Natural Language Generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа по курсу NLP. Natural Language Generation\n",
    "## Репин Владимир 382006-3м"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import (\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    TextDataset,\n",
    "    DataCollatorForLanguageModeling,\n",
    "    TrainingArguments,\n",
    "    Trainer,\n",
    "    pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Используем одну из доступных русскоязычных моделей, способных генерировать текст:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2/resolve/main/config.json from cache at C:\\Users\\mezotaken/.cache\\huggingface\\transformers\\06f48b6b3173390d047e15d691fda67ae4ea7733a5eea4b6e0115f5099c4e700.b5cdfa39c63384f94159c36bc9042660c747cea5cf520b43d543bd2c68b3164d\n",
      "Model config GPT2Config {\n",
      "  \"_name_or_path\": \"sberbank-ai/rugpt3small_based_on_gpt2\",\n",
      "  \"activation_function\": \"gelu_new\",\n",
      "  \"architectures\": [\n",
      "    \"GPT2LMHeadModel\"\n",
      "  ],\n",
      "  \"attn_pdrop\": 0.1,\n",
      "  \"bos_token_id\": 50256,\n",
      "  \"embd_pdrop\": 0.1,\n",
      "  \"eos_token_id\": 50256,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"layer_norm_epsilon\": 1e-05,\n",
      "  \"model_type\": \"gpt2\",\n",
      "  \"n_ctx\": 2048,\n",
      "  \"n_embd\": 768,\n",
      "  \"n_head\": 12,\n",
      "  \"n_inner\": null,\n",
      "  \"n_layer\": 12,\n",
      "  \"n_positions\": 2048,\n",
      "  \"reorder_and_upcast_attn\": false,\n",
      "  \"resid_pdrop\": 0.1,\n",
      "  \"scale_attn_by_inverse_layer_idx\": false,\n",
      "  \"scale_attn_weights\": true,\n",
      "  \"summary_activation\": null,\n",
      "  \"summary_first_dropout\": 0.1,\n",
      "  \"summary_proj_to_labels\": true,\n",
      "  \"summary_type\": \"cls_index\",\n",
      "  \"summary_use_proj\": true,\n",
      "  \"transformers_version\": \"4.14.1\",\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50264\n",
      "}\n",
      "\n",
      "loading weights file https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2/resolve/main/pytorch_model.bin from cache at C:\\Users\\mezotaken/.cache\\huggingface\\transformers\\df2b64a4c86a349ba84354d85b7117b106f2b87085c9bb54cde70d3751907c45.4e3da19dd8adaa6d6a9804bfd45d2dcf17ba544de445847443ef1816bfa3d693\n",
      "All model checkpoint weights were used when initializing GPT2LMHeadModel.\n",
      "\n",
      "All the weights of GPT2LMHeadModel were initialized from the model checkpoint at sberbank-ai/rugpt3small_based_on_gpt2.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use GPT2LMHeadModel for predictions without further training.\n",
      "loading configuration file https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2/resolve/main/config.json from cache at C:\\Users\\mezotaken/.cache\\huggingface\\transformers\\06f48b6b3173390d047e15d691fda67ae4ea7733a5eea4b6e0115f5099c4e700.b5cdfa39c63384f94159c36bc9042660c747cea5cf520b43d543bd2c68b3164d\n",
      "Model config GPT2Config {\n",
      "  \"_name_or_path\": \"sberbank-ai/rugpt3small_based_on_gpt2\",\n",
      "  \"activation_function\": \"gelu_new\",\n",
      "  \"architectures\": [\n",
      "    \"GPT2LMHeadModel\"\n",
      "  ],\n",
      "  \"attn_pdrop\": 0.1,\n",
      "  \"bos_token_id\": 50256,\n",
      "  \"embd_pdrop\": 0.1,\n",
      "  \"eos_token_id\": 50256,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"layer_norm_epsilon\": 1e-05,\n",
      "  \"model_type\": \"gpt2\",\n",
      "  \"n_ctx\": 2048,\n",
      "  \"n_embd\": 768,\n",
      "  \"n_head\": 12,\n",
      "  \"n_inner\": null,\n",
      "  \"n_layer\": 12,\n",
      "  \"n_positions\": 2048,\n",
      "  \"reorder_and_upcast_attn\": false,\n",
      "  \"resid_pdrop\": 0.1,\n",
      "  \"scale_attn_by_inverse_layer_idx\": false,\n",
      "  \"scale_attn_weights\": true,\n",
      "  \"summary_activation\": null,\n",
      "  \"summary_first_dropout\": 0.1,\n",
      "  \"summary_proj_to_labels\": true,\n",
      "  \"summary_type\": \"cls_index\",\n",
      "  \"summary_use_proj\": true,\n",
      "  \"transformers_version\": \"4.14.1\",\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50264\n",
      "}\n",
      "\n",
      "loading weights file https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2/resolve/main/pytorch_model.bin from cache at C:\\Users\\mezotaken/.cache\\huggingface\\transformers\\df2b64a4c86a349ba84354d85b7117b106f2b87085c9bb54cde70d3751907c45.4e3da19dd8adaa6d6a9804bfd45d2dcf17ba544de445847443ef1816bfa3d693\n",
      "All model checkpoint weights were used when initializing GPT2LMHeadModel.\n",
      "\n",
      "All the weights of GPT2LMHeadModel were initialized from the model checkpoint at sberbank-ai/rugpt3small_based_on_gpt2.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use GPT2LMHeadModel for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "anecmodel = AutoModelForCausalLM.from_pretrained(\"sberbank-ai/rugpt3small_based_on_gpt2\")\n",
    "witchermodel = AutoModelForCausalLM.from_pretrained(\"sberbank-ai/rugpt3small_based_on_gpt2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для правильной работы необходимо использовать токенайзер связанный с моделью:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"sberbank-ai/rugpt3small_based_on_gpt2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Использованный датасет\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для fine-tune модели используется два разных датасета: полная серия книг \"Ведьмак\" и большой сборник анекдотов."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для загрузки датасета рекомендуется использовать библиотеку \"datasets\", но она упорно не устанавливается, поэтому пока данная версия работает - пользуюсь ей. Здесь функция, пользуясь заданным токенайзером делит текст на блоки равного размера и снабжает нужными для data collator методами, чтобы всё это подать для обучения модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Soft\\Anaconda\\lib\\site-packages\\transformers\\data\\datasets\\language_modeling.py:54: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py\n",
      "  warnings.warn(\n",
      "Creating features from dataset file at \n",
      "Saving features into cached file cached_lm_GPT2TokenizerFast_128_anectrain.txt [took 0.003 s]\n",
      "Creating features from dataset file at \n",
      "Saving features into cached file cached_lm_GPT2TokenizerFast_128_anecvalid.txt [took 0.001 s]\n"
     ]
    }
   ],
   "source": [
    "anec_train_dataset = TextDataset(\n",
    "    tokenizer=tokenizer,\n",
    "    file_path='anectrain.txt',\n",
    "    block_size=128)\n",
    "     \n",
    "anec_valid_dataset = TextDataset(\n",
    "    tokenizer=tokenizer,\n",
    "    file_path='anecvalid.txt',\n",
    "    block_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "witcher_train_dataset = TextDataset(\n",
    "    tokenizer=tokenizer,\n",
    "    file_path='witchertrain.txt',\n",
    "    block_size=128)\n",
    "     \n",
    "witcher_valid_dataset = TextDataset(\n",
    "    tokenizer=tokenizer,\n",
    "    file_path='witchervalid.txt',\n",
    "    block_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tune модели (анекдоты)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Модель Сбербанка основана на gpt2, которая использует Causual Language Modelling, генерацию следующего токена по последовательности. Поэтому в data collator для обучения выключаем Masked Language Modelling, где генерируется последовательность токенов вместо уникального токена маски, включённого в промт."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Устанавливаем необходимые для обучения параметры и создаём объект библиотеки huggingface, который и используется, чтобы обучить модель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
     ]
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    overwrite_output_dir = True,\n",
    "    output_dir = 'anecmodel',\n",
    "    per_device_train_batch_size = 1,\n",
    "    per_device_eval_batch_size = 1,\n",
    "    learning_rate = 1e-5,\n",
    "    num_train_epochs = 2,\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model = anecmodel,\n",
    "    args = training_args,\n",
    "    data_collator = data_collator,\n",
    "    train_dataset = anec_train_dataset,\n",
    "    eval_dataset = anec_valid_dataset\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running training *****\n",
      "  Num examples = 1038\n",
      "  Num Epochs = 2\n",
      "  Instantaneous batch size per device = 1\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 1\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 2076\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='2076' max='2076' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [2076/2076 06:35, Epoch 2/2]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>3.248100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>3.175400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>2.946900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>2.949000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to anecmodel\\checkpoint-500\n",
      "Configuration saved in anecmodel\\checkpoint-500\\config.json\n",
      "Model weights saved in anecmodel\\checkpoint-500\\pytorch_model.bin\n",
      "Saving model checkpoint to anecmodel\\checkpoint-1000\n",
      "Configuration saved in anecmodel\\checkpoint-1000\\config.json\n",
      "Model weights saved in anecmodel\\checkpoint-1000\\pytorch_model.bin\n",
      "Saving model checkpoint to anecmodel\\checkpoint-1500\n",
      "Configuration saved in anecmodel\\checkpoint-1500\\config.json\n",
      "Model weights saved in anecmodel\\checkpoint-1500\\pytorch_model.bin\n",
      "Saving model checkpoint to anecmodel\\checkpoint-2000\n",
      "Configuration saved in anecmodel\\checkpoint-2000\\config.json\n",
      "Model weights saved in anecmodel\\checkpoint-2000\\pytorch_model.bin\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=2076, training_loss=3.075748597955428, metrics={'train_runtime': 395.9946, 'train_samples_per_second': 5.242, 'train_steps_per_second': 5.242, 'total_flos': 135610564608000.0, 'train_loss': 3.075748597955428, 'epoch': 2.0})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сохраним модель и создадим объект для генерации текста из абстракции pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to anecmodel\n",
      "Configuration saved in anecmodel\\config.json\n",
      "Model weights saved in anecmodel\\pytorch_model.bin\n"
     ]
    }
   ],
   "source": [
    "trainer.save_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file anecmodel\\config.json\n",
      "Model config GPT2Config {\n",
      "  \"_name_or_path\": \"anecmodel\",\n",
      "  \"activation_function\": \"gelu_new\",\n",
      "  \"architectures\": [\n",
      "    \"GPT2LMHeadModel\"\n",
      "  ],\n",
      "  \"attn_pdrop\": 0.1,\n",
      "  \"bos_token_id\": 50256,\n",
      "  \"embd_pdrop\": 0.1,\n",
      "  \"eos_token_id\": 50256,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"layer_norm_epsilon\": 1e-05,\n",
      "  \"model_type\": \"gpt2\",\n",
      "  \"n_ctx\": 2048,\n",
      "  \"n_embd\": 768,\n",
      "  \"n_head\": 12,\n",
      "  \"n_inner\": null,\n",
      "  \"n_layer\": 12,\n",
      "  \"n_positions\": 2048,\n",
      "  \"reorder_and_upcast_attn\": false,\n",
      "  \"resid_pdrop\": 0.1,\n",
      "  \"scale_attn_by_inverse_layer_idx\": false,\n",
      "  \"scale_attn_weights\": true,\n",
      "  \"summary_activation\": null,\n",
      "  \"summary_first_dropout\": 0.1,\n",
      "  \"summary_proj_to_labels\": true,\n",
      "  \"summary_type\": \"cls_index\",\n",
      "  \"summary_use_proj\": true,\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.14.1\",\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50264\n",
      "}\n",
      "\n",
      "loading configuration file anecmodel\\config.json\n",
      "Model config GPT2Config {\n",
      "  \"_name_or_path\": \"anecmodel\",\n",
      "  \"activation_function\": \"gelu_new\",\n",
      "  \"architectures\": [\n",
      "    \"GPT2LMHeadModel\"\n",
      "  ],\n",
      "  \"attn_pdrop\": 0.1,\n",
      "  \"bos_token_id\": 50256,\n",
      "  \"embd_pdrop\": 0.1,\n",
      "  \"eos_token_id\": 50256,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"layer_norm_epsilon\": 1e-05,\n",
      "  \"model_type\": \"gpt2\",\n",
      "  \"n_ctx\": 2048,\n",
      "  \"n_embd\": 768,\n",
      "  \"n_head\": 12,\n",
      "  \"n_inner\": null,\n",
      "  \"n_layer\": 12,\n",
      "  \"n_positions\": 2048,\n",
      "  \"reorder_and_upcast_attn\": false,\n",
      "  \"resid_pdrop\": 0.1,\n",
      "  \"scale_attn_by_inverse_layer_idx\": false,\n",
      "  \"scale_attn_weights\": true,\n",
      "  \"summary_activation\": null,\n",
      "  \"summary_first_dropout\": 0.1,\n",
      "  \"summary_proj_to_labels\": true,\n",
      "  \"summary_type\": \"cls_index\",\n",
      "  \"summary_use_proj\": true,\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.14.1\",\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50264\n",
      "}\n",
      "\n",
      "loading weights file anecmodel\\pytorch_model.bin\n",
      "All model checkpoint weights were used when initializing GPT2LMHeadModel.\n",
      "\n",
      "All the weights of GPT2LMHeadModel were initialized from the model checkpoint at anecmodel.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use GPT2LMHeadModel for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "anecgenerator = pipeline('text-generation', tokenizer=tokenizer, model='anecmodel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Подходит бандит к мужику  и говорит:\n",
      "- Ты что, с ума сошел? Я тебе дам пистолет!!. А ты мне - бутылку водки. Ну давай...\n",
      "\n",
      "Мужик достает из кармана пачку денег (в рублях), протягивает бандиту и спрашивает у него паспорт : \"А вы кто такой будете?\" Тот отвечает ему в ответ: \"Я русский\". И тут мужик начинает стрелять по нему со всех стволов...\n",
      "\n",
      "Бандит падает замертво от страха.\n"
     ]
    }
   ],
   "source": [
    "print(anecgenerator('Подходит бандит к мужику и', max_length=100,repetition_penalty=1.35)[0]['generated_text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tune модели (Ведьмак)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
     ]
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    overwrite_output_dir = True,\n",
    "    output_dir = 'witchermodel',\n",
    "    per_device_train_batch_size = 1,\n",
    "    per_device_eval_batch_size = 1,\n",
    "    learning_rate = 3e-5,\n",
    "    num_train_epochs = 1,\n",
    "    save_total_limit = 1\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model = witchermodel,\n",
    "    args = training_args,\n",
    "    data_collator = data_collator,\n",
    "    train_dataset = witcher_train_dataset,\n",
    "    eval_dataset = witcher_valid_dataset\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running training *****\n",
      "  Num examples = 10468\n",
      "  Num Epochs = 1\n",
      "  Instantaneous batch size per device = 1\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 1\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 10468\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='10468' max='10468' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [10468/10468 33:21, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>3.634200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>3.609300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>3.534100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>3.538900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2500</td>\n",
       "      <td>3.513000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>3.458400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3500</td>\n",
       "      <td>3.456700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>3.457700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4500</td>\n",
       "      <td>3.447100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5000</td>\n",
       "      <td>3.376900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5500</td>\n",
       "      <td>3.419100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6000</td>\n",
       "      <td>3.388000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6500</td>\n",
       "      <td>3.362800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7000</td>\n",
       "      <td>3.362700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7500</td>\n",
       "      <td>3.370400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8000</td>\n",
       "      <td>3.352900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8500</td>\n",
       "      <td>3.278300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9000</td>\n",
       "      <td>3.316100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9500</td>\n",
       "      <td>3.317900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10000</td>\n",
       "      <td>3.320500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to witchermodel\\checkpoint-500\n",
      "Configuration saved in witchermodel\\checkpoint-500\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-500\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-8000] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-1000\n",
      "Configuration saved in witchermodel\\checkpoint-1000\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-1000\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-500] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-1500\n",
      "Configuration saved in witchermodel\\checkpoint-1500\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-1500\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-1000] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-2000\n",
      "Configuration saved in witchermodel\\checkpoint-2000\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-2000\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-1500] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-2500\n",
      "Configuration saved in witchermodel\\checkpoint-2500\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-2500\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-2000] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-3000\n",
      "Configuration saved in witchermodel\\checkpoint-3000\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-3000\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-2500] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-3500\n",
      "Configuration saved in witchermodel\\checkpoint-3500\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-3500\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-3000] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-4000\n",
      "Configuration saved in witchermodel\\checkpoint-4000\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-4000\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-3500] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-4500\n",
      "Configuration saved in witchermodel\\checkpoint-4500\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-4500\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-4000] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-5000\n",
      "Configuration saved in witchermodel\\checkpoint-5000\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-5000\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-4500] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-5500\n",
      "Configuration saved in witchermodel\\checkpoint-5500\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-5500\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-5000] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-6000\n",
      "Configuration saved in witchermodel\\checkpoint-6000\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-6000\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-5500] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-6500\n",
      "Configuration saved in witchermodel\\checkpoint-6500\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-6500\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-6000] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-7000\n",
      "Configuration saved in witchermodel\\checkpoint-7000\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-7000\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-6500] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-7500\n",
      "Configuration saved in witchermodel\\checkpoint-7500\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-7500\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-7000] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-8000\n",
      "Configuration saved in witchermodel\\checkpoint-8000\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-8000\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-7500] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-8500\n",
      "Configuration saved in witchermodel\\checkpoint-8500\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-8500\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-8000] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-9000\n",
      "Configuration saved in witchermodel\\checkpoint-9000\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-9000\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-8500] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-9500\n",
      "Configuration saved in witchermodel\\checkpoint-9500\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-9500\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-9000] due to args.save_total_limit\n",
      "Saving model checkpoint to witchermodel\\checkpoint-10000\n",
      "Configuration saved in witchermodel\\checkpoint-10000\\config.json\n",
      "Model weights saved in witchermodel\\checkpoint-10000\\pytorch_model.bin\n",
      "Deleting older checkpoint [witchermodel\\checkpoint-9500] due to args.save_total_limit\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=10468, training_loss=3.4215032965981145, metrics={'train_runtime': 2001.8402, 'train_samples_per_second': 5.229, 'train_steps_per_second': 5.229, 'total_flos': 683801247744000.0, 'train_loss': 3.4215032965981145, 'epoch': 1.0})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to witchermodel\n",
      "Configuration saved in witchermodel\\config.json\n",
      "Model weights saved in witchermodel\\pytorch_model.bin\n"
     ]
    }
   ],
   "source": [
    "trainer.save_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "witchergenerator = pipeline('text-generation', tokenizer=tokenizer, model='witchermodel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Что такое огонь?  Это пламя, которое горит в огне.\n",
      "\n",
      "– Огонь – это вода, – сказал Геральт. – Вода – это огонь. А огонь… Он не может быть холодным и холодным. Не может быть холодным и холодным.\n",
      "\n",
      "– Холодным и холодным огнем, – повторил Лютик. – Огнем, который сжигает все живое на свете. И даже людей. Но ты же знаешь, что я имею в виду. Я знаю, как выглядит огонь. Ты можешь его увидеть. Увидеть, как он пылает. Как горят деревья, цветы, листья, трава, небо, звезды. Все, что есть огня.\n",
      "\n",
      "– Нет! – крикнул ведьмак. – Нет! Этого нет!\n",
      "\n",
      "– Да будет так, – сказала Цири. – Так пусть этот огонь погаснет. Пусть он погасит то, чего мы боимся больше всего.\n",
      "\n",
      "Она подняла руку, коснулась пламени, но оно было слишком мягким для нее. Она\n"
     ]
    }
   ],
   "source": [
    "print(witchergenerator('- Что такое огонь? ', max_length=200,repetition_penalty=1.2)[0]['generated_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Что такое огонь?  Это пламя, которое горит в огне.\n",
      "\n",
      "– Огонь – это вода! Вода… Она не может быть водой и огнем. Не должна сгореть вместе с тобой на этом свете. Ты должен ее спасти от огня. Я знаю тебя лучше всех. И я помогу тебе сделать то же самое со мной. Только ты можешь меня уберечь».\n",
      "\n",
      "Он молчал долго. Потом сказал: «Я хочу знать все о том пламени…» Он знал об огненном потоке воды из озера Брокилон. О воде, которая течет по его лицу сквозь пальцы. Об озере, который он видел во сне. Озеро было черным как смоль, а небо затянуто тучами молний. Но Цири знала также, что озеро действительно черное. А она видела воду под ногами у него за спиной. В темноте. На дне пруда. Под ним была черная лужа крови. Кровь текла прямо ему навстречу. У нее были руки, ноги, волосы цвета воронова крыла. Ее глаза\n"
     ]
    }
   ],
   "source": [
    "print(witchergenerator('- Что такое огонь? ', max_length=200,repetition_penalty=1.45)[0]['generated_text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тут смешные промты получить сложно, но это и не было целью. Интересно было посмотреть, как близко к стилю книг получится сгенерированный текст, а оказалось очень близко, не хватает только смысла. \n",
    "\n",
    "Также стоит отметить, что штрафом за повторения можно регулировать диалоги и описательный текст, мне кажется потому, что штрафуются дефисы, которые в диалогах часто повторяются."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Забавные результаты от модели на анекдотах"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Мужик приходит домой пьяный после работы к жене со словами :\n",
    "\n",
    "\\- Жена, а давай купим машину за полцены? Я уже купил ее вчера вечером, но она стоит того чтобы купить её сегодня утром..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заходит улитка в бар и спрашивает у барменши:\n",
    "\n",
    "\\- Скажите, а вы не знаете почему я такая толстая? Я же ем только бананы!\n",
    "\n",
    "Барменша отвечает ей с улыбкой. Она говорит : \"Потому что ты ешь их каждый день\".\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Врач: \"У вас есть минутки?\"\n",
    "\n",
    "Мужик: \"Да\". Врач: \"А сколько вам лет?\". Мужик: \"Двадцать один год... . Врач: \"И что же вы делаете с этими часами?!\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заходят в бар русский, американец и еврей.\n",
    "\\- Ну что? - спрашивает бармен у русского:\n",
    "\\- Я заказал пиво \"Бургер Кинг\"..\n",
    "\n",
    "Русский отвечает:\n",
    "\n",
    "\"Давай по сто грамм\", а потом добавляет еще два слова на иврите: \"Пьяный я! \". И тут же поворачивается к еврею спиной...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Врач:\n",
    "\\- У вас есть сахарный диабет или ожирение?\n",
    "\n",
    "Мужик (с улыбкой):\n",
    "\n",
    "\\- Нету сахара!!!\n",
    "\n",
    "доктор:\n",
    "\n",
    "\\- Ну и дурак же ты, мужик!!!! Ты хоть бы ложку принес!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как доехать до Нижнего Новгорода?\n",
    "\\- А что, есть варианты?!?."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Дополнительно"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Промт от оригинальной модели Сбербанка"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Наверное, это даже лучше, чем анекдоты"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тексты выше сгенерированы уже в финальной версии, но была ещё самая первая, где датасетом служила смесь ведмака и анекдотов(из другого сборника) в пропорции 1:4. Потом уже стало ясно, что это бессмысленно, учитывая как работает модель. Тренировка там была очень быстрой, пробной, на малом сабсете и результаты были очень странными. Сохранилось это в виде скринов, которые пересылались паре друзей, не участвовавших в курсе."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Крайне странные результаты из той же первой пробы"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}