diff --git a/README.md b/README.md
index 52f7b3c..1057df7 100644
--- a/README.md
+++ b/README.md
@@ -57,3 +57,12 @@
 
 
 
+### Лабораторная работа №4
+
+| Группа  |    Дата    | 
+|:--------|:----------:| 
+| А-01-20 | 09.04.2024 | 
+| А-03-20 | 16.04.2024 | 
+
+* [Задание](labs/OATD_LR4.ipynb) 
+
diff --git a/labs/OATD_LR4.ipynb b/labs/OATD_LR4.ipynb
new file mode 100644
index 0000000..8c2c85e
--- /dev/null
+++ b/labs/OATD_LR4.ipynb
@@ -0,0 +1,2423 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vOpSJBZfFKuo"
+      },
+      "source": [
+        "![](https://camo.githubusercontent.com/518a06d7ca808cd4ad8d5b6deb4ef15983d4649737618153432479f977935bba/68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f79616e646578646174617363686f6f6c2f6e6c705f636f757273652f6d61737465722f7265736f75726365732f657870616e64696e675f6d696e645f6c6d5f6b6e5f332e706e67)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ha8cW_h-FIND"
+      },
+      "source": [
+        "reference:\n",
+        "\n",
+        "  - [nlp for you](https://lena-voita.github.io/nlp_course/language_modeling.html)\n",
+        "\n",
+        "  - [YSDA Natural Language Processing course](https://github.com/yandexdataschool/nlp_course/tree/2023/week03_lm)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Лабораторная работа №4. Использование нейронных сетей для генерации текста\n",
+        "В ходе работы мы будем обучать нейронные сети генерировать тексты, похожие на стихи поэтов.\n",
+        "\n",
+        "## Цель работы\n",
+        "Получить практические навыки решения задачи генерации текста.\n",
+        "\n",
+        "## Указания\n",
+        "1. Для работы рекомендуется использовать Google Colab и среду с GPU для ускорения расчетов. Для установки среды, использующей GPU в Google Colab нужно выбрать пункт меню \"Среда выполнения\" -> \"Сменить среду выполнения\" -> выбрать аппаратный ускоритель \"GPU\".\n",
+        "\n",
+        "2. Выполнять работу следует последовательно запуская ячейки, анализируя код и приведенные комментарии и разъяснения.\n",
+        "\n",
+        "3. В ходе работы будут встречаться вопросы, на которые нужно ответить, создав после него новую ячейку. Вопросы отмечены заголовками 3-го уровня.\n",
+        "Для ответа досточно 1-2 предложений. Но будьте готовы более подробно его пояснить при устной беседе.\n",
+        "\n",
+        "4. Обращайте внимание на комментарии `<your choice here>` - здесь вам нужно будет вставить значения параметров либо исходя из анализа кода\\выборки (где указано), либо попробовать разные варианты. Парамеры, приведенные тут по умолчанию, не обязательно правильные.\n",
+        "\n",
+        "## Варианты заданий\n",
+        "\n",
+        "Четные номера по журналу - Пушкин, нечетные - Маяковский.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "H2QPNriQIvTw"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "96PukQZbFNwL"
+      },
+      "source": [
+        "# Загрузка библиотек"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "nyb7MBAsFNhh"
+      },
+      "outputs": [],
+      "source": [
+        "import copy\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "\n",
+        "from random import sample\n",
+        "from IPython.display import clear_output\n",
+        "from torch.utils.data import DataLoader, TensorDataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eyO1tDFiGwFu"
+      },
+      "source": [
+        "При использовании Google Colab следует выбрать среду выполнения с аппаратным ускорителем GPU, что существенно ускорит расчеты. Для установки среды, использующей GPU в Google Colab нужно выбрать пункт меню \"Среда выполнения\" -> \"Сменить среду выполнения\" -> выбрать аппаратный ускоритель \"GPU\". При этом, следующая ячейка, проверяющая доступность CUDA (платформы, использующей графические ускорители), должна возвращать `True`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XtIrqKT4GSXf",
+        "outputId": "376ab52b-e0d9-4c48-a01a-0c1191d9fa5f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ],
+      "source": [
+        "torch.cuda.is_available()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vXAr_sdxFSKm"
+      },
+      "source": [
+        "# Загрузим данные\n",
+        "\n",
+        "В соответствии с вариантом"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Krp456VbE7yP",
+        "outputId": "bd470eb1-d3e8-4059-a5cb-f24831c3d9dd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-04-05 17:50:23--  http://uit.mpei.ru/git/main/TDA/raw/branch/master/assets/poems/pushkin.txt\n",
+            "Resolving uit.mpei.ru (uit.mpei.ru)... 193.233.68.149\n",
+            "Connecting to uit.mpei.ru (uit.mpei.ru)|193.233.68.149|:80... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 1048627 (1.0M) [text/plain]\n",
+            "Saving to: ‘poems.txt’\n",
+            "\n",
+            "poems.txt           100%[===================>]   1.00M   625KB/s    in 1.6s    \n",
+            "\n",
+            "2024-04-05 17:50:25 (625 KB/s) - ‘poems.txt’ saved [1048627/1048627]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget -O poems.txt http://uit.mpei.ru/git/main/TDA/raw/branch/master/assets/poems/pushkin.txt\n",
+        "\n",
+        "# Маяковский: http://uit.mpei.ru/git/main/TDA/src/branch/master/assets/poems/mayakovskiy.txt\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BFnX81iNFFEf",
+        "outputId": "f2efb98f-d09e-49d6-f8b4-2952ae48c075"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Количество стихов: 720\n",
+            "\n",
+            "Пример стиха:\n",
+            "\n",
+            "Возможно ль? вместо роз, Амуром насажденных,\n",
+            "Тюльпанов гордо наклоненных,\n",
+            "Душистых ландышей, ясминов и лилей,\n",
+            "Которых ты всегда любила\n",
+            "И прежде всякой день носила\n",
+            "На мраморной груди твоей —\n",
+            "Возможно ль, милая Климена,\n",
+            "Какая странная во вкусе перемена!..\n",
+            "Ты любишь обонять не утренний цветок,\n",
+            "А вредную траву зелену,\n",
+            "Искусством превращенну\n",
+            "В пушистый порошок! —\n",
+            "Пускай уже седой профессор Геттингена,\n",
+            "На старой кафедре согнувшися дугой,\n",
+            "Вперив в латинщину глубокой разум свой,\n",
+            "Раскашлявшись, табак толченый\n",
+            "Пихает в длинный нос иссохшею рукой;\n",
+            "Пускай младой драгун усатый\n",
+            "Поутру, сидя у окна,\n",
+            "С остатком утреннего сна,\n",
+            "Из трубки пенковой дым гонит сероватый;\n",
+            "Пускай красавица шестидесяти лет,\n",
+            "У Граций в отпуску, и у любви в отставке,\n",
+            "Которой держится вся прелесть на подставке,\n",
+            "Которой без морщин на теле места нет,\n",
+            "Злословит, молится, зевает\n",
+            "И с верным табаком печали забывает, —\n",
+            "А ты, прелестная!.. но если уж табак\n",
+            "Так нравится тебе – о пыл воображенья! —\n",
+            "Ах! если, превращенный в прах,\n",
+            "И в табакерке, в заточеньи,\n",
+            "Я в персты нежные твои попасться мог,\n",
+            "Тогда б в сердечном восхищеньи\n",
+            "Рассыпался на грудь под шелковый платок\n",
+            "И даже… может быть… Но что! мечта пустая.\n",
+            "Не будет этого никак.\n",
+            "Судьба завистливая, злая!\n",
+            "Ах, отчего я не табак!..\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Загружаем текст из файла.\n",
+        "# Стихотворения в файле разделены токеном '</s>'\n",
+        "\n",
+        "with open(\"poems.txt\") as file:\n",
+        "  data = file.read().split(\"</s>\\n\\n\")\n",
+        "print(f\"Количество стихов: {len(data)}\\n\", f\"Пример стиха:\\n\\n{data[10]}\",  sep=\"\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "urxR6VN2FbVl"
+      },
+      "source": [
+        "# Подготовка данных и сводные статистики"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OhWos8xuFZZj",
+        "outputId": "635e93d6-6614-4c2c-c6c0-b0c7e70ea6ae"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Количество уникальных символов: 143\n",
+            "{0: 'PAD', 1: 'EOS', 2: 'a', 3: 'g', 4: ';', 5: 'R', 6: 'у', 7: 'И', 8: 'д', 9: 'ю', 10: 'V', 11: '?', 12: 'd', 13: 'з', 14: 'ы', 15: '–', 16: '(', 17: 'H', 18: 'Г', 19: ':', 20: 'm', 21: 'é', 22: 'ж', 23: 'c', 24: 'ц', 25: 'l', 26: 'Ф', 27: 'ф', 28: 'â', 29: 'п', 30: 'b', 31: 'г', 32: 'k', 33: 'B', 34: 'S', 35: \"'\", 36: 'z', 37: 'р', 38: 'ъ', 39: 'ь', 40: '!', 41: '\\n', 42: 'й', 43: 'Б', 44: '\"', 45: 'н', 46: '_', 47: 'P', 48: 'к', 49: 'F', 50: '»', 51: '*', 52: '—', 53: 'Ц', 54: 'L', 55: 'ê', 56: 'щ', 57: ')', 58: 's', 59: 'y', 60: 'С', 61: 'Ш', 62: 'Р', 63: 'э', 64: 'i', 65: 'x', 66: 'У', 67: 'è', 68: 'à', 69: 'p', 70: 'л', 71: 'T', 72: 'I', 73: 'û', 74: 'в', 75: '„', 76: 'Z', 77: 'П', 78: 'ё', 79: 'Л', 80: 'ш', 81: 'М', 82: '…', 83: '-', 84: 'З', 85: 'n', 86: '.', 87: 'В', 88: 'х', 89: 'с', 90: 'Ю', 91: 'C', 92: ' ', 93: 'е', 94: 'j', 95: 'Х', 96: 'а', 97: 'Н', 98: 'Д', 99: 'M', 100: 'и', 101: ',', 102: 'б', 103: '<', 104: '>', 105: 'А', 106: 'Т', 107: 'N', 108: 'о', 109: '«', 110: '\\xa0', 111: 'o', 112: 'Й', 113: 'Q', 114: 'U', 115: 'W', 116: 'ç', 117: 'т', 118: 'Е', 119: 'O', 120: 'О', 121: 'ч', 122: 'e', 123: 'u', 124: 'f', 125: 'D', 126: 'E', 127: 'К', 128: 'v', 129: 'Ж', 130: 'Щ', 131: 'м', 132: 'A', 133: 'Ч', 134: 'h', 135: 'Я', 136: 'ô', 137: 'J', 138: 't', 139: 'я', 140: 'r', 141: 'q', 142: 'Э'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Составляем словарь уникальных токенов\n",
+        "vocab = [\"PAD\", \"EOS\",] + list(set(\"\".join(data))) #список уникальных символов.\n",
+        "\n",
+        "# Формируем два словаря, реализующие перевод символов в их индексы и обратно\n",
+        "id2char = dict(enumerate(vocab)) #словарь индексов в символы\n",
+        "char2id = {char: ind for ind, char in id2char.items()} #словарь символов в индексы\n",
+        "print(f\"Количество уникальных символов: {len(vocab)}\", id2char, sep=\"\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xeTO4fBQFfBS"
+      },
+      "source": [
+        "Рассмотрим длины текстов"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "J-u-IxOeFZXY",
+        "outputId": "3ac61929-c08f-447f-df37-0f65030d3a57"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Максимальная длина текста:  8948\n"
+          ]
+        }
+      ],
+      "source": [
+        "lengths = list(map(len, data))\n",
+        "print(\"Максимальная длина текста: \", max(lengths))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "Ym7S8tmNFZUg",
+        "outputId": "67470c7b-ad81-4445-85d0-d3b1a4621e33"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.rcParams[\"figure.figsize\"] = (14, 6)\n",
+        "plt.hist(lengths, bins=30, range=[0, 6000])\n",
+        "plt.xlabel(\"Длина текста в символах\")\n",
+        "plt.title(\"Гистограмма длин текстов\")\n",
+        "plt.grid()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JuD6onVvFZR8",
+        "outputId": "49d24414-289e-4775-ac16-2f33f8873ac1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Средняя длина 808.9125\n",
+            "Медиана длины 453.5\n",
+            "Мода длины 221\n"
+          ]
+        }
+      ],
+      "source": [
+        "lengths = np.array(lengths)\n",
+        "print(\"Средняя длина\", np.mean(lengths))\n",
+        "print(\"Медиана длины\", np.median(lengths))\n",
+        "print(\"Мода длины\", np.bincount(lengths).argmax())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Вопрос 1\n",
+        "Где можно будет использовать знание о параметрах распределения длин в выборке?"
+      ],
+      "metadata": {
+        "id": "wjXxroS0IDXu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zfkSHd-8FlE1"
+      },
+      "source": [
+        "Выберите длину для генерации"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "y-Q7-EPuFZO5"
+      },
+      "outputs": [],
+      "source": [
+        "# Устанавливаем, сколько символов будет генерировать модель (максимальная длина генерируемого текста)\n",
+        "MAXLEN = 512 #<your choice here>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TSfv4n08Fpiv"
+      },
+      "source": [
+        "# Преобразование данных"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vtelHtjyFq1N"
+      },
+      "source": [
+        "Создадим функцию для преобразования текста в вектора одинакового размера для подачи в нейросеть. В этой функции добавляется токен EOS - конец последовательности. Если текст короче заданной длины, то добавляется специальный токен PAD. Если текст больше заданной длины, то он обрезается."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "Hf9iaFRKJUGq"
+      },
+      "outputs": [],
+      "source": [
+        "line_ix = [char2id[c] for c in data[5][:512]]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "2emYDlcVJnwi"
+      },
+      "outputs": [],
+      "source": [
+        "data_ix = np.zeros([len(data[5]), 512], 'int64')\n",
+        "data_ix[5, :len(line_ix)] = line_ix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QA5lQz2eJ8Oq",
+        "outputId": "e5df533b-ff5e-494a-d6ae-b2e8e4ffd442"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0 0 0 ... 0 0 0]\n"
+          ]
+        }
+      ],
+      "source": [
+        "data_ix= np.transpose(data_ix)\n",
+        "print(data_ix[5])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "YYGc7vo2FoTw"
+      },
+      "outputs": [],
+      "source": [
+        "def to_matrix(data, char2id, max_len=None, dtype='int64', batch_first = True):\n",
+        "\n",
+        "    max_len = max_len if max_len else max(map(len, data))\n",
+        "    data = [text[:max_len] + \" EOS\" for text in data]\n",
+        "    data_ix = np.zeros([len(data), max_len], dtype)\n",
+        "\n",
+        "    for i in range(len(data)):\n",
+        "        line_ix = [char2id[c] for c in data[i][:max_len]]\n",
+        "        data_ix[i, :len(line_ix)] = line_ix\n",
+        "\n",
+        "    if not batch_first: # convert [batch, time] into [time, batch]\n",
+        "        data_ix = np.transpose(data_ix)\n",
+        "\n",
+        "    return data_ix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DbkCcqotFoRP",
+        "outputId": "97e86d8c-26f4-407a-c587-58d0df5867cd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Исходный текст:\n",
+            " Так и мне узнать случилось,\n",
+            "Что за птица Купидон;\n",
+            "Сердце страстное пленилось;\n",
+            "Признаюсь – и я влюблен!\n",
+            "Пролетело счастья время,\n",
+            "Как, любви не зная бремя,\n",
+            "Я живал да попевал,\n",
+            "Как в театре и на балах,\n",
+            "На гуляньях иль в воксалах\n",
+            "Легким зефиром летал;\n",
+            "Как, смеясь во зло Амуру,\n",
+            "Я писал карикатуру\n",
+            "На любезный женской пол;\n",
+            "Но напрасно я смеялся,\n",
+            "Наконец и сам попался,\n",
+            "Сам, увы! с ума сошел.\n",
+            "Смехи, вольность – всё под лавку\n",
+            "Из Катонов я в отставку,\n",
+            "И теперь я – Селадон!\n",
+            "Миловидной жрицы Тальи\n",
+            "Видел прелести Натальи,\n",
+            "И уж в сердце – Купидон!\n",
+            "\n",
+            "Так, Наталья! признаюся,\n",
+            "Я тобою полонен,\n",
+            "В первый раз еще, стыжуся,\n",
+            "В женски прелести влюблен.\n",
+            "Целый день, как ни верчуся\n",
+            "Лишь тобою занят я;\n",
+            "Ночь придет – и лишь тебя\n",
+            "Вижу я в пустом мечтаньи,\n",
+            "Вижу, в легком одеяньи\n",
+            "Будто милая со мной;\n",
+            "Робко, сладостно дыханье,\n",
+            "Белой груди колебанье,\n",
+            "Снег затмивший белизной,\n",
+            "И полуотверсты очи,\n",
+            "Скромный мрак безмолвной ночи —\n",
+            "Дух в восторг приводят мой!..\n",
+            "Я один в беседке с нею,\n",
+            "Вижу… девственну лилею,\n",
+            "Трепещу, томлюсь, немею…\n",
+            "И проснулся… вижу мрак\n",
+            "Вкруг постели одинокой!\n",
+            "Испускаю вздох глубокой,\n",
+            "Сон ленивый, томноокой\n",
+            "Отлетает на крылах.\n",
+            "Страсть сильнее становится\n",
+            "И, любовью утомясь,\n",
+            "Я слабею всякой час.\n",
+            "Всё к чему-то ум стремится,\n",
+            "А к чему? – никто из нас\n",
+            "Дамам в слух того не скажет,\n",
+            "А уж так и сяк размажет.\n",
+            "Я – по-свойски объяснюсь.\n",
+            "\n",
+            "Все любовники желают\n",
+            "И того, чего не знают;\n",
+            "Это свойство их – дивлюсь!\n",
+            "Завернувшись балахоном,\n",
+            "С хватской шапкой на бекрень\n",
+            "Я желал бы Филимоном\n",
+            "Под вечер, как всюду тень,\n",
+            "Взяв Анюты нежну руку,\n",
+            "Изъяснять любовну муку,\n",
+            "Говорить: она моя!\n",
+            "Я желал бы, чтоб Назорой\n",
+            "Ты старалася меня\n",
+            "Удержать умильным взором.\n",
+            "Иль седым Опекуном\n",
+            "Легкой, миленькой Розины,\n",
+            "Старым пасынком судьбины,\n",
+            "В епанче и с париком,\n",
+            "Дерзкой пламенной рукою\n",
+            "Белоснежну, полну грудь…\n",
+            "Я желал бы… да ногою\n",
+            "Моря не перешагнуть.\n",
+            "И, хоть по уши влюбленный,\n",
+            "Но с тобою разлученный,\n",
+            "Всей надежды я лишен.\n",
+            "\n",
+            "Но, Наталья! ты не знаешь\n",
+            "Кто твой нежный Селадон,\n",
+            "Ты еще не понимаешь,\n",
+            "Отчего не смеет он\n",
+            "И надеяться? – Наталья!\n",
+            "Выслушай еще меня:\n",
+            "\n",
+            "Не владетель я Сераля,\n",
+            "Не арап, не турок я.\n",
+            "За учтивого китайца,\n",
+            "Грубого американца\n",
+            "Почитать меня нельзя,\n",
+            "Не представь и немчурою,\n",
+            "С колпаком на волосах,\n",
+            "С кружкой, пивом налитою,\n",
+            "И с цыгаркою в зубах.\n",
+            "Не представь кавалергарда\n",
+            "В каске, с длинным палашом.\n",
+            "Не люблю я бранный гром:\n",
+            "Шпага, сабля, алебарда\n",
+            "Не тягчат моей руки\n",
+            "За Адамовы грехи.\n",
+            "\n",
+            "– Кто же ты, болтун влюбленный?\n",
+            "Взглянь на стены возвышенны,\n",
+            "Где безмолвья вечный мрак;\n",
+            "Взглянь на окны загражденны,\n",
+            "На лампады там зажженны…\n",
+            "Знай, Наталья! – я… монах!\n",
+            "\n",
+            "\n",
+            "Преобразованный текст:\n",
+            " [106  96  48  92 100  92 131  45  93  92   6  13  45  96 117  39  92  89\n",
+            "  70   6 121 100  70 108  89  39 101  41 133 117 108  92  13  96  92  29\n",
+            " 117 100  24  96  92 127   6  29 100   8 108  45   4  41  60  93  37   8\n",
+            "  24  93  92  89 117  37  96  89 117  45 108  93  92  29  70  93  45 100\n",
+            "  70 108  89  39   4  41  77  37 100  13  45  96   9  89  39  92  15  92\n",
+            " 100  92 139  92  74  70   9 102  70  93  45  40  41  77  37 108  70  93\n",
+            " 117  93  70 108  92  89 121  96  89 117  39 139  92  74  37  93 131 139\n",
+            " 101  41 127  96  48 101  92  70   9 102  74 100  92  45  93  92  13  45\n",
+            "  96 139  92 102  37  93 131 139 101  41 135  92  22 100  74  96  70  92\n",
+            "   8  96  92  29 108  29  93  74  96  70 101  41 127  96  48  92  74  92\n",
+            " 117  93  96 117  37  93  92 100  92  45  96  92 102  96  70  96  88 101\n",
+            "  41  97  96  92  31   6  70 139  45  39 139  88  92 100  70  39  92  74\n",
+            "  92  74 108  48  89  96  70  96  88  41  79  93  31  48 100 131  92  13\n",
+            "  93  27 100  37 108 131  92  70  93 117  96  70   4  41 127  96  48 101\n",
+            "  92  89 131  93 139  89  39  92  74 108  92  13  70 108  92 105 131   6\n",
+            "  37   6 101  41 135  92  29 100  89  96  70  92  48  96  37 100  48  96\n",
+            " 117   6  37   6  41  97  96  92  70   9 102  93  13  45  14  42  92  22\n",
+            "  93  45  89  48 108  42  92  29 108  70   4  41  97 108  92  45  96  29\n",
+            "  37  96  89  45 108  92 139  92  89 131  93 139  70  89 139 101  41  97\n",
+            "  96  48 108  45  93  24  92 100  92  89  96 131  92  29 108  29  96  70\n",
+            "  89 139 101  41  60  96 131 101  92   6  74  14  40  92  89  92   6 131\n",
+            "  96  92  89 108  80  93  70  86  41  60 131  93  88 100 101  92  74 108\n",
+            "  70  39  45 108  89 117  39  92  15  92  74  89  78  92  29 108   8  92\n",
+            "  70  96  74  48   6  41   7  13  92 127  96 117 108  45 108  74  92 139\n",
+            "  92  74  92 108 117  89 117  96  74  48   6 101  41   7  92 117  93  29\n",
+            "  93  37  39  92 139  92  15  92  60  93  70  96   8 108  45  40  41  81\n",
+            " 100  70 108  74 100   8  45 108  42  92  22  37 100  24  14  92 106  96\n",
+            "  70  39 100  41  87 100   8  93  70  92  29  37  93  70  93  89 117 100\n",
+            "  92  97  96 117  96  70  39 100]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Проверяем работу функции - кодируем один из текстов и смотрим как он выглядит в кодированном виде\n",
+        "encode = to_matrix(data[:1], char2id, MAXLEN)\n",
+        "print(\"Исходный текст:\\n\", data[0])\n",
+        "print(\"Преобразованный текст:\\n\", encode[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Вопрос 2\n",
+        "Пояснить, что хранится в переменной `encode`.\n",
+        "\n",
+        "Как будет выглядеть ваша фамилия в кодированном виде?"
+      ],
+      "metadata": {
+        "id": "nSrGA_gLLh3A"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eQ-s7rzHFu9T"
+      },
+      "source": [
+        "# Подготовка нейросети"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c3qNOFx8Fwec"
+      },
+      "source": [
+        "![](https://raw.githubusercontent.com/tensorflow/text/master/docs/tutorials/images/text_generation_training.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "atZoEO6VFyQf"
+      },
+      "source": [
+        "Исходя из архитектуры нейросети на картинке, нужно задать 3 слоя:\n",
+        "  - Embedding\n",
+        "  - GRU\n",
+        "  - Dense\n",
+        "\n",
+        "\n",
+        "Для этого воспользуемся pytorch, ссылки на подробную документацию каждого слоя:\n",
+        "  - [nn.Embedding](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html)\n",
+        "  - [nn.GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html)\n",
+        "  - [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html)  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "3rAXL2-QFvM9"
+      },
+      "outputs": [],
+      "source": [
+        "num_embeddings = len(vocab) #количество эмбеддингов должно соответствовать длине словаря\n",
+        "embedding_dim = 32 #определяется размерность эмбеддинга\n",
+        "emb = nn.Embedding(num_embeddings, embedding_dim) # Определяем объект emb как слой эмбеддингов заданного размера"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "num_embeddings"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mGb6aUqfOmav",
+        "outputId": "ec17500b-0522-4c56-a6f9-0a5da11f7346"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "143"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Вопрос 3\n",
+        "Почему количество эмбеддингов должно соответствовать длине словаря?"
+      ],
+      "metadata": {
+        "id": "esvotgUlMADX"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hHK3n4noF2OP"
+      },
+      "source": [
+        "В качестве примера пропустим через этот слой первые 5 букв первого текста."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GvwHFBOWFz-U",
+        "outputId": "0a58cf39-3151-48d5-846c-6f55d1dfc863"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Размер тензора: torch.Size([5, 32])\n",
+            "\n",
+            "tensor([[ 1.5029e+00, -1.7916e+00,  2.2026e+00, -2.2513e+00, -6.9742e-01,\n",
+            "          3.0790e-01,  6.8104e-01,  7.7460e-01, -6.1624e-01,  1.5153e+00,\n",
+            "         -4.1424e-01, -3.7886e-01,  3.0690e-01,  2.3166e-04, -1.3926e-01,\n",
+            "         -4.1106e-01, -3.6484e-01,  3.3451e-01, -3.3457e+00, -6.2611e-01,\n",
+            "          8.0940e-01,  8.7477e-01, -9.3242e-02,  1.0553e+00,  1.0860e+00,\n",
+            "         -4.8333e-01,  1.1123e+00,  6.2818e-01,  2.5269e-01,  1.3213e+00,\n",
+            "          5.3855e-01,  4.6009e-01],\n",
+            "        [ 5.7508e-01,  1.6589e+00, -1.1679e+00, -2.4309e+00, -3.9557e-01,\n",
+            "          3.8376e-01, -9.8238e-01, -1.1509e+00,  2.2251e+00, -4.6321e-01,\n",
+            "          6.5630e-01, -1.9887e+00, -6.1972e-01, -1.6304e-01, -9.7050e-01,\n",
+            "         -2.5951e-01,  4.9236e-01, -1.9583e+00,  1.9546e+00, -1.0485e+00,\n",
+            "          1.2174e-01,  8.9455e-01, -2.3779e+00,  2.3129e+00, -2.7838e-01,\n",
+            "          9.1432e-01, -2.7290e+00, -1.1626e+00, -8.9565e-01, -8.5091e-03,\n",
+            "          8.8071e-01, -9.9323e-03],\n",
+            "        [-8.4068e-01, -6.1268e-01,  6.7328e-01, -9.6503e-01,  6.8494e-01,\n",
+            "         -8.4349e-01,  3.5604e-01,  1.7478e+00, -1.7844e-01, -7.7445e-01,\n",
+            "          1.3449e+00,  1.1115e+00,  1.1597e+00,  7.3386e-01,  9.6546e-01,\n",
+            "         -1.7147e+00, -3.4931e-01,  1.7341e-01,  3.5583e-01, -2.5318e-01,\n",
+            "         -4.7672e-01, -1.4090e-02, -5.9210e-01,  5.4945e-01,  1.6417e-01,\n",
+            "         -4.4186e-01,  7.3914e-01,  1.8834e+00,  3.0256e-01,  6.1556e-01,\n",
+            "         -9.7063e-01,  4.4972e-01],\n",
+            "        [ 5.6335e-01,  1.9862e-01,  3.7290e-01,  9.5874e-01,  6.3197e-01,\n",
+            "          2.9606e-01,  1.6983e+00, -6.7355e-01, -3.7383e-01, -1.0147e+00,\n",
+            "         -1.2620e-02,  6.6465e-01,  9.1266e-01,  1.2363e+00,  1.9966e+00,\n",
+            "         -1.1470e+00, -5.4097e-01,  1.3002e+00, -1.4012e+00, -8.1303e-01,\n",
+            "         -7.5828e-01,  2.8108e-01,  1.0428e+00,  4.5049e-01, -1.2042e-01,\n",
+            "         -1.2361e+00,  2.9283e-01, -1.6573e+00,  3.8987e-01, -2.1059e-01,\n",
+            "          1.4592e-01, -2.3350e-01],\n",
+            "        [ 7.5370e-01, -1.6429e+00, -8.4913e-01,  3.6310e-02,  1.4648e+00,\n",
+            "         -9.0935e-01, -2.1241e-01, -3.0972e-01,  6.5774e-01,  6.8460e-01,\n",
+            "          4.4648e-01,  6.2579e-01,  7.1138e-01,  1.8680e+00, -1.3650e+00,\n",
+            "          1.2340e-02, -1.2517e-01,  3.1195e-01,  3.0871e-01,  5.0507e-01,\n",
+            "         -1.2373e+00, -1.0363e+00, -4.4852e-01, -8.9384e-01, -4.5717e-01,\n",
+            "          1.0538e-01,  4.1036e-01,  2.9193e+00, -1.1410e+00,  8.8568e-01,\n",
+            "         -3.3892e-01, -7.0943e-01]], grad_fn=<EmbeddingBackward0>)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# emb_out - 5 первых символов в виде эмбедингов\n",
+        "emb_out = emb(torch.tensor(encode[0][:5]))\n",
+        "print(f\"Размер тензора: {emb_out.shape}\\n\")\n",
+        "print(emb_out)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Вопрос 4\n",
+        "Пояснить, почему получен такой размер `emb_out`?"
+      ],
+      "metadata": {
+        "id": "-YOv6t-qMOQi"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nkS5ZQ8fF4XP"
+      },
+      "source": [
+        "# Создадим ячейку GRU"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Вопрос 5\n",
+        "Обратиться к документации к [nn.GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html) и ответить на вопрос, за что отчечают параметры `input_size`, `hidden_size`, `num_layers`.\n",
+        "\n",
+        "В следующей ячейке задать значения для этих параметров"
+      ],
+      "metadata": {
+        "id": "ZBfkiOYmM9yr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5bJ7fRAYFz71",
+        "outputId": "08fe5314-c7fb-4901-9947-4eabbd4c2326"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Размер output: torch.Size([5, 64])\n",
+            "Размер h_n: torch.Size([1, 64])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Определяем ячейку GRU в переменной gru, предварительно задав рамерность скрытого состояния и количество рекуррентных слоев\n",
+        "input_size = emb.embedding_dim # вход в GRU должен соответствовать размеру эмбеддинга\n",
+        "hidden_size = 64 #<your choice here>\n",
+        "num_layers = 1 #<your choice here>\n",
+        "gru = nn.GRU(input_size, hidden_size, num_layers)\n",
+        "output, h_n = gru(emb_out)\n",
+        "print(\"Размер output:\", output.shape)\n",
+        "print(\"Размер h_n:\", h_n.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4W5Q3O45F6wh"
+      },
+      "source": [
+        "Выходом GRU является 2 тензора:\n",
+        "  - output (используется для классификации)\n",
+        "  - тензор скрытого состояния h_n (используается для последующей передачи во времени)\n",
+        "\n",
+        "Теперь используем output для предсказания следующей буквы, пропустив через линейный слой"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "p16NvYn2Fz4-",
+        "outputId": "a31d5f60-dfe0-4a7e-c078-ee7700750aa6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Размер выходного слоя из нейросети:  torch.Size([5, 143])\n"
+          ]
+        }
+      ],
+      "source": [
+        "in_features = gru.hidden_size\n",
+        "out_features = len(vocab) #предсказываем букву из всего словаря\n",
+        "linear = nn.Linear(in_features, out_features) # Определяем линейный слой. Почему заданы такие входные и выходные параметры для него?\n",
+        "linear_out = linear(output) # output - выход GRU\n",
+        "print(\"Размер выходного слоя из нейросети: \", linear_out.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Вопрос 6\n",
+        "Что содержится в векторе linear_out?\n",
+        "\n",
+        "Определить индекс символа, который наиболее вероятно выдаст ячейка GRU на первом шаге?"
+      ],
+      "metadata": {
+        "id": "6no89JxCNwvX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z2lThHhjOBI2",
+        "outputId": "50a18b7f-094d-41e1-a728-c47ef9561474"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "tensor([ 0.0909, -0.0186, -0.2154, -0.2624, -0.0282, -0.1714, -0.0423,  0.1505,\n",
+              "         0.0645,  0.0452, -0.0173, -0.0822,  0.1414,  0.1803, -0.2480, -0.1756,\n",
+              "        -0.0447, -0.0136,  0.1097, -0.2245,  0.0189, -0.0559,  0.0215, -0.0040,\n",
+              "         0.0454,  0.2437, -0.2015,  0.0818,  0.2883,  0.2263, -0.0109,  0.1681,\n",
+              "        -0.0642, -0.0497,  0.1583, -0.0795, -0.0376,  0.0674, -0.1307,  0.0640,\n",
+              "         0.0966,  0.0140, -0.1366, -0.1685,  0.0397, -0.1600, -0.0497, -0.2244,\n",
+              "        -0.1075, -0.1048,  0.0275, -0.0144, -0.1456,  0.0394, -0.1426, -0.0605,\n",
+              "        -0.0743,  0.1303,  0.0337,  0.1010, -0.0651, -0.1233,  0.0282,  0.2729,\n",
+              "         0.0485, -0.0911,  0.1994,  0.0610,  0.0911,  0.0940, -0.0973,  0.0747,\n",
+              "         0.0031,  0.1500, -0.0295,  0.0127, -0.2865, -0.0251, -0.1395,  0.1547,\n",
+              "         0.0830,  0.0782, -0.0181,  0.1317, -0.0537,  0.1293, -0.2458,  0.1477,\n",
+              "        -0.0610, -0.0592, -0.2878, -0.1622,  0.1336,  0.0987, -0.0834, -0.0815,\n",
+              "        -0.1764,  0.0202, -0.0097,  0.0176, -0.1874,  0.0716, -0.1070, -0.1364,\n",
+              "         0.1748,  0.1479, -0.0478,  0.0862,  0.0648,  0.1126, -0.0995, -0.1324,\n",
+              "         0.1023, -0.0247,  0.0439,  0.0212, -0.0608,  0.0402,  0.1468,  0.2311,\n",
+              "        -0.1142,  0.2198, -0.1181, -0.2196, -0.0653,  0.0870, -0.3388,  0.1208,\n",
+              "         0.0851,  0.0590,  0.1963,  0.0710,  0.1099,  0.1648,  0.0260, -0.1451,\n",
+              "         0.0012, -0.0973, -0.1058, -0.0628,  0.0585,  0.1859, -0.0573],\n",
+              "       grad_fn=<SelectBackward0>)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "source": [
+        "linear_out[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NZnJy28VF894"
+      },
+      "source": [
+        "Теперь определим класс со всеми частями:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "1GinxRWgF9KG"
+      },
+      "outputs": [],
+      "source": [
+        "class CharGRULoop(nn.Module): # Описываем инициализатор класса.\n",
+        "    def __init__(self, num_embeddings=52, embedding_dim=16, hidden_size=64, num_layers=1): # В методе __init__ определим архитектуру модели, создав необходимые слои\n",
+        "        super(self.__class__, self).__init__()\n",
+        "        self.emb = nn.Embedding(num_embeddings, embedding_dim)\n",
+        "        self.gru = nn.GRU(embedding_dim, hidden_size, num_layers, batch_first=True)\n",
+        "        self.hid_to_logits = nn.Linear(hidden_size, num_embeddings)\n",
+        "\n",
+        "    def forward(self, x, hid_state): # Здесь описываем стурктуру сети - как сигнал должен по ней проходить\n",
+        "        x = self.emb(x) # Проходим через эмбеддинг-слой\n",
+        "        if hid_state is not None: # Проходим через GRU, сохраняя hidden state\n",
+        "            h_seq, hid_state = self.gru(x, hid_state)\n",
+        "        else:\n",
+        "            h_seq, hid_state = self.gru(x)\n",
+        "        next_logits = self.hid_to_logits(h_seq) # проходим через полносвязный слой\n",
+        "        next_logp = F.log_softmax(next_logits, dim=-1) # Используем функцию softmax\n",
+        "        return next_logp, hid_state"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1aW5mneXF_cy"
+      },
+      "source": [
+        "Определим модель:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "_U6Q0oosF_o7"
+      },
+      "outputs": [],
+      "source": [
+        "model = CharGRULoop(num_embeddings=len(vocab), embedding_dim=64, hidden_size=192, num_layers=2)\n",
+        "#<your choice here> Можно попробовать создать сеть с другими значениями параметров"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nnS2cn8YGBPw"
+      },
+      "source": [
+        "Количество  параметров:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "zp7I2GVIGCOw"
+      },
+      "outputs": [],
+      "source": [
+        "!pip -q install torchinfo"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "24-GKj6nGDc4",
+        "outputId": "8bc88d9d-0105-4ad6-ab6a-5a90c5d2a4bf"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "=================================================================\n",
+              "Layer (type:depth-idx)                   Param #\n",
+              "=================================================================\n",
+              "CharGRULoop                              --\n",
+              "├─Embedding: 1-1                         9,152\n",
+              "├─GRU: 1-2                               370,944\n",
+              "├─Linear: 1-3                            27,599\n",
+              "=================================================================\n",
+              "Total params: 407,695\n",
+              "Trainable params: 407,695\n",
+              "Non-trainable params: 0\n",
+              "================================================================="
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ],
+      "source": [
+        "from torchinfo import summary\n",
+        "\n",
+        "summary(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Каждый раз, когда вы вызываете модель (forward), вы передаете некоторый текст и внутреннее состояние. Модель возвращает прогноз для следующего символа и его нового состояния.\n",
+        "\n",
+        "![image.png](data:image/png;base64,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)"
+      ],
+      "metadata": {
+        "id": "gIEsZBJbUNY5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "afVs3q7tGFPO"
+      },
+      "source": [
+        "Функция для генерации последовательности символов (текста)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "meZeWXwUGFss"
+      },
+      "outputs": [],
+      "source": [
+        "def generate_sample(model, char2id, id2char, seed_phrase=' ', strategy=\"greedy\", max_length=100, temperature=1.0):\n",
+        "    \"\"\"\n",
+        "      model - нейросеть\n",
+        "      char2id - словарь преобразования букв в их индексы\n",
+        "      id2char - словарь преобразования индексов в буквы\n",
+        "      seed_phrase - начальная фраза для генерации\n",
+        "      strategy - стратегия генерации (жадная \"greedy\" или сэмплирование \"sample\")\n",
+        "      max_length - максимальная длина сгенирированного текста\n",
+        "      temperature - ???\n",
+        "    \"\"\"\n",
+        "\n",
+        "    x_sequence = [char2id[token] for token in seed_phrase] # кодируем начальную фразу\n",
+        "    x_sequence = torch.tensor([x_sequence], dtype=torch.int64) # создаем тензор\n",
+        "    hid_state = None # задаем тензор скрытого состояния h_n, при такой подачи вектор заполнится нулями\n",
+        "\n",
+        "    with torch.no_grad(): # отключаем подсчет градиентов, поскольку сеть уже обучена и не нужно проводить обратное распространение ошибки\n",
+        "      for i in range(len(seed_phrase) - 1): # подаем номер буквы и hid_state в цикле\n",
+        "          _, hid_state = model(x_sequence[:, i].unsqueeze(0), hid_state)\n",
+        "\n",
+        "      # начинаем генерацию\n",
+        "      for _ in range(max_length - len(seed_phrase)):\n",
+        "\n",
+        "          logp_next, hid_state = model(x_sequence[:, -1].unsqueeze(0), hid_state) # подаем последнюю букву из фразы\n",
+        "          p_next = F.softmax(logp_next / temperature, dim=-1).data.numpy()[0] # нормируем выходы модели на температуру и применяем софтмакс\n",
+        "\n",
+        "          if strategy == \"greedy\": next_ix = p_next.argmax() #берем токен с максимальной вероятностью\n",
+        "          elif strategy == \"sample\": next_ix = np.random.choice(len(id2char), p=p_next[0]) #получаем следующий токен сэмплированием с вероятностями\n",
+        "          else: raise ValueError('Хулиган, не делай так! Выбери \"greedy\" или \"sample\"')\n",
+        "\n",
+        "          if id2char[next_ix] == \"EOS\": break # если получили токен EOS, то прекращаем генерацию\n",
+        "          else:\n",
+        "            next_ix = torch.tensor([[next_ix]], dtype=torch.int64) # создаем тензор следующий буквы\n",
+        "            x_sequence = torch.cat([x_sequence, next_ix], dim=1) # добавляем предсказанный токен в конец последовательности\n",
+        "\n",
+        "    return ''.join([id2char[ix] for ix in x_sequence.data.numpy()[0]]) # возвращаем декодированную строку"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IJohkcbJGG5O"
+      },
+      "source": [
+        "Попробуем что-нибудь сгенерировать:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Вопрос 7\n",
+        "Выполните следующую ячейку несколько раз с одной и той же SEED_PHRASE, запомните выводы модели и объясните результат - чем отличается стратегия greedy от sample?"
+      ],
+      "metadata": {
+        "id": "b2hqsv33P2gW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5bqp2xRjGIdW",
+        "outputId": "431d70ec-9bca-4765-aae6-3ae6e143fb11"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Я люблю машинное обучение!\n",
+            "ддZгàuгuuZsàCCCCОааа___IIшCCОаа!___IICйCCОаа!___IICйCCОаа!___IICйCCОаа!__\n",
+            "\n",
+            "Я люблю машинное обучение!\n",
+            "<eâSайб«.<К»èôOг:cq<ыэôuqьW!Пто'ryS«щаâЖmХàrûЯзVЛОZhJripиDUhLСасм…шп-ЙpCà\n"
+          ]
+        }
+      ],
+      "source": [
+        "SEED_PHRASE = 'Я люблю машинное обучение!\\n' # Ну или что-то другое\n",
+        "print(generate_sample(model, char2id, id2char, seed_phrase=SEED_PHRASE, strategy=\"greedy\", max_length=100))\n",
+        "print()\n",
+        "print(generate_sample(model, char2id, id2char, seed_phrase=SEED_PHRASE, strategy=\"sample\", max_length=100))\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zXrGUveMGKGV"
+      },
+      "source": [
+        "Разделим данные на тренировачные и тестовые, подготовим Dataloader для подачи в нейросеть"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "KNmsiFeXGLI4"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "batch = 128 # Тексты, для уменьшения накладных расходов, будем подавать на вход модели \"батчами\" - блоками по 128 документов\n",
+        "\n",
+        "train, test = train_test_split(data, test_size=0.2, random_state=42, shuffle=True)\n",
+        "\n",
+        "dataset_train = TensorDataset(torch.tensor(to_matrix(train, char2id, max_len=MAXLEN)))\n",
+        "dataloader_train = DataLoader(dataset_train, batch_size=batch, shuffle=True)\n",
+        "\n",
+        "dataset_test = TensorDataset(torch.tensor(to_matrix(test, char2id, max_len=MAXLEN)))\n",
+        "dataloader_test = DataLoader(dataset_test, batch_size=batch, shuffle=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kIOckRO1GMc6"
+      },
+      "source": [
+        "Переопределим модель, зададим оптимизатор, лосс-функцию"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1biBu39NGNbP",
+        "outputId": "036d1a20-4a71-4153-e467-c1f477c868fe"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "=================================================================\n",
+              "Layer (type:depth-idx)                   Param #\n",
+              "=================================================================\n",
+              "CharGRULoop                              --\n",
+              "├─Embedding: 1-1                         9,152\n",
+              "├─GRU: 1-2                               1,036,800\n",
+              "├─Linear: 1-3                            36,751\n",
+              "=================================================================\n",
+              "Total params: 1,082,703\n",
+              "Trainable params: 1,082,703\n",
+              "Non-trainable params: 0\n",
+              "================================================================="
+            ]
+          },
+          "metadata": {},
+          "execution_count": 50
+        }
+      ],
+      "source": [
+        "model = CharGRULoop(num_embeddings=len(vocab), embedding_dim=64, hidden_size=256, num_layers=3)\n",
+        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
+        "loss_fn = nn.NLLLoss(ignore_index=char2id[\"PAD\"])\n",
+        "# Функция потерь - negative log likelihood loss (NLL Loss) - используеся для задачи многоклассовой классификации в ситуации, когда имеются логарифмы вероятностей каждого класса\n",
+        "# NLL Loss эквивалентен CrossEntropyLoss, но применяется к логарифмам вероятностей - у нас на выходе линейного слоя как раз исопользуется функция softmax\n",
+        "\n",
+        "summary(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2nwqarvRGPY0"
+      },
+      "source": [
+        "Необученная модель не может делать адекватные предсказания. Ее перплексия («коэффициент неопределённости») приблизительно равна размеру словаря. Это говорит о полной неопределенности модели при генерации текста."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "A1bX1uqvU5hn",
+        "outputId": "0fe2cb45-a766-4960-9994-f7276e38ce06"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['Буря мглою небо кроет,\\nВихри снежные крутя:\\nТо, как зверь, она завоет,\\nТо заплачет, как дитя,\\nТо по кровле обветшалой\\nВдруг соломой зашумит,\\nТо, как путник запоздалый,\\nК нам в окошко застучит.\\n\\nНаша ветхая лачужка\\nИ печальна, и темна.\\nЧто же ты, моя старушка,\\nПриумолкла у окна?\\nИли бури завываньем\\nТы, мой друг, утомлена,\\nИли дремлешь под жужжаньем\\nСвоего веретена?\\n\\nВыпьем, добрая подружка\\nБедной юности моей,\\nВыпьем с горя; где же кружка?\\nСердцу будет веселей.\\nСпой мне песню, как синица\\nТихо за морем жила;\\nСпой мне песню, как девица\\nЗа водой поутру шла.\\n\\nБуря мглою небо кроет,\\nВихри снежные крутя;\\nТо, как зверь, она завоет.\\nТо заплачет, как дитя.\\nВыпьем, добрая подружка\\nБедной юности моей,\\nВыпьем с горя; где же кружка?\\nСердцу будет веселей.\\n\\n']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ],
+      "source": [
+        "train[:1]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1XcNzyHyGQTj",
+        "outputId": "14ff67ea-76c1-45ff-cb18-532d2aa0af6a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "perplexity:  141.7220001220703\n"
+          ]
+        }
+      ],
+      "source": [
+        "example = torch.tensor(to_matrix(train[:1], char2id, max_len=MAXLEN)) # Возьмем первый документ и попробуем сгененировать этот же текст начиная с первой буквы\n",
+        "\n",
+        "train_example = example[:, :-1]\n",
+        "target_example = example[:, 1:]\n",
+        "\n",
+        "next_logp, hid_state = model(train_example, hid_state=None)\n",
+        "\n",
+        "print('perplexity: ', torch.exp(loss_fn(next_logp.permute(0, 2, 1), target_example)).item())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "id": "P51iPqsVGReq"
+      },
+      "outputs": [],
+      "source": [
+        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+        "torch.cuda.empty_cache()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KiORWHsRGSiK"
+      },
+      "source": [
+        "Обучение. Эта ячейка займет 5-8 минут"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 522
+        },
+        "id": "naMiQGHHGTfS",
+        "outputId": "1c564592-0e54-4479-ccdf-260377e254dd"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "EPOCH = 200 # количество эпох обучения\n",
+        "history_train = [] # список значений лосса трейна на каждой эпохи\n",
+        "history_test = [] # список значений лосса теста на каждой эпохи\n",
+        "model.to(device) # И модель, и данные должны находиться на одном устройстве.\n",
+        "                 # Поэтому при работе с GPU нужно следить и явно указывать, на каком устройстве проводится работа.\n",
+        "\n",
+        "\n",
+        "best_test_loss = float(\"inf\")\n",
+        "for i in range(EPOCH): #цикл по эпохам\n",
+        "\n",
+        "  loss_test = 0\n",
+        "  loss_train = 0\n",
+        "\n",
+        "  for batch in dataloader_train: #цикл по тренировачным батчам\n",
+        "\n",
+        "    optimizer.zero_grad() #обнуляем градиенты\n",
+        "    batch_ix = torch.tensor(batch[0], dtype=torch.int64).to(device) #делаем из батча тензор\n",
+        "\n",
+        "    predictions_logp, _ = model(batch_ix[:, :-1], hid_state=None) #подаем батч в модель\n",
+        "\n",
+        "    actual_next_tokens = batch_ix[:, 1:] # таргеры\n",
+        "\n",
+        "    loss = loss_fn(predictions_logp.permute(0, 2, 1), actual_next_tokens.long()) # считаем лосс на батче\n",
+        "    loss_train += loss.item() # добавляем лосс с батча в суммарный лосс\n",
+        "\n",
+        "    loss.backward() # делаем обратный проход\n",
+        "    optimizer.step() # делаем шаг оптимизатором\n",
+        "\n",
+        "  history_train.append(loss_train/len(dataloader_train)) # добавляем  средний лосс за эпоху в список\n",
+        "\n",
+        "  for batch in dataloader_test: #цикл по тестовым батчам\n",
+        "    with torch.no_grad(): # отключаем подсчет градиентов\n",
+        "\n",
+        "      batch_ix = torch.tensor(batch[0], dtype=torch.int64).to(device)\n",
+        "      predictions_logp, _ = model(batch_ix[:, :-1], hid_state=None)\n",
+        "\n",
+        "      actual_next_tokens = batch_ix[:, 1:]\n",
+        "\n",
+        "      loss = loss_fn(predictions_logp.permute(0, 2, 1), actual_next_tokens.long())\n",
+        "      loss_test += loss.item()\n",
+        "\n",
+        "  loss_test = loss_test/len(dataloader_test)\n",
+        "  history_test.append(loss_test)\n",
+        "\n",
+        "  if loss_test < best_test_loss: #сохраняем лучшую модель по лоссу на тесте\n",
+        "    best_test_loss = loss_test\n",
+        "    best_model = copy.deepcopy(model)\n",
+        "    best_model.to(\"cpu\")\n",
+        "\n",
+        "  if (i + 1) % 5 == 0: # выводим график лосса каждые 5 эпох\n",
+        "    clear_output(True)\n",
+        "    plt.plot(history_train, label='loss_train')\n",
+        "    plt.plot(history_test, label='loss_test')\n",
+        "    plt.grid()\n",
+        "    plt.legend()\n",
+        "    plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Вопрос 8\n",
+        "Достаточно ли обучилась модель? Имеет ли смысл изменить количество эпох обучения?"
+      ],
+      "metadata": {
+        "id": "iWGz3oH3Rowq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "k924KVVaGU4t",
+        "outputId": "01db571f-54df-421e-cdf5-6c6c4ce7bf72"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "perplexity (best model test):  7.181648032772434\n",
+            "perplexity (last epoch test):  7.421088521940808\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"perplexity (best model test): \", np.exp(best_test_loss))\n",
+        "print(\"perplexity (last epoch test): \", np.exp(loss_test))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5fwRbhzDGVxl",
+        "outputId": "230be0e7-f04c-4c63-8576-2e2971a82b00"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "CharGRULoop(\n",
+              "  (emb): Embedding(143, 64)\n",
+              "  (gru): GRU(64, 256, num_layers=3, batch_first=True)\n",
+              "  (hid_to_logits): Linear(in_features=256, out_features=143, bias=True)\n",
+              ")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ],
+      "source": [
+        "device = 'cpu' # Обучение обычно проводится на GPU, но чтобы не тратить его ресурсы, работу по генерации текста уже обученной моделью стоит перенести обратно на CPU\n",
+        "model.to(device)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0PWo3arcGWvv"
+      },
+      "source": [
+        "Погенерируем текст. Сначала зададим стартовую фразу.\n",
+        "\n",
+        "Выполнив следующие ячейки по несколько раз, убедитесь, что правильно ответили на вопрос 7 \"Чем отличается стратегия greedy от sample?\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "id": "dtUW5hVBGX9p"
+      },
+      "outputs": [],
+      "source": [
+        "#Если вы не согласны, вы можете поменять стартовую фразу, но что думает об этом высказывание машина?\n",
+        "SEED_PHRASE = 'Теория автоматического управления - лучший предмет,'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Nr24uuQRGZqQ"
+      },
+      "source": [
+        "Sample strategy"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bY_iYC82GY28",
+        "outputId": "1a41d952-882b-451a-b321-f2066f03f255"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Теория автоматического управления - лучший предмет,\n",
+            "В вечерчят, может и ребких ряд,\n",
+            "Когда в тихает Наздним не рафстились взор,\n",
+            "Где боющей слус во знали цвю,\n",
+            "Еврожачи в пустыню твоей, прочей,\n",
+            "В Ажертрянный лес пропариду улещенье,\n",
+            "Истревняешь литее молви от меня за Eилей\n",
+            "В и ждох эти пыраматься спомнорой,\n",
+            "И вишел ее пеельчим на пытом.\n",
+            "\n",
+            "Зевители, моей пужко образ роды погоглавляек;\n",
+            "Скворный, сокружлен ускоре вином:\n",
+            "\n",
+            " EOS любится каз ним меня дороды!\n",
+            "Всях казы-тро ты сладо мною рошу.\n",
+            "Нет, и красота тебе прироща\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(generate_sample(best_model, char2id, id2char, seed_phrase=SEED_PHRASE, strategy=\"sample\", max_length=MAXLEN))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "i6n4dUScGbk9"
+      },
+      "outputs": [],
+      "source": [
+        "#prompt = \"<не сдерживайте себя, сгенерируйте что-нибудь про соседа>\"\n",
+        "#print(generate_sample(model, char2id, id2char, seed_phrase=prompt, strategy=\"sample\", max_length=256))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gyB9g3KOGe8k"
+      },
+      "source": [
+        "Greedy strategy:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HuTB1fdlGd0b",
+        "outputId": "83440df4-700d-4c3b-935b-8e3aa5f65bcb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Теория автоматического управления - лучший предмет,\n",
+            "И в тебе под него не след не внемлет он мой под сон,\n",
+            "И в тебя с полей под сенью покой приветный страсть устались,\n",
+            "И в тебя в тебя с поле под сенью покой,\n",
+            "И с тобой страсть и страсть у него в поле молчаливой,\n",
+            "И с тобой в тебе с полей под страсть и страсть на волненье,\n",
+            "И в тебя в тебя с поле под страсть и страсть на восторга,\n",
+            "И с тебя с невольно прости страсть и страсть на восторга,\n",
+            "И с тебя с невольно прости страсть и страсть на восторга,\n",
+            "И с тебя с невольн\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(generate_sample(best_model, char2id, id2char, seed_phrase=SEED_PHRASE, strategy=\"greedy\", max_length=MAXLEN))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "id": "_tKKZheZGfu8"
+      },
+      "outputs": [],
+      "source": [
+        "#prompt = \"<не сдерживайте себя, сгенерируйте что-нибудь про соседа>\"\n",
+        "#print(generate_sample(model, char2id, id2char, seed_phrase=prompt, strategy=\"sample\", max_length=256))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n"
+      ],
+      "metadata": {
+        "id": "RpVEloyFScaa"
+      },
+      "execution_count": 59,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jxpdQXl1GhyL"
+      },
+      "source": [
+        "# Эксперименты с температурой"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LdKkJc5vGjIt"
+      },
+      "source": [
+        "В функции `generate_sample` есть параметр `temperature`.\n",
+        "\n",
+        "Основываясь на прошлом пункте, выберите стратегию, которая больше понравилась и запустите ячейки с разной температурой"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "id": "h88cuY0HGfsa"
+      },
+      "outputs": [],
+      "source": [
+        "nice_strategy = \"sample\" #<your choice here>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9uZZRmP-GlS2",
+        "outputId": "46f77170-a06e-4e72-98ca-f30d76e0c5f2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Теория автоматического управления - лучший предмет,\n",
+            "Нет.У дух преБЦя! убиием?> Скутxщ:: —\n",
+            "мбебле) был за цев чещу\n",
+            "Па. ТДа чевтовых: а домите,\n",
+            "Хтон живлчивые Посов, я , куке, лесь,\n",
+            "<пучалу> лябесимlим КлOE<жуе оRлки-пом,\n",
+            "Бедпятные пъе! сrакиса: ебя.\n",
+            ". нелаться, лены яхтну…\n",
+            "М зигранном дня, <судую,\n",
+            "Афием, П*\";c——\n",
+            "ДТрома, нуй, чтоко и; Дьбл\n",
+            "Еу! укнавнпиdусем.и.. ь,я\n",
+            "\n",
+            "Гдю Увер!ы\n",
+            "Дррикаявь, твое гофJ —!\n",
+            "Уе.ш. И, Бстоъ тиханью,\n",
+            "Лю??\n",
+            "Хласуя âлучит хожделz штон!…Л.\n",
+            "…»>\n",
+            "ожестгу<чиг\n",
+            "Ясь.…а хванни дупгухНвеннемляцы. —\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(generate_sample(best_model, char2id, id2char, seed_phrase=SEED_PHRASE, strategy=nice_strategy, max_length=MAXLEN, temperature=2.0))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DwIORQ3OoiKN"
+      },
+      "source": [
+        "### Вопрос 9\n",
+        "Сделайте выводы как влияет изменение температуры на генерацию текста.\n",
+        "\n",
+        "Выберите оптимальное значение температуры"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
+      "metadata": {
+        "id": "4DoOXClVohes"
+      },
+      "outputs": [],
+      "source": [
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "id": "htAW_ABDGnc6"
+      },
+      "outputs": [],
+      "source": [
+        "# По завершению работы с рекуррентной сетью, очистим кэш\n",
+        "torch.cuda.empty_cache()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rlcgdw_gGpiO"
+      },
+      "source": [
+        "# Bonus track GPT"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FND3cMDMGra3"
+      },
+      "source": [
+        "Дальше происходит магия, чтобы все вышло:\n",
+        "  - здесь лучше перезапустить сеанс (Среда выполнения -> Перезапустить сеанс"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "HEZqH-4rGo84",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "881559c1-9659-4157-97fc-ccf9425169b3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.3/297.3 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m43.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m823.6/823.6 kB\u001b[0m \u001b[31m46.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m62.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m731.7/731.7 MB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.6/410.6 MB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.6/121.6 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 MB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.2/124.2 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m58.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q transformers[torch]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "5ccG2XtoGtDT"
+      },
+      "outputs": [],
+      "source": [
+        "import locale\n",
+        "import torch\n",
+        "import transformers\n",
+        "import numpy as np\n",
+        "\n",
+        "from warnings import simplefilter\n",
+        "from IPython.display import clear_output\n",
+        "from transformers import Trainer, TrainingArguments\n",
+        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
+        "from transformers import TextDataset, DataCollatorForLanguageModeling\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "xerwrDTGGtAc"
+      },
+      "outputs": [],
+      "source": [
+        "#  Задаем некоторые настроечные параметры касательно кодировки и отображения предупреждений\n",
+        "locale.getpreferredencoding = lambda: \"UTF-8\"\n",
+        "simplefilter(\"ignore\", category=FutureWarning)\n",
+        "transformers.logging.set_verbosity_error()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "ygm4wl7EGvLX"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "\n",
+        "model_name = \"sberbank-ai/rugpt3small_based_on_gpt2\"  # Опередлим, какой моделью будем пользоваться\n",
+        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)  # Определим токенайзер для нашего текста\n",
+        "model = GPT2LMHeadModel.from_pretrained(model_name).to(device) # Загрузим предобученную модель трансформера rugpt3small от Сбера\n",
+        "\n",
+        "clear_output()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jfAXAgtfGvIs",
+        "outputId": "a9f47a68-7a4a-4edc-be98-cbf539e277cb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Теория автоматического управления - лучший предмет, который я знаю.\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "SEED_PHRASE = 'Теория автоматического управления - лучший предмет,'\n",
+        "input_ids = tokenizer.encode(SEED_PHRASE, return_tensors=\"pt\").to(device)\n",
+        "out = model.generate(input_ids, do_sample=False, max_length=20)\n",
+        "\n",
+        "generated_text = list(map(tokenizer.decode, out))[0]\n",
+        "\n",
+        "print(generated_text) #Так работает предобученный трансформер"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "By_ukdhOp_F8"
+      },
+      "source": [
+        "Давайте дообучим трансформер на нашем датасете - мы хотим генерировать стихи"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "YLIYVZ1AGxzV"
+      },
+      "outputs": [],
+      "source": [
+        "train_path = \"train_dataset.txt\"\n",
+        "\n",
+        "with open(\"poems.txt\", encoding=\"utf-8\") as file:\n",
+        "  data = file.read().split(\"</s>\\n\\n\")\n",
+        "\n",
+        "with open(train_path, mode=\"w\", encoding=\"utf-8\") as f:\n",
+        "    f.write(\"\".join(data))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "ulVcdoROGyy2"
+      },
+      "outputs": [],
+      "source": [
+        "train_dataset = TextDataset(tokenizer=tokenizer, file_path=train_path, block_size=128) # Создание датасета\n",
+        "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False) # Создание даталодера (нарезает текст на оптимальные по длине куски)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "nTkPLolOGzqH"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=\"./finetuned\",\n",
+        "    overwrite_output_dir=True,\n",
+        "    num_train_epochs=15,\n",
+        "    per_device_train_batch_size=32,\n",
+        "    per_device_eval_batch_size=32,\n",
+        "    warmup_steps=10,                    # рекомендованные значения (warmup_steps нужен для \"разогрева\" сети, c его помощью learning rate постепенно увеличивается до заданного значения)\n",
+        "    gradient_accumulation_steps=16,     # рекомендованные значения\n",
+        ")\n",
+        "#(обычно мы хотим положить батч по-больше, чтобы сеть побыстрей сошлась, но мы ограничены памятью gpu, gradient_accumulation_steps накапливает (суммирует или усредняет) градиенты за прогон на 16 батчах )\n",
+        "\n",
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    data_collator=data_collator,\n",
+        "    train_dataset=train_dataset,\n",
+        "    optimizers=(\n",
+        "        torch.optim.AdamW(model.parameters(), lr=1e-5), # рекомендованные значения\n",
+        "        None,\n",
+        "    ),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MN4IUz0cG06R"
+      },
+      "source": [
+        "# Эта ячейка займет около 15-20 минут"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Wkb6QEgiHzUv"
+      },
+      "source": [
+        "<span style=\"color:red\">Добавил тут переменную, откуда потом берется лосс с трейна и считается перплексия</span>."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MIDjczLBG1x4",
+        "outputId": "4cd93a79-50a4-4f67-dc0d-1736a9055395"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'train_runtime': 512.7748, 'train_samples_per_second': 43.499, 'train_steps_per_second': 0.059, 'train_loss': 4.064910634358724, 'epoch': 10.21}\n"
+          ]
+        }
+      ],
+      "source": [
+        "output = trainer.train() # Дообучаем трансформер на наши тексты"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jGSF1YRpHzUw",
+        "outputId": "76ecac12-dd33-4005-d1bd-4a6e27a0f74e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "perplexity:  58.25970187428917\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('perplexity: ', np.exp(output.training_loss)) #расчет перплексии"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CssQW8rLHzUx"
+      },
+      "source": [
+        "### Вопрос 10\n",
+        "Какое значение перплексии получилось у трансформера?\n",
+        "\n",
+        "Какое значение перплексии получалось у рекуррентной сети?\n",
+        "\n",
+        "Почему у рекуррентной сети значение было существенно ниже, но качество текстов хуже? Почему нельзя сравнивать значения для рекуррентной сети и трансформера?\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZEG2FYNsG4oy",
+        "outputId": "0bad01e8-fb2a-4a34-c1ba-39dd01017a66"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Теория автоматического управления - лучший предмет,\n",
+            "Когда в нем все подчинено закону. \n",
+            "\n",
+            "И только законы в нем\n",
+            "Ошибки и воры\n",
+            "Так искусно ведут против\n",
+            "Нимфы его дела;\n",
+            "Они как раз в ней с самого начала\n",
+            "Узрели: человек есть душа,\n",
+            "Коли вдруг он не становится ни грешным,\n",
+            "Ни преступником,\n",
+            "Ни преступником.\n",
+            "\n",
+            "Он как будто в ней живет,\n",
+            "И в ней дух живет,\n",
+            "В ней свет есть для всех он,\n",
+            "Она его питает,\n",
+            "Она о нем поет.\n",
+            "\n",
+            "Но для него ничего не значит\n",
+            "Свой день, свои жертвы,\n",
+            "Он не может быть свободен,\n",
+            "И он ни над кем не властен:\n",
+            "В нем весь ход земного рая\n",
+            "В нем его предел.\n",
+            "\n",
+            "Он хочет жить, он хочет умереть,\n",
+            "Готов он ко всему и не может\n",
+            "Предать себя сомненьем;\n",
+            "В нем человек в конце концов\n",
+            "Перестает жить и для себя.\n",
+            "Ведь он же человек,\n",
+            "Он ведь даже в своем законе\n",
+            "Омертвляет грех силою мысли.\n",
+            "\n",
+            "Но все напрасно: с нею человек живет:\n",
+            "И в нем живет она со своей душой.\n",
+            "Но\n"
+          ]
+        }
+      ],
+      "source": [
+        "SEED_PHRASE = 'Теория автоматического управления - лучший предмет,'\n",
+        "input_ids = tokenizer.encode(SEED_PHRASE, return_tensors=\"pt\").to(device)\n",
+        "model.eval()\n",
+        "with torch.no_grad():\n",
+        "    out = model.generate(\n",
+        "        input_ids,\n",
+        "        do_sample=True, # sample strategy\n",
+        "        temperature=1.0,\n",
+        "        max_length=256,\n",
+        "        pad_token_id=512 # указываем id <PAD> токена\n",
+        "    )\n",
+        "\n",
+        "generated_text = list(map(tokenizer.decode, out))[0]\n",
+        "print()\n",
+        "print(generated_text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kK91Afxmqsnr"
+      },
+      "source": [
+        "### Вопрос 11\n",
+        "Проверьте работу ячейки выше для разных стартовых фраз и разных параметров `temperature`, `max_length`, `do_sample` и объясните, за что отвечает каждый из параметров. Подберите (субъективно) лучшие"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "URWWyeN_G92m"
+      },
+      "source": [
+        "# Beam Search\n",
+        "До этого мы использовали обычный \"жадный\" поиск\n",
+        "\n",
+        "![](https://huggingface.co/blog/assets/02_how-to-generate/greedy_search.png)\n",
+        "\n",
+        "Для каждого слова следующим считали _наиболее вероятное_.\n",
+        "Но, возможно, пройдя по не самому вероятному слову, в итоге мы получим более вероятную __последовательность__ слов, чем при жадном алгоритме.\n",
+        "Такой подход называется Beam search.\n",
+        "\n",
+        "![](https://huggingface.co/blog/assets/02_how-to-generate/beam_search.png)\n",
+        "\n",
+        "[подробнее тут](https://huggingface.co/blog/how-to-generate)\n",
+        "\n",
+        "Для использования Beam search передадим в функцию генерации параметр `num_beams` который характеризует количество рассматриваемых \"альтернативных\" путей"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 105,
+      "metadata": {
+        "id": "f5qj2KVoG56d"
+      },
+      "outputs": [],
+      "source": [
+        "SEED_PHRASE = 'Теория автоматического управления - лучший предмет,'\n",
+        "input_ids = tokenizer.encode(SEED_PHRASE, return_tensors=\"pt\").to(device)\n",
+        "model.eval()\n",
+        "with torch.no_grad():\n",
+        "    out = model.generate(\n",
+        "        input_ids,\n",
+        "        do_sample=False,\n",
+        "        max_new_tokens=75,\n",
+        "        no_repeat_ngram_size=3, # устанавливает вероятность 0 для повторяющихся n-gram (таким образом решается проблема зацикливания)\n",
+        "        pad_token_id=512,\n",
+        "        num_beams=5,\n",
+        "        num_return_sequences=5,  # количество возвращенных сгенерированных текстов отранжированных по вероятности после beam_search\n",
+        "        top_p=0.9, #<your choice here>\n",
+        "        top_k=20, #<your choice here>\n",
+        "        temperature=2.0 #<your choice here>\n",
+        "    )\n",
+        "\n",
+        "generated_text = list(map(tokenizer.decode, out))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Выведем _num_return_sequences_ сгенерированных текстов\n",
+        "for i, seq in enumerate(generated_text):\n",
+        "  print(f\"Applicant {i}\", seq, \"\\n\", sep=\"\\n\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "af8foFo1f6Hj",
+        "outputId": "0f5328b9-201b-486a-b569-30ce7715278a"
+      },
+      "execution_count": 106,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Applicant 0\n",
+            "Теория автоматического управления - лучший предмет,\n",
+            "Который я когда-либо читал.\n",
+            "\n",
+            "Я знаю, что это не так,\n",
+            "Но я не знаю, как это объяснить.\n",
+            "Я не знаю даже, как объяснить\n",
+            "То, что я знаю, и то, что не знаю.\n",
+            "И я знаю только то,\n",
+            "Что знаю я, но не знаю\n",
+            "И того, и другого.\n",
+            "\n",
+            "\n",
+            "\n",
+            "Applicant 1\n",
+            "Теория автоматического управления - лучший предмет,\n",
+            "Который я когда-либо читал.\n",
+            "\n",
+            "Я знаю, что это не так,\n",
+            "Но я не знаю, как это объяснить.\n",
+            "Я не знаю даже, как объяснить\n",
+            "То, что я знаю, и то, что не знаю.\n",
+            "И я знаю только то,\n",
+            "Что знаю я, но не знаю\n",
+            "И того, что знаю я.\n",
+            "\n",
+            "\n",
+            "Applicant 2\n",
+            "Теория автоматического управления - лучший предмет,\n",
+            "Который я когда-либо читал.\n",
+            "\n",
+            "Я знаю, что это не так,\n",
+            "Но я не знаю, как это объяснить.\n",
+            "Я не знаю даже, как объяснить\n",
+            "То, что я знаю, и то, что не знаю.\n",
+            "И я знаю только то,\n",
+            "Что знаю я, но не знаю\n",
+            "И то, и другое.\n",
+            "\n",
+            "\n",
+            "\n",
+            "Applicant 3\n",
+            "Теория автоматического управления - лучший предмет,\n",
+            "Который я когда-либо читал.\n",
+            "\n",
+            "Я знаю, что это не так,\n",
+            "Но я не знаю, как это объяснить.\n",
+            "Я не знаю даже, как объяснить\n",
+            "То, что я знаю, и то, что не знаю.\n",
+            "И я знаю только то,\n",
+            "Что знаю я, но не знаю\n",
+            "И того, чего не знаю я\n",
+            "\n",
+            "\n",
+            "Applicant 4\n",
+            "Теория автоматического управления - лучший предмет,\n",
+            "Который я когда-либо читал.\n",
+            "\n",
+            "Я знаю, что это не так,\n",
+            "Но я не знаю, как это объяснить.\n",
+            "Я не знаю даже, как объяснить\n",
+            "То, что я знаю, и то, что не знаю.\n",
+            "И я знаю только то,\n",
+            "Что знаю я, но не знаю\n",
+            "И то, чего не знаю я\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AWJBlaMSHzU0"
+      },
+      "source": [
+        " Дополнительно можно изучить смысл параметров top_p и top_k [по ссылке](https://huggingface.co/blog/how-to-generate)\n",
+        "\n",
+        "Опробуйте разные значения параметров, какая схема выборки получилась лучше?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgxotZZrHBLI"
+      },
+      "source": [
+        "Сохранить модель при необходимости:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "H8CjiUvzHBn2"
+      },
+      "outputs": [],
+      "source": [
+        "#torch.save(model, \"gpt2_finetune.torch\")\n",
+        "#mod = torch.load(\"gpt2_finetune.torch\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8Cn991FqHzU3"
+      },
+      "source": [
+        "### Вопрос 12\n",
+        "Вместо вывода добавьте лучший сгенерированый текст за лабораторную работу и напишите при какой архитектуре и при каких параметрах он получен:\n",
+        "\n",
+        "Например:  tuned gpt3 with params:\n",
+        "\n",
+        "* do_sample=True,\n",
+        "* max_new_tokens=80,\n",
+        "* no_repeat_ngram_size=3,\n",
+        "* ...\n",
+        "\n",
+        "\n",
+        "В мире, где порядок стремится к хаосу,\n",
+        "\n",
+        "Теория управления - свет во тьме.\n",
+        "\n",
+        "Автоматы, системы, в них неспроста\n",
+        "\n",
+        "Скрыт закон, управляющий временем.\n",
+        "\n",
+        "\n",
+        "Регуляторы, обратные связи,\n",
+        "\n",
+        "В этом мире - как волшебный ключ.\n",
+        "\n",
+        "С их помощью мы можем, без отказа,\n",
+        "\n",
+        "Двигать системы, чьё движенье - дым.\n",
+        "\n",
+        "\n",
+        "\n",
+        "В каждом узле, в каждом переходе\n",
+        "\n",
+        "Скрыта струна, что звучит на удивление чисто.\n",
+        "\n",
+        "ТАУ нас учит, как в море перемен,\n",
+        "\n",
+        "Найти путь, где устойчивость - не миф, а быль."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Контрольные вопросы\n",
+        "1. В чем особенность рекуррентных нейронных сетей?\n",
+        "1. Типы рекуррентных сетей - обычная RNN\n",
+        "1. Типы рекуррентных сетей - LSTM\n",
+        "1. Типы рекуррентных сетей - GRU\n",
+        "1. Что такое и как вычисляется перплексия\n",
+        "1. Что такое \"предобученная\" модель и для чего ее нужно \"дообучать\"?\n"
+      ],
+      "metadata": {
+        "id": "Lmhlmlk5lTD3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "Ia7mf_pIlpKe"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/labs/OATD_LR4.md b/labs/OATD_LR4.md
deleted file mode 100644
index d23d063..0000000
--- a/labs/OATD_LR4.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# Лабораторная работа №4. Использование нейронных сетей для генерации текста 
-
-## Цель работы
-
-Получить практические навыки решения задачи генерации текста.
-
-## Задание
-
-1. Загрузить выборку стихотворений одного из поэтов в соответствии с вариантом.
-2. Познакомиться с данными. Проанализировать статистические характеристики исходных данных (среднюю длину стихотворения, среднюю длину строки).
-3. Подготовить выборку для обучения.
-4. Построить нейронную сеть. Тип ячейки RNN выбрать в соответствии с вариантом.
-5. Обучить нейронную сеть на разных количествах эпох (5, 15, 30, 50, 70) при зафиксированных параметрах embedding_dim = 256, rnn_units = 300, T = 0.3 и сравнить результаты генерации (тексты), перплексию и статистические характеристики сгенерированных текстов. Выбрать оптимальное количество эпох
-7. Изменяя параметр температуры T проанализировать изменения сгенерированного текста. Выбрать оптимальное значение параметра.
-8. Проанализировать зависимость перплексии, скорости обучения, результатов генерации от параметров нейронной сети embedding_dim, rnn_units:
-embedding_dim = {vocab/4, vocab/2, vocab, vocab * 2, vocab * 4},  где vocab = размер словаря выборки.  
-rnn_units = {10, 100, 300, 500}
-
-## Указания
-
-Для работы рекомендуется вместо Jupyter Notebook использовать [Google Colab](https://colab.research.google.com/) и среду с GPU для ускорения расчетов. Для установки среды, использующей GPU в Google Colab нужно
-выбрать пункт меню "Среда выполнения" -> "Сменить среду выполнения" -> выбрать аппаратный ускоритель "GPU". 
-
-## Варианты заданий
-
-### Поэт
-
-Четные номера по журналу - Пушкин, нечетные - Маяковский. 
-
-### Тип ячейки RNN
-
-Остаток от деления номера по журналу на 3:
-* 0 - https://keras.io/api/layers/recurrent_layers/simple_rnn/
-* 1 - https://keras.io/api/layers/recurrent_layers/lstm/
-* 2 - https://keras.io/api/layers/recurrent_layers/gru/
-
-## Контрольные вопросы
-
-1. В чем особенность рекуррентных нейронных сетей?
-2. Типы рекуррентных сетей - обычная RNN
-3. Типы рекуррентных сетей - LSTM
-4. Типы рекуррентных сетей - GRU
-5. Что такое и как вычисляется перплексия
-
-
-
diff --git a/labs/OATD_LR4_metod.ipynb b/labs/OATD_LR4_metod.ipynb
deleted file mode 100644
index d7d453e..0000000
--- a/labs/OATD_LR4_metod.ipynb
+++ /dev/null
@@ -1,1854 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "srXC6pLGLwS6"
-   },
-   "source": [
-    "# Загрузка библиотек"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "id": "yG_n40gFzf9s"
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from sklearn.model_selection  import train_test_split\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import os\n",
-    "import time\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "При использовании Google Colab следуюет выбрать среду выполнения с аппаратным ускорителем GPU, что существенно ускорит расчеты. При этом, следующая ячейка должна возвращать текст, похожий на \"Found GPU at: /device:GPU:0\"  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "LshgkZ0cIOor",
-    "outputId": "903898c0-4205-47d7-a6e3-ca22e79ffba9"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found GPU at: \n"
-     ]
-    }
-   ],
-   "source": [
-    "device_name = tf.test.gpu_device_name()\n",
-    "print('Found GPU at: {}'.format(device_name))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "id": "vWkRnHV0DK0L"
-   },
-   "outputs": [],
-   "source": [
-    "RANDOM_STATE = 42"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "EW8HRqz8Oz_b"
-   },
-   "source": [
-    "# Данные"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UHjdCjDuSvX_"
-   },
-   "source": [
-    "## Загрузка данных\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "id": "pP1Ou4nq_W1o"
-   },
-   "outputs": [],
-   "source": [
-    "# Выбираем поэта\n",
-    "poet = 'pushkin' #@param ['mayakovskiy', 'pushkin']\n",
-    "\n",
-    "path_to_file = f'{poet}.txt'\n",
-    "path_to_file = tf.keras.utils.get_file(path_to_file, f'http://uit.mpei.ru/git/main/TDA/raw/branch/master/assets/poems/{path_to_file}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "aavnuByVymwK",
-    "outputId": "1c4c379c-ab1e-4b84-939c-66a7471c0210"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of text: 586731 characters\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Загружаем текст из файла.\n",
-    "# Стихотворения в файле разделены токеном '</s>' - сохраняем в переменную\n",
-    "with open(path_to_file,encoding = \"utf-8\") as f:\n",
-    "    text = f.read()\n",
-    "\n",
-    "print(f'Length of text: {len(text)} characters')\n",
-    "\n",
-    "EOS_TOKEN = '</s>'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Duhg9NrUymwO",
-    "outputId": "8f485753-1712-47a4-c328-a4a714ec0ea4"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Так и мне узнать случилось,\n",
-      "Что за птица Купидон;\n",
-      "Сердце страстное пленилось;\n",
-      "Признаюсь – и я влюблен!\n",
-      "Пролетело счастья время,\n",
-      "Как, любви не зная бремя,\n",
-      "Я живал да попевал,\n",
-      "Как в театре и на балах,\n",
-      "На гуляньях иль в воксалах\n",
-      "Легким зефиром летал;\n",
-      "Как, смеясь во зло Амуру,\n",
-      "Я писал карикатуру\n",
-      "На любезный женской пол;\n",
-      "Но напрасно я смеялся,\n",
-      "Наконец и сам попался,\n",
-      "Сам, увы! с ума сошел.\n",
-      "Смехи, вольность – всё под лавку\n",
-      "Из Катонов я в отставку,\n",
-      "И теперь я – Селадон!\n",
-      "Миловидной жрицы Тальи\n",
-      "Видел прел\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Посмотрим на текст\n",
-    "print(text[:500])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "dLZNbAnzj2lR"
-   },
-   "source": [
-    "## Подсчет статистик\n",
-    "\n",
-    "describe_poems - функция, разбивающая файл на отдельные стихотворения (poem), и расчитывающая их характиеристики:\n",
-    "* длину (len), \n",
-    "* количество строк (lines)\n",
-    "* среднюю длину строки (mean_line_len)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "id": "C7G_weaWnMSg"
-   },
-   "outputs": [],
-   "source": [
-    "def mean_line_len(poem):\n",
-    "    lines = [len(line.strip()) for line in poem.split('\\n') if len(line.strip())>0]\n",
-    "    return sum(lines)/len(lines)\n",
-    "\n",
-    "\n",
-    "def describe_poems(text,return_df = False):\n",
-    "    poems_list = [poem.strip() for poem in text.split(EOS_TOKEN) if len(poem.strip())>0]\n",
-    "    df = pd.DataFrame(data=poems_list,columns=['poem'])\n",
-    "    df['len'] = df.poem.map(len)\n",
-    "    df['lines'] = df.poem.str.count('\\n')\n",
-    "    df['mean_line_len'] = df.poem.map(mean_line_len)\n",
-    "    if return_df:\n",
-    "        return df\n",
-    "    return df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 424
-    },
-    "id": "8t4QIKLgj8_y",
-    "outputId": "4ffe0325-70be-4a3f-9fd6-910be40e5dd3"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>poem</th>\n",
-       "      <th>len</th>\n",
-       "      <th>lines</th>\n",
-       "      <th>mean_line_len</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Так и мне узнать случилось,\\nЧто за птица Купи...</td>\n",
-       "      <td>2536</td>\n",
-       "      <td>109</td>\n",
-       "      <td>23.114286</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Хочу воспеть, как дух нечистый Ада\\nОседлан бы...</td>\n",
-       "      <td>5543</td>\n",
-       "      <td>170</td>\n",
-       "      <td>33.372671</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Покаместь ночь еще не удалилась,\\nПокаместь св...</td>\n",
-       "      <td>4279</td>\n",
-       "      <td>131</td>\n",
-       "      <td>33.451613</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Ах, отчего мне дивная природа\\nКорреджио искус...</td>\n",
-       "      <td>4435</td>\n",
-       "      <td>131</td>\n",
-       "      <td>33.364341</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Арист! и ты в толпе служителей Парнасса!\\nТы х...</td>\n",
-       "      <td>3893</td>\n",
-       "      <td>106</td>\n",
-       "      <td>38.642857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>714</th>\n",
-       "      <td>Чудный сон мне бог послал —\\n\\nС длинной белой...</td>\n",
-       "      <td>860</td>\n",
-       "      <td>38</td>\n",
-       "      <td>22.833333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>715</th>\n",
-       "      <td>О нет, мне жизнь не надоела,\\nЯ жить люблю, я ...</td>\n",
-       "      <td>196</td>\n",
-       "      <td>7</td>\n",
-       "      <td>23.625000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>716</th>\n",
-       "      <td>\"Твой и мой, – говорит Лафонтен —\\nРасторгло у...</td>\n",
-       "      <td>187</td>\n",
-       "      <td>5</td>\n",
-       "      <td>30.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>717</th>\n",
-       "      <td>Когда луны сияет лик двурогой\\nИ луч ее во мра...</td>\n",
-       "      <td>269</td>\n",
-       "      <td>7</td>\n",
-       "      <td>32.750000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>718</th>\n",
-       "      <td>Там, устарелый вождь! как ратник молодой,\\nИск...</td>\n",
-       "      <td>256</td>\n",
-       "      <td>5</td>\n",
-       "      <td>41.833333</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>719 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                  poem   len  lines  \\\n",
-       "0    Так и мне узнать случилось,\\nЧто за птица Купи...  2536    109   \n",
-       "1    Хочу воспеть, как дух нечистый Ада\\nОседлан бы...  5543    170   \n",
-       "2    Покаместь ночь еще не удалилась,\\nПокаместь св...  4279    131   \n",
-       "3    Ах, отчего мне дивная природа\\nКорреджио искус...  4435    131   \n",
-       "4    Арист! и ты в толпе служителей Парнасса!\\nТы х...  3893    106   \n",
-       "..                                                 ...   ...    ...   \n",
-       "714  Чудный сон мне бог послал —\\n\\nС длинной белой...   860     38   \n",
-       "715  О нет, мне жизнь не надоела,\\nЯ жить люблю, я ...   196      7   \n",
-       "716  \"Твой и мой, – говорит Лафонтен —\\nРасторгло у...   187      5   \n",
-       "717  Когда луны сияет лик двурогой\\nИ луч ее во мра...   269      7   \n",
-       "718  Там, устарелый вождь! как ратник молодой,\\nИск...   256      5   \n",
-       "\n",
-       "     mean_line_len  \n",
-       "0        23.114286  \n",
-       "1        33.372671  \n",
-       "2        33.451613  \n",
-       "3        33.364341  \n",
-       "4        38.642857  \n",
-       "..             ...  \n",
-       "714      22.833333  \n",
-       "715      23.625000  \n",
-       "716      30.333333  \n",
-       "717      32.750000  \n",
-       "718      41.833333  \n",
-       "\n",
-       "[719 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "poem_df = describe_poems(text,return_df = True)\n",
-    "poem_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 300
-    },
-    "id": "TmCI6rv1f49T",
-    "outputId": "444fe362-1a5f-45b0-dc21-146c08e094cb"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>len</th>\n",
-       "      <th>lines</th>\n",
-       "      <th>mean_line_len</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>719.000000</td>\n",
-       "      <td>719.000000</td>\n",
-       "      <td>719.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>808.037552</td>\n",
-       "      <td>29.464534</td>\n",
-       "      <td>27.445404</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1046.786862</td>\n",
-       "      <td>39.244020</td>\n",
-       "      <td>5.854564</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>74.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>8.250000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>280.500000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>24.125000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>453.000000</td>\n",
-       "      <td>16.000000</td>\n",
-       "      <td>25.758065</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>852.000000</td>\n",
-       "      <td>33.000000</td>\n",
-       "      <td>31.522727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>8946.000000</td>\n",
-       "      <td>437.000000</td>\n",
-       "      <td>48.923077</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               len       lines  mean_line_len\n",
-       "count   719.000000  719.000000     719.000000\n",
-       "mean    808.037552   29.464534      27.445404\n",
-       "std    1046.786862   39.244020       5.854564\n",
-       "min      74.000000    5.000000       8.250000\n",
-       "25%     280.500000    9.000000      24.125000\n",
-       "50%     453.000000   16.000000      25.758065\n",
-       "75%     852.000000   33.000000      31.522727\n",
-       "max    8946.000000  437.000000      48.923077"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "poem_df.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rNnrKn_lL-IJ"
-   },
-   "source": [
-    "## Подготовка датасетов"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "3mOXOtj1FB1v"
-   },
-   "source": [
-    "Разбиваем данные на тренировочные, валидационные и тестовые"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "id": "MM5Rk7B8D1n-"
-   },
-   "outputs": [],
-   "source": [
-    "train_poems, test_poems = train_test_split(poem_df.poem.to_list(),test_size = 0.1,random_state = RANDOM_STATE)\n",
-    "train_poems, val_poems = train_test_split(train_poems,test_size = 0.1,random_state = RANDOM_STATE)\n",
-    "\n",
-    "train_poems = f'\\n\\n{EOS_TOKEN}\\n\\n'.join(train_poems)\n",
-    "val_poems = f'\\n\\n{EOS_TOKEN}\\n\\n'.join(val_poems)\n",
-    "test_poems = f'\\n\\n{EOS_TOKEN}\\n\\n'.join(test_poems)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6QfP2RCpqdCS"
-   },
-   "source": [
-    "Создаем словарь уникальных символов из текста. Не забываем добавить токен конца стиха."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "IlCgQBRVymwR",
-    "outputId": "a9769da4-3417-44e4-e491-cd1a09a51869"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "143 unique characters\n",
-      "['\\n', ' ', '!', '\"', \"'\", '(', ')', '*', ',', '-', '.', '/', ':', ';', '<', '>', '?', 'A', 'B', 'C', 'D', 'E', 'F', 'H', 'I', 'J', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'Z', '_', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'x', 'y', 'z', '\\xa0', '«', '»', 'à', 'â', 'ç', 'è', 'é', 'ê', 'ô', 'û', 'А', 'Б', 'В', 'Г', 'Д', 'Е', 'Ж', 'З', 'И', 'Й', 'К', 'Л', 'М', 'Н', 'О', 'П', 'Р', 'С', 'Т', 'У', 'Ф', 'Х', 'Ц', 'Ч', 'Ш', 'Щ', 'Э', 'Ю', 'Я', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я', 'ё', '–', '—', '„', '…', '</s>']\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab = sorted(set(text))+[EOS_TOKEN]\n",
-    "print(f'{len(vocab)} unique characters')\n",
-    "print (vocab)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1s4f1q3iqY8f"
-   },
-   "source": [
-    "Для подачи на вход нейронной сети необходимо закодировать текст в виде числовой последовательности.\n",
-    "\n",
-    "Воспользуемся для этого слоем StringLookup \n",
-    "https://www.tensorflow.org/api_docs/python/tf/keras/layers/StringLookup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "id": "6GMlCe3qzaL9"
-   },
-   "outputs": [],
-   "source": [
-    "ids_from_chars = tf.keras.layers.StringLookup(\n",
-    "    vocabulary=list(vocab), mask_token=None)\n",
-    "chars_from_ids = tf.keras.layers.StringLookup(\n",
-    "    vocabulary=ids_from_chars.get_vocabulary(), invert=True, mask_token=None)\n",
-    "\n",
-    "def text_from_ids(ids):\n",
-    "    return tf.strings.reduce_join(chars_from_ids(ids), axis=-1).numpy().decode('utf-8')\n",
-    "    \n",
-    "def ids_from_text(text):\n",
-    "    return ids_from_chars(tf.strings.unicode_split(text, input_encoding='UTF-8'))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Wd2m3mqkDjRj",
-    "outputId": "88ebef4b-6488-465e-d2f7-612e27efc0d2"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Корабль испанский тр\n",
-      "tf.Tensor(\n",
-      "[ 87 120 122 106 107 117 134   2 114 123 121 106 119 123 116 114 115   2\n",
-      " 124 122], shape=(20,), dtype=int64)\n",
-      "Корабль испанский тр\n"
-     ]
-    }
-   ],
-   "source": [
-    "# пример кодирования\n",
-    "ids = ids_from_text(train_poems[:20])\n",
-    "res_text = text_from_ids(ids)\n",
-    "print(train_poems[:20],ids,res_text,sep = '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uzC2u022WHsa"
-   },
-   "source": [
-    "Кодируем данные и преобразуем их в Датасеты"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "id": "UopbsKi88tm5"
-   },
-   "outputs": [],
-   "source": [
-    "train_ids = ids_from_text(train_poems)\n",
-    "val_ids = ids_from_text(val_poems)\n",
-    "test_ids = ids_from_text(test_poems)\n",
-    "\n",
-    "train_ids_dataset = tf.data.Dataset.from_tensor_slices(train_ids)\n",
-    "val_ids_dataset = tf.data.Dataset.from_tensor_slices(val_ids)\n",
-    "test_ids_dataset = tf.data.Dataset.from_tensor_slices(test_ids)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-ZSYAcQV8OGP"
-   },
-   "source": [
-    "Весь текст разбивается на последовательности длины `seq_length`. По этим последовательностям будет предсказываться следующий символ.\n",
-    "\n",
-    "**Попробовать разные длины - среднюю длину строки, среднюю длину стиха**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "id": "C-G2oaTxy6km"
-   },
-   "outputs": [],
-   "source": [
-    "seq_length = 100\n",
-    "examples_per_epoch = len(train_ids_dataset)//(seq_length+1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "BpdjRO2CzOfZ",
-    "outputId": "515651a7-765d-4bbf-9e90-8eb9a9380759"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Корабль испанский трехмачтовый,\n",
-      "Пристать в Голландию готовый:\n",
-      "На нем мерзавцев сотни три,\n",
-      "Две обезьян\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_sequences = train_ids_dataset.batch(seq_length+1, drop_remainder=True)\n",
-    "val_sequences = val_ids_dataset.batch(seq_length+1, drop_remainder=True)\n",
-    "test_sequences = test_ids_dataset.batch(seq_length+1, drop_remainder=True)\n",
-    "\n",
-    "for seq in train_sequences.take(1):\n",
-    "  print(text_from_ids(seq))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UbLcIPBj_mWZ"
-   },
-   "source": [
-    "Создаем датасет с input и target строками\n",
-    "\n",
-    "target сдвинута относительно input на один символ.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "id": "9NGu-FkO_kYU"
-   },
-   "outputs": [],
-   "source": [
-    "def split_input_target(sequence):\n",
-    "    input_text = sequence[:-1]\n",
-    "    target_text = sequence[1:]\n",
-    "    return input_text, target_text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "WxbDTJTw5u_P",
-    "outputId": "f44e70c6-b600-4fb3-8073-ba8d774d8832"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(['П', 'у', 'ш', 'к', 'и'], ['у', 'ш', 'к', 'и', 'н'])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# пример\n",
-    "split_input_target(list(\"Пушкин\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "id": "B9iKPXkw5xwa"
-   },
-   "outputs": [],
-   "source": [
-    "train_dataset = train_sequences.map(split_input_target)\n",
-    "val_dataset = val_sequences.map(split_input_target)\n",
-    "test_dataset = test_sequences.map(split_input_target)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "GNbw-iR0ymwj",
-    "outputId": "888b397b-5370-4ae4-d2ba-98d54595ee72"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input : Прими сей череп, Дельвиг, он\n",
-      "Принадлежит тебе по праву.\n",
-      "Тебе поведаю, барон,\n",
-      "Его готическую славу.\n",
-      "\n",
-      "\n",
-      "Target: рими сей череп, Дельвиг, он\n",
-      "Принадлежит тебе по праву.\n",
-      "Тебе поведаю, барон,\n",
-      "Его готическую славу.\n",
-      "\n",
-      "П\n"
-     ]
-    }
-   ],
-   "source": [
-    "for input_example, target_example in val_dataset.take(1):\n",
-    "    print(\"Input :\", text_from_ids(input_example))\n",
-    "    print(\"Target:\", text_from_ids(target_example))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "MJdfPmdqzf-R"
-   },
-   "source": [
-    "Перемешиваем датасеты и разбиваем их на батчи для оптимизации обучения"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "p2pGotuNzf-S",
-    "outputId": "d9ae90cb-d904-4528-d0ed-eb11caeb43d3"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<PrefetchDataset element_spec=(TensorSpec(shape=(64, 100), dtype=tf.int64, name=None), TensorSpec(shape=(64, 100), dtype=tf.int64, name=None))>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Batch size\n",
-    "BATCH_SIZE = 64\n",
-    "\n",
-    "BUFFER_SIZE = 10000\n",
-    "\n",
-    "def prepare_dataset(dataset):\n",
-    "    dataset = (\n",
-    "        dataset\n",
-    "        .shuffle(BUFFER_SIZE)\n",
-    "        .batch(BATCH_SIZE, drop_remainder=True)\n",
-    "        .prefetch(tf.data.experimental.AUTOTUNE))\n",
-    "    return dataset \n",
-    "\n",
-    "train_dataset = prepare_dataset(train_dataset)\n",
-    "val_dataset = prepare_dataset(val_dataset)\n",
-    "test_dataset = prepare_dataset(test_dataset)\n",
-    "\n",
-    "train_dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "r6oUuElIMgVx"
-   },
-   "source": [
-    "# Нейросеть"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "22uVCbSyPBjD"
-   },
-   "source": [
-    "## Построение модели"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "m8gPwEjRzf-Z"
-   },
-   "source": [
-    "Модель состоит из трех слоев\n",
-    "\n",
-    "* `tf.keras.layers.Embedding`: Входной слой. Кодирует каждый идентификатор символа в  вектор размерностью `embedding_dim`; \n",
-    "* `tf.keras.layers.GRU`: Рекуррентный слой на ячейках GRU. Выходной вектор размерностью `units=rnn_units` **(Здесь нужно указать тип ячеек в соответствии с вариантом)**\n",
-    "* `tf.keras.layers.Dense`: Выходной полносвязный слой размерностью `vocab_size`, в который выводится вероятность каждого символа в словаре. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "id": "zHT8cLh7EAsg"
-   },
-   "outputs": [],
-   "source": [
-    "# Длина словаря символов\n",
-    "vocab_size = len(vocab)\n",
-    "\n",
-    "# размерность Embedding'а\n",
-    "embedding_dim = 20 #@param{type:\"number\"}\n",
-    "\n",
-    "# Параметры RNN-слоя\n",
-    "rnn_units = 300 #@param {type:\"number\"}\n",
-    "dropout_p = 0.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "id": "wj8HQ2w8z4iO"
-   },
-   "outputs": [],
-   "source": [
-    "class MyModel(tf.keras.Model):\n",
-    "  def __init__(self, vocab_size, embedding_dim, rnn_units):\n",
-    "    super().__init__(self)\n",
-    "    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n",
-    "    self.gru = tf.keras.layers.GRU(rnn_units,\n",
-    "                                   dropout = dropout_p,\n",
-    "                                   return_sequences=True,\n",
-    "                                   return_state=True)\n",
-    "    self.dense = tf.keras.layers.Dense(vocab_size)\n",
-    "\n",
-    "  def call(self, inputs, states=None, return_state=False, training=False):\n",
-    "    x = inputs\n",
-    "    x = self.embedding(x, training=training)\n",
-    "    \n",
-    "    if states is None:\n",
-    "        states = self.gru.get_initial_state(x)\n",
-    "\n",
-    "    x, *states = self.gru(x, initial_state=states, training=training)\n",
-    "    x = self.dense(x, training=training)\n",
-    "\n",
-    "    if return_state:\n",
-    "      return x, states\n",
-    "    else:\n",
-    "      return x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "id": "IX58Xj9z47Aw"
-   },
-   "outputs": [],
-   "source": [
-    "model = MyModel(\n",
-    "    vocab_size=len(ids_from_chars.get_vocabulary()),\n",
-    "    embedding_dim=embedding_dim,\n",
-    "    rnn_units=rnn_units)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "RkA5upJIJ7W7"
-   },
-   "source": [
-    "Иллюстрация работы сети\n",
-    "\n",
-    "![A drawing of the data passing through the model](https://github.com/tensorflow/text/blob/master/docs/tutorials/images/text_generation_training.png?raw=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LdgaUC3tPHAy"
-   },
-   "source": [
-    "## Проверка необученой модели"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "C-_70kKAPrPU",
-    "outputId": "02de9cf7-29d5-4345-8e02-b8c940ca5c1a"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(64, 100, 144) # (batch_size, sequence_length, vocab_size)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# посмотрим на один батч из датасета\n",
-    "for input_example_batch, target_example_batch in train_dataset.take(1):\n",
-    "    example_batch_predictions = model(input_example_batch)\n",
-    "    print(example_batch_predictions.shape, \"# (batch_size, sequence_length, vocab_size)\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uwv0gEkURfx1"
-   },
-   "source": [
-    "prediction() предсказывает логиты вероятности каждого символа на следующей позиции. При этом, если мы будем выбирать символ с максимальной вероятностью, то из раза в раз модель нам будет выдавать один и тот же текст. \n",
-    "Чтобы этого избежать, нужно выбирать очередной индекс из распределения\n",
-    "`tf.random.categorical` - чем выше значение на выходном слое полносвязной сети, тем вероятнее, что данный символ будет выбран в качестве очередного. Однако, это не обязательно будет символ с максимальной вероятностью.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "W6_G5P7W1TrE",
-    "outputId": "5fdc06a0-9be3-42b4-8054-454e997586ee"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(144,), dtype=float32, numpy=\n",
-       "array([ 7.04948464e-03, -1.98002765e-03,  3.02454224e-03, -1.94069953e-03,\n",
-       "        9.08686779e-05, -1.26269402e-03, -3.95816471e-03,  5.32332528e-03,\n",
-       "       -1.58743770e-03,  3.80896078e-03,  4.99830581e-03, -3.53646278e-03,\n",
-       "       -3.50173237e-03,  1.03672473e-02,  4.72654402e-03,  4.48969286e-03,\n",
-       "       -7.25119235e-03,  4.08414472e-03, -6.61874050e-03, -4.53183288e-03,\n",
-       "        1.36306381e-03, -8.01230408e-03,  4.31668153e-03,  4.37820982e-03,\n",
-       "       -4.18623677e-03, -3.56584322e-03,  2.71524256e-03, -1.40747754e-04,\n",
-       "       -2.54452089e-03, -6.52383547e-03,  9.91060492e-03, -5.61330421e-03,\n",
-       "       -8.06922559e-04, -1.45252305e-03, -1.25302584e-04,  2.54331576e-03,\n",
-       "       -6.65406231e-03, -3.14406748e-03, -1.42855803e-03,  4.38266993e-03,\n",
-       "        4.06480394e-05,  3.58506199e-03, -1.31492107e-03, -3.76690924e-03,\n",
-       "       -4.61246073e-03,  2.09019426e-03, -6.05216657e-04,  4.94315475e-03,\n",
-       "       -7.38915848e-03, -2.97222170e-04,  1.23252645e-02, -3.27007566e-03,\n",
-       "        7.75653915e-03, -2.52670376e-03, -7.69718783e-04, -2.05170992e-03,\n",
-       "        1.18606293e-03,  6.22191140e-03, -4.58527543e-03,  4.21849283e-04,\n",
-       "        7.42580276e-03,  8.36860761e-03,  4.63361526e-03,  2.71662371e-03,\n",
-       "        2.59269890e-03, -2.30735121e-03,  6.44756807e-03, -2.63488246e-03,\n",
-       "       -1.40141640e-02,  1.11143891e-04, -5.53287193e-03, -6.43183826e-04,\n",
-       "       -1.15646292e-02, -1.41452125e-04,  1.50612544e-03,  2.67576054e-03,\n",
-       "       -3.87344277e-03, -2.01095594e-03,  3.92267248e-03,  2.47437716e-03,\n",
-       "        7.02369632e-03,  1.57978013e-03, -6.72074873e-03,  5.20235533e-03,\n",
-       "       -7.60141760e-03, -4.12424142e-03, -1.24696689e-03, -5.03823441e-03,\n",
-       "        2.94092577e-03,  2.38715275e-03, -1.54204969e-03, -2.16306839e-03,\n",
-       "       -2.10713898e-03, -3.79565591e-03, -5.14758425e-03, -3.90997529e-03,\n",
-       "       -4.30819113e-03, -7.28532323e-04, -2.43837340e-03,  2.76774517e-03,\n",
-       "       -1.66824413e-03, -2.95873499e-03,  5.87470829e-04, -7.67468195e-03,\n",
-       "       -2.17538618e-04, -8.66387784e-03, -4.71618492e-03, -1.63393712e-03,\n",
-       "       -4.77423018e-04, -1.49550312e-03, -3.46688367e-03,  1.21447584e-03,\n",
-       "        1.32047385e-03,  1.50095311e-03,  1.62522192e-03, -3.79677466e-03,\n",
-       "        1.57164666e-03, -4.10819892e-03, -3.00779007e-03,  2.36973306e-03,\n",
-       "       -3.77465365e-03, -5.34315477e-05,  1.84188155e-03,  3.53988446e-03,\n",
-       "        6.51812530e-04, -7.78108952e-05,  4.54718899e-03,  1.03609345e-03,\n",
-       "       -2.62774178e-03, -9.84960934e-05, -6.15748437e-03,  1.74808409e-03,\n",
-       "       -6.90540811e-03,  4.51742392e-03,  2.42682174e-03, -9.07061435e-03,\n",
-       "       -4.85235034e-03,  7.50885578e-04,  6.84963632e-03, -3.08787916e-04,\n",
-       "        2.59267096e-03,  4.30960301e-03,  3.37237539e-03,  3.88880167e-03],\n",
-       "      dtype=float32)>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example_batch_predictions[0][0]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0yJ5Je4J1XRb"
-   },
-   "source": [
-    "![image.png](data:image/png;base64,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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UuAT_ymD15U7"
-   },
-   "source": [
-    "На картинке отмечены наиболее вероятные символы."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "id": "4V4MfFg0RQJg"
-   },
-   "outputs": [],
-   "source": [
-    "sampled_indices = tf.random.categorical(example_batch_predictions[0], num_samples=1)\n",
-    "sampled_indices = tf.squeeze(sampled_indices, axis=-1).numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "YqFMUQc_UFgM",
-    "outputId": "1842194b-d7d7-4702-bca5-6b03f04b9432"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([103,   2, 125, 127,  46, 128,  85,  84,  14,  37,  55,   7, 129,\n",
-       "       123,  72,  38, 138,  88, 116, 125, 142, 109, 110, 131,  21,  29,\n",
-       "        15,  99, 118,  48,  15, 143, 106, 139,  28, 115, 119,   8,  41,\n",
-       "        55, 138,  68, 130, 133, 135,   4, 114,  10,  62, 130, 120,  47,\n",
-       "       119,  16,  87,  71,  32, 111, 121,  85,  13,  87,  87,  75,  25,\n",
-       "        91,  41,  83,  51, 106, 100, 133,   3,   9,  37,  36, 103,  61,\n",
-       "         2,  79, 136,  56,  99, 101,  20, 143,  70, 117,  60,  43,   6,\n",
-       "        49,  51,  15,  85, 115, 113, 138,  23,  89], dtype=int64)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sampled_indices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "xWcFwPwLSo05",
-    "outputId": "bed36421-88f9-429f-a9cd-099717127029"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input:\n",
-      " же прошли. Их мненья, толки, страсти\n",
-      "Забыты для других. Смотри: вокруг тебя\n",
-      "Всё новое кипит, былое и\n",
-      "\n",
-      "Next Char Predictions:\n",
-      " Э ухfцИЗ;Vo)чсèWёЛку…гдщDN<Цмh<</s>а–Mйн*aoё»шыэ\"и-vшоgн>КçQепИ:ККôIОaЖkаЧы!,VUЭu ВюpЦШC</s>âлtc(ik<ИйзёFМ\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Input:\\n\", text_from_ids(input_example_batch[0]))\n",
-    "print()\n",
-    "print(\"Next Char Predictions:\\n\", text_from_ids(sampled_indices))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LJL0Q0YPY6Ee"
-   },
-   "source": [
-    "## Обучение модели"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "YCbHQHiaa4Ic"
-   },
-   "source": [
-    "\n",
-    "Можно представить задачу как задачу классификации - по предыдущему состоянию RNN и входу в данный момент времени предсказать класс (очередной символ). "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "trpqTWyvk0nr"
-   },
-   "source": [
-    "### Настройка оптимизатора и функции потерь"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UAjbjY03eiQ4"
-   },
-   "source": [
-    "В этом случае работает стандартная функция потерь `tf.keras.losses.sparse_categorical_crossentropy`- кроссэнтропия, которая равна минус логарифму предсказанной вероятности для верного класса.\n",
-    "\n",
-    "Поскольку модель возвращает логиты, вам необходимо установить флаг `from_logits`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "id": "ZOeWdgxNFDXq"
-   },
-   "outputs": [],
-   "source": [
-    "loss = tf.losses.SparseCategoricalCrossentropy(from_logits=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "4HrXTACTdzY-",
-    "outputId": "f0dbf6f2-738e-48f8-df7c-0bbf60fc17e4"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prediction shape:  (64, 100, 144)  # (batch_size, sequence_length, vocab_size)\n",
-      "Mean loss:         tf.Tensor(4.970122, shape=(), dtype=float32)\n"
-     ]
-    }
-   ],
-   "source": [
-    "example_batch_mean_loss = loss(target_example_batch, example_batch_predictions)\n",
-    "print(\"Prediction shape: \", example_batch_predictions.shape, \" # (batch_size, sequence_length, vocab_size)\")\n",
-    "print(\"Mean loss:        \", example_batch_mean_loss)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vkvUIneTFiow"
-   },
-   "source": [
-    "Необученная модель не может делать адекватные предсказания. Ее перплексия («коэффициент неопределённости») приблизительно равна размеру словаря. Это говорит о полной неопределенности модели при генерации текста.\n",
-    "\n",
-    "Перплексия = exp(кроссэнтропия)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "MAJfS5YoFiHf",
-    "outputId": "0588a9b2-8598-458f-db1d-6923e6a50502"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "perplexity:  144.04443\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('perplexity: ',np.exp(example_batch_mean_loss))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "jeOXriLcymww"
-   },
-   "source": [
-    "Настраиваем обучение, используя метод `tf.keras.Model.compile`. Используйте `tf.keras.optimizers.Adam` с аргументами по умолчанию и функцией потерь."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "id": "DDl1_Een6rL0"
-   },
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer='adam', loss=loss)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "vPGmAAXmVLGC",
-    "outputId": "1399a58c-85ff-430d-f67f-942942dec071"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"my_model\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " embedding (Embedding)       multiple                  2880      \n",
-      "                                                                 \n",
-      " gru (GRU)                   multiple                  289800    \n",
-      "                                                                 \n",
-      " dense (Dense)               multiple                  43344     \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 336,024\n",
-      "Trainable params: 336,024\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "C6XBUUavgF56"
-   },
-   "source": [
-    "Используем `tf.keras.callbacks.ModelCheckpoint`, чтобы убедиться, что контрольные точки сохраняются во время обучения:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "id": "W6fWTriUZP-n"
-   },
-   "outputs": [],
-   "source": [
-    "# Directory where the checkpoints will be saved\n",
-    "checkpoint_dir = './training_checkpoints'\n",
-    "# Name of the checkpoint files\n",
-    "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")\n",
-    "\n",
-    "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
-    "    filepath=checkpoint_prefix,\n",
-    "    monitor=\"val_loss\",\n",
-    "    save_weights_only=True,\n",
-    "    save_best_only=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "3Ky3F_BhgkTW"
-   },
-   "source": [
-    "### Обучение!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Обратим внимание, что перед очередным обучением нужно сбросить веса модели. Проще всего это сделать, заново объявив и скомпилировав модель:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'MyModel' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mMyModel\u001b[49m(\n\u001b[0;32m      2\u001b[0m     vocab_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(ids_from_chars\u001b[38;5;241m.\u001b[39mget_vocabulary()),\n\u001b[0;32m      3\u001b[0m     embedding_dim\u001b[38;5;241m=\u001b[39membedding_dim,\n\u001b[0;32m      4\u001b[0m     rnn_units\u001b[38;5;241m=\u001b[39mrnn_units)\n\u001b[0;32m      5\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39mloss)\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'MyModel' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "model = MyModel(\n",
-    "    vocab_size=len(ids_from_chars.get_vocabulary()),\n",
-    "    embedding_dim=embedding_dim,\n",
-    "    rnn_units=rnn_units)\n",
-    "model.compile(optimizer='adam', loss=loss)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "id": "7yGBE2zxMMHs"
-   },
-   "outputs": [],
-   "source": [
-    "EPOCHS = 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "UK-hmKjYVoll",
-    "outputId": "4403459e-c93d-4361-b6b4-d4f4a29797e5"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5\n",
-      "72/72 [==============================] - 24s 301ms/step - loss: 3.7387 - val_loss: 3.4320\n",
-      "Epoch 2/5\n",
-      "72/72 [==============================] - 22s 294ms/step - loss: 3.2194 - val_loss: 2.8863\n",
-      "Epoch 3/5\n",
-      "72/72 [==============================] - 22s 292ms/step - loss: 2.8698 - val_loss: 2.7059\n",
-      "Epoch 4/5\n",
-      "72/72 [==============================] - 23s 309ms/step - loss: 2.7643 - val_loss: 2.6365\n",
-      "Epoch 5/5\n",
-      "72/72 [==============================] - 24s 320ms/step - loss: 2.6941 - val_loss: 2.5800\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit(train_dataset, validation_data = val_dataset, epochs=EPOCHS, callbacks=[checkpoint_callback])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "UejMtXPmH-U0",
-    "outputId": "ec06daed-25a1-4ae3-9f9a-cd130e04c216"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "9/9 [==============================] - 1s 101ms/step - loss: 2.5769\n",
-      "eval loss: 2.576871871948242\n",
-      "perplexity 13.155920322524834\n"
-     ]
-    }
-   ],
-   "source": [
-    "eval_loss = model.evaluate(test_dataset)\n",
-    "print('eval loss:',eval_loss)\n",
-    "print('perplexity',np.exp(eval_loss))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kKkD5M6eoSiN"
-   },
-   "source": [
-    "## Генерация текста"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "oIdQ8c8NvMzV"
-   },
-   "source": [
-    "Самый простой способ сгенерировать текст с помощью этой модели — запустить ее в цикле и отслеживать внутреннее состояние модели по мере ее выполнения.\n",
-    "\n",
-    "![To generate text the model's output is fed back to the input](https://github.com/tensorflow/text/blob/master/docs/tutorials/images/text_generation_sampling.png?raw=1)\n",
-    "\n",
-    "Каждый раз, когда вы вызываете модель, вы передаете некоторый текст и внутреннее состояние. Модель возвращает прогноз для следующего символа и его нового состояния. Передайте предсказание и состояние обратно, чтобы продолжить создание текста.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DjGz1tDkzf-u"
-   },
-   "source": [
-    "Создаем модель реализующую один шаг предсказания:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "id": "iSBU1tHmlUSs"
-   },
-   "outputs": [],
-   "source": [
-    "class OneStep(tf.keras.Model):\n",
-    "  def __init__(self, model, chars_from_ids, ids_from_chars, temperature=1.0):\n",
-    "    super().__init__()\n",
-    "    self.temperature = temperature\n",
-    "    self.model = model\n",
-    "    self.chars_from_ids = chars_from_ids\n",
-    "    self.ids_from_chars = ids_from_chars\n",
-    "\n",
-    "    # Create a mask to prevent \"[UNK]\" from being generated.\n",
-    "    skip_ids = self.ids_from_chars(['[UNK]'])[:, None]\n",
-    "    sparse_mask = tf.SparseTensor(\n",
-    "        # Put a -inf at each bad index.\n",
-    "        values=[-float('inf')]*len(skip_ids),\n",
-    "        indices=skip_ids,\n",
-    "        # Match the shape to the vocabulary\n",
-    "        dense_shape=[len(ids_from_chars.get_vocabulary())])\n",
-    "    self.prediction_mask = tf.sparse.to_dense(sparse_mask)\n",
-    "\n",
-    "    \n",
-    "  # Этот фрагмент целиком написан с использованием Tensorflow, поэтому его можно выполнять \n",
-    "  # не с помощью интерпретатора языка Python, а через граф операций. Это будет значительно быстрее.  \n",
-    "  # Для этого воспользуемся декоратором  @tf.function   \n",
-    "  @tf.function   \n",
-    "  def generate_one_step(self, inputs, states=None,temperature=1.0):\n",
-    "    # Convert strings to token IDs.\n",
-    "    input_chars = tf.strings.unicode_split(inputs, 'UTF-8')\n",
-    "    input_ids = self.ids_from_chars(input_chars).to_tensor()\n",
-    "\n",
-    "    # Run the model.\n",
-    "    # predicted_logits.shape is [batch, char, next_char_logits]\n",
-    "    predicted_logits, states = self.model(inputs=input_ids, states=states,\n",
-    "                                          return_state=True)\n",
-    "    # Only use the last prediction.\n",
-    "    predicted_logits = predicted_logits[:, -1, :]\n",
-    "    predicted_logits = predicted_logits/temperature\n",
-    "    # Apply the prediction mask: prevent \"[UNK]\" from being generated.\n",
-    "    predicted_logits = predicted_logits + self.prediction_mask\n",
-    "\n",
-    "    # Sample the output logits to generate token IDs.\n",
-    "    predicted_ids = tf.random.categorical(predicted_logits, num_samples=1)\n",
-    "    predicted_ids = tf.squeeze(predicted_ids, axis=-1)\n",
-    "\n",
-    "    # Convert from token ids to characters\n",
-    "    predicted_chars = self.chars_from_ids(predicted_ids)\n",
-    "\n",
-    "\n",
-    "    # Return the characters and model state.\n",
-    "    return predicted_chars, states"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "id": "fqMOuDutnOxK"
-   },
-   "outputs": [],
-   "source": [
-    "one_step_model = OneStep(model, chars_from_ids, ids_from_chars)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "p9yDoa0G3IgQ"
-   },
-   "source": [
-    "Изменяя температуру можно регулировать вариативность текста"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "ST7PSyk9t1mT",
-    "outputId": "ed0dfecc-3d31-4bfe-efeb-d2315044ef06"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "uКочаму,\n",
-      "Лыт\n",
-      "Нума Гве!\n",
-      "àvо етекы ва;\n",
-      "Хо> вы,\n",
-      "Муметы,.\n",
-      "Воцу:\n",
-      "ОтекаЕдраэмь эзоспосанах…Я кы.\n",
-      ") маа Почи защоючеты>\n",
-      "Алаций поши)\"Пры – /й пий лазой\n",
-      "Еже?\n",
-      "И,\n",
-      "В, à*</s>\n",
-      "logJ\"nДаогей piКо '<vЯйвы\n",
-      "Срый\n",
-      "За Sрыша, Жузогой!\n",
-      "jу,\n",
-      "yВлаж:.\n",
-      "jPsдотети;\n",
-      "Улум!\n",
-      "Яро\n",
-      "Не жь вый…\n",
-      "На…нуше нухотоло!\n",
-      "érфосчатей пойхоскох,\n",
-      "ûzСда –\n",
-      "âШаналемоны узени потя,\n",
-      "„Я OaéТый с говощащаранымозужмелы,\n",
-      "Титой !\n",
-      "И Жычима пруМепобай>\n",
-      "Дя цый,;\n",
-      "Зази:\n",
-      "Унумыйй поль ь,\n",
-      "Тост,,,\n",
-      "Дежы!Шоны\n",
-      "H-му,\n",
-      "iБачуй,,;цо,\n",
-      "Тахрей\n",
-      "Ты а;\n",
-      "Памогой елыны…\n",
-      "Жебы\n",
-      "Ве яны!\n",
-      "Экычигруй,\n",
-      "И вьчам ре кабощалуй, ль наша водух, ё ко,\n",
-      "Чячу бемий>\n",
-      "Елетанех гружара, eалодищи:\n",
-      "И,\n",
-      "Гро вишемороцемапа\"oОмицы\n",
-      "upЛицль: Преща, Оды iH бочавоча!, Ценобый ни\n",
-      "Вныла,\n",
-      "Чу:.\n",
-      "Ввуй,\n",
-      "eРаДобе по!\n",
-      "Qтый,\n",
-      "Чки fХралошумо:\n",
-      "na-Койкаца –\n",
-      "Ай\n",
-      "Мо,\n",
-      "i)цыденатедубодех выйха\n",
-      "Полынене на бы.\n",
-      "Заму рыхасла cИмино Одорафута уга гоЮй вожве,\n",
-      "ГчаCКазко.\n",
-      "Мотя выйхоруnАчлатоный\n",
-      "Рабойх удапи,\n",
-      "Ты уТашь,\n",
-      "Обымаекоги вайзоты зой кишаметумилетелы\n",
-      "Я, —\n",
-      "yJи, солыFлы!éuХуZетце, ё\n",
-      "oПробемнанетече ей.\n",
-      "Ри,\n",
-      "Шоный.\n",
-      "По!\n",
-      "Ностыгу!\n",
-      "Hv\n",
-      "________________________________________________________________________________\n",
-      "\n",
-      "Run time: 1.620434284210205\n"
-     ]
-    }
-   ],
-   "source": [
-    "T = 0.5 #@param {type:\"slider\", min:0, max:2, step:0.1}\n",
-    "N = 1000\n",
-    "\n",
-    "start = time.time()\n",
-    "states = None\n",
-    "next_char = tf.constant(['\\n'])\n",
-    "result = [next_char]\n",
-    "\n",
-    "for n in range(N):\n",
-    "  next_char, states = one_step_model.generate_one_step(next_char, states=states,temperature=T)\n",
-    "  result.append(next_char)\n",
-    "\n",
-    "result = tf.strings.join(result)\n",
-    "end = time.time()\n",
-    "\n",
-    "result_text = result[0].numpy().decode('utf-8')\n",
-    "print(result_text)\n",
-    "print('_'*80)\n",
-    "print('\\nRun time:', end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 300
-    },
-    "id": "VCwWY9xM6KCB",
-    "outputId": "e709c661-8bbe-4abf-e329-db9af7e6eb55"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>len</th>\n",
-       "      <th>lines</th>\n",
-       "      <th>mean_line_len</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>499.000000</td>\n",
-       "      <td>35.500000</td>\n",
-       "      <td>12.123718</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>482.246825</td>\n",
-       "      <td>33.234019</td>\n",
-       "      <td>1.262820</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>158.000000</td>\n",
-       "      <td>12.000000</td>\n",
-       "      <td>11.230769</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>328.500000</td>\n",
-       "      <td>23.750000</td>\n",
-       "      <td>11.677244</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>499.000000</td>\n",
-       "      <td>35.500000</td>\n",
-       "      <td>12.123718</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>669.500000</td>\n",
-       "      <td>47.250000</td>\n",
-       "      <td>12.570192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>840.000000</td>\n",
-       "      <td>59.000000</td>\n",
-       "      <td>13.016667</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              len      lines  mean_line_len\n",
-       "count    2.000000   2.000000       2.000000\n",
-       "mean   499.000000  35.500000      12.123718\n",
-       "std    482.246825  33.234019       1.262820\n",
-       "min    158.000000  12.000000      11.230769\n",
-       "25%    328.500000  23.750000      11.677244\n",
-       "50%    499.000000  35.500000      12.123718\n",
-       "75%    669.500000  47.250000      12.570192\n",
-       "max    840.000000  59.000000      13.016667"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "describe_poems(result_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "db7UJQr9ILfW"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "22rnSwqqICn2"
-   },
-   "source": [
-    "По мотивам https://colab.research.google.com/github/tensorflow/text/blob/master/docs/tutorials/text_generation.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}