens_ids = data_process(slowa_test_dev0)\n",
    "test_A_tokens_ids = data_process(slowa_test_A)\n",
    "\n",
    "train_labels = labels_process(kody_etykiet_train)\n",
    "test_dev0_labels = labels_process(kody_etykiet_dev0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "944 199\n",
      "214 256\n",
      "229 283\n",
      "tensor([  2,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,\n",
      "         14,  17,   4,  18,  19,   7,  20,  17,   6,  21,  22,  23,  24,  25,\n",
      "          5,  26,  27,   9,  10,  11,  12,  28,  29,  30,  31,  32,  20,  33,\n",
      "         20,  34,  35,  36,  37,  38,  39,  13,  14,  17,  40,  41,  42,  43,\n",
      "         44,  45,  11,  46,  47,   9,  34,   7,  20,  48,  49,  50,  51,  52,\n",
      "         48,  53,  54,   5,  55,  28,  17,  56,  20,  15,  57,  58,  59,  35,\n",
      "         60,  13,  14,  30,  34,  61,  20,  17,  62,  63,  64,  59,  34,  65,\n",
      "         66,  20,  67,  68,  69,  34,  70,  20,  71,  72,  34,  73,  53,  74,\n",
      "         75,  26,  76,  21,  17,  77,  13,  14,  78,  79,  80,  81,  82,  83,\n",
      "         84,  85,  86,  87,  81,   9,  32,  21,   5,  37,  88,  89,  90,  91,\n",
      "         53,  92,  93,  94,  95,  96,  60,  97,  98,  13,  14,  99, 100,  17,\n",
      "        101, 102, 103, 104,  20, 105, 106, 107, 108,  21,   5,  97, 109, 110,\n",
      "        111,  53,  54,  31, 112, 113, 114, 115,   9, 116,  44,  45,  11, 117,\n",
      "         47,  13,  14,  34, 118, 119,  20,  17, 111, 120,  59, 121, 122, 123,\n",
      "         13,  14,   3])\n",
      "tensor([    2,  1949,   459,    65,  1950,  1951,  1592,    13,    14,    15,\n",
      "        19342,    14,  1793,   103,  1465,    20,  1952,  1592,    59,    65,\n",
      "         1950,  1951,  1954,    28,   947,   166,    14,  1992,   166,  1993,\n",
      "         1703,  1965,    21,    31,  2038,   103,  3671,  2932,    13,    14,\n",
      "         1965,  6226,   103, 16331,    45,  1995,  1996,     0,    47,    53,\n",
      "         1993,     0,    13,    14,  1993,  1055,  1330,    53,  1965,  1864,\n",
      "           13,    14, 17021,   166,  1991, 19322,   103, 14088,    45,  1977,\n",
      "            0,  1620,    53, 10801, 12466,  1620,  1962,  1958,     0,     0,\n",
      "           47,    53,  1956, 19326,    45,  1960, 19327, 19328,    47,   103,\n",
      "        16667,    13,    14, 19313,  1363,   166,  2012,     0,    45,  1995,\n",
      "         2752,  5725,  1962,  1967,     0,     0,    47,    53,  1985,     0,\n",
      "           13,    14,    15,    45,    34,  2037,    47,   166,  2020, 14779,\n",
      "           53,  2018,     0,    45,  2030,  2059,  5455,   620,   618,    53,\n",
      "         1967,     0,  1602,    53,  1963,     0,  1976,    53,  1974,     0,\n",
      "         1976,    53,  1958,     0,  3843,    47,    13,    14, 19318,   166,\n",
      "         2002, 16329,    45,  2024, 19320,  9379,    53,  2007,  2008,  1979,\n",
      "         1962,  2061, 10865,     0,    47,    53,  2034,     0,    45,  2024,\n",
      "            0,  2054,    47,    14,  6206,   166, 12584, 11568,   103, 11269,\n",
      "           45,  1974, 19334,  9379,    53,  1967, 19335,  1997,    53,  1967,\n",
      "        17052,  6226,    47,    53,  2000, 15584,   103,     0,    13,    14,\n",
      "         9493,   166,  2026, 10970,   103,     0,    53,  9314,     0,    45,\n",
      "         1974,  2717,     0,    53,     0,  6237,   620,   618,    53,  6223,\n",
      "        19332, 11058,  1962,  6227,   401,     0,    47,    14,  9488,   166,\n",
      "         1972, 19340,   103,     0,    45,  1974,  1975,  4451,   620,   618,\n",
      "           47,    53,  2010, 14739,    45,  6223,  6224,  1979,  1962,  1977,\n",
      "         4839,  1981,    47,    13,    14,     3])\n",
      "tensor([    2,  6342,   769,   459,     0,   960,     9,  1681,   150,    17,\n",
      "          253,    13,    14,  5474,     0,    14,  1681,  3063,     0,     0,\n",
      "           14,    28,   947,     9,   701,  7189,   572,  2124,    30,     0,\n",
      "          300,   301,    17,   253,   150,    34, 14863,  6363,   371,    68,\n",
      "           26,  3333,   370,  3631,   608, 11618,    13,    14,     0,    53,\n",
      "           92,  1738,  1753,     0,   154,    26,  3388,    97,  1952,    28,\n",
      "         3978,   145,    53,    26,  2116,    59,    17,     0,  2099, 14403,\n",
      "          148,   186,    20,    34,   695,  2519,     0,    21,  1208,    30,\n",
      "            0,   324,   729,    13,    14,  2725,     0,   185,   618,   863,\n",
      "         1521,   103,     0,   154,    68,   197,  2954,   960,  2955,     0,\n",
      "            0,    53,    92,   197,   170,  3063,     9,    34,  1208,  2154,\n",
      "          150,  1824,  1053,   618,    20,    34,   457,   695,    13,    14,\n",
      "            0,   371,   110, 12530,   150,    34,   274,  1593,  1711,    53,\n",
      "           74,    68,   596,   452,    94,  1458,  2954,    96,    34,  1681,\n",
      "          749,    85,  2517,  1208,    59,    34,  8797,  9174,    13,    14,\n",
      "            0,   154,    68,   197,  4705,    34,  6392,   186,   836,  2521,\n",
      "          700,    17, 17097,    13,    14,    48,    68,  1240,    34,  1593,\n",
      "         1711,    28,    34,  4645,  3370,    53,    48,     0,   154,    13,\n",
      "           14,    48,  1500,  1798,    75,   693,   226,  5612,    97,   606,\n",
      "           95,   960,   103,    68,   154,    68,   693,   226,  1221,    13,\n",
      "           14,  1500,  4604,   184,   185,    34,   156,   155,     9,    95,\n",
      "           97,   671,    13,    48,    14,  2099,   166,  2718,     0,    53,\n",
      "            0, 16807,    53,     0,     0,    53,  2886,     0,    53,     0,\n",
      "            0,    53,  2854,     0,    53, 10959,     0,    53, 11542,     0,\n",
      "           53,     0,     0,    53,  2219,     0,    53,     0,     0,    53,\n",
      "         4036,     0,    53, 17118,     0,    53, 11460,     0,     0,    53,\n",
      "         4036,     0,    53,     0,     0,    53,     0,     0,    53,  9462,\n",
      "            0,    53, 13541,     0,    53, 11542,     0,    53,  2219,     0,\n",
      "           13,    14,     3])\n"
     ]
    }
   ],
   "source": [
    "print(len(train_tokens_ids), len(train_tokens_ids[0]))\n",
    "print(len(test_dev0_tokens_ids), len(test_dev0_tokens_ids[0]))\n",
    "print(len(test_A_tokens_ids), len(test_A_tokens_ids[0]))\n",
    "\n",
    "print(train_tokens_ids[0])\n",
    "print(test_dev0_tokens_ids[0])\n",
    "print(test_A_tokens_ids[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "944 199\n",
      "214 256\n",
      "tensor([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        2, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 5, 5, 0, 0,\n",
      "        0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 3, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 4, 5, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0,\n",
      "        0, 0, 0, 2, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 1, 3, 0,\n",
      "        0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0,\n",
      "        0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 2, 6, 0,\n",
      "        7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 0, 2, 0, 2, 6, 0, 0, 7, 0,\n",
      "        0, 7, 0, 0, 1, 3, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3,\n",
      "        0, 0, 0, 0, 2, 0, 7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0,\n",
      "        0, 1, 3, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0,\n",
      "        0, 0, 7, 0, 0, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 1, 0,\n",
      "        0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 0,\n",
      "        0, 0, 7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 0, 0])\n"
     ]
    }
   ],
   "source": [
    "print(len(train_labels), len(train_labels[0]))\n",
    "print(len(test_dev0_labels), len(test_dev0_labels[0]))\n",
    "\n",
    "print(train_labels[0])\n",
    "print(test_dev0_labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_tags = len(etykieta_na_kod.keys())\n",
    "\n",
    "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)\n",
    "        self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)\n",
    "        self.fc1 = torch.nn.Linear(256, num_tags)\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": "code",
   "execution_count": null,
   "metadata": {},
   "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].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.numpy())\n",
    "\n",
    "    return get_scores(Y_true, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm = LSTM()\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(lstm.parameters())\n",
    "NUM_EPOCHS = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|████████▊ | 832/944 [00:37<00:05, 21.94it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[23], line 13\u001b[0m\n\u001b[0;32m     10\u001b[0m     optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m     11\u001b[0m     loss \u001b[38;5;241m=\u001b[39m criterion(predicted_tags\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m0\u001b[39m), tags\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m---> 13\u001b[0m     \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     14\u001b[0m     optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[0;32m     16\u001b[0m lstm\u001b[38;5;241m.\u001b[39meval()\n",
      "File \u001b[1;32mc:\\Users\\Dominik\\Desktop\\Studia\\11.sem1\\en-ner-conll-2003\\.venv\\Lib\\site-packages\\torch\\_tensor.py:525\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    515\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    517\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    518\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    523\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m    524\u001b[0m     )\n\u001b[1;32m--> 525\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    526\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m    527\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Dominik\\Desktop\\Studia\\11.sem1\\en-ner-conll-2003\\.venv\\Lib\\site-packages\\torch\\autograd\\__init__.py:267\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    262\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    264\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    265\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    266\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 267\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    269\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    270\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    271\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    272\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    273\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    274\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    275\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Dominik\\Desktop\\Studia\\11.sem1\\en-ner-conll-2003\\.venv\\Lib\\site-packages\\torch\\autograd\\graph.py:744\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    742\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m    743\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 744\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    745\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    746\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    747\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    748\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 214/214 [00:00<00:00, 262.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.6558005752636625, 0.7225352112676057, 0.687552353828112)\n"
     ]
    }
   ],
   "source": [
    "print(eval_model(test_dev0_tokens_ids, test_dev0_labels, lstm))"
   ]
  }
 ],
 "metadata": {
  "author": "Jakub Pokrywka",
  "email": "kubapok@wmi.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": "3.12.3"
  },
  "subtitle": "11.NER RNN[ćwiczenia]",
  "title": "Ekstrakcja informacji",
  "year": "2021"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}