Bielik_Data_Generation_and_Fewshot_Prompting_(4_bit).ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "  <img src=\"https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png\">\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vf7Ls3Ae9R3p"
   },
   "source": [
    "## Pobieranie Pakietów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "KFJQZk9TGWLK"
   },
   "outputs": [],
   "source": [
    "!pip install accelerate -q\n",
    "!pip install -i https://pypi.org/simple/ bitsandbytes -q\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L9e_pzqA9gLQ"
   },
   "source": [
    "Pakiet accelerate jest wymagany przez inne pakiety do efektywnego uruchamiania modeli na wielu kartach graficznych/TPU.\n",
    "<br><br>\n",
    "bitsandbytes pozwoli nam na załadowanie modelu w trybie ograniczonej precyzji wag. Prowadzi to do istotnego zmniejszenia wymagań pamięciowych modelu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H2Kh_9gQEsBz"
   },
   "source": [
    "## Ładowanie Bielika"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "FcjkzCnUrz-t"
   },
   "outputs": [],
   "source": [
    "import pandas as pd  # Working with dataset\n",
    "import torch  # Deep learning framework\n",
    "\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from huggingface_hub import hf_hub_download  # Downloading Poquad dataset from hugginface manually.\n",
    "\n",
    "import json  # Saving new dataset\n",
    "import warnings\n",
    "\n",
    "from math import ceil\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "noN5Bq4r-j_Q"
   },
   "source": [
    "Poniższa komórka:\n",
    "- pobiera model bielik, ładuje do pamięci wagi modelu oraz tokenizera\n",
    "- Tworzy ustawienia generowania (tj temperature, max_tokens, top_k, top_p).\n",
    "- Ustawia niższą precyzję reprezentowania wag (kwantyzacja) za pomocą BitsAndBytes.\n",
    "-A także sprawia, że tekst generowany przez model będzie wyświetlał się w czasie rzeczywistym (TextStreamer)."
   ]
  },
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      "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
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    {
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      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer, Trainer, Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
    "\n",
    "device = \"cuda\"\n",
    "model_name = 'speakleash/Bielik-7B-Instruct-v0.1'\n",
    "\n",
    "\n",
    "temperature = 1.0\n",
    "max_tokens = 500\n",
    "top_k = 200\n",
    "top_p = 1\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
    "\n",
    "quantization_config = BitsAndBytesConfig(\n",
    "      load_in_4bit=True,\n",
    "      bnb_4bit_compute_dtype=torch.bfloat16\n",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
    "                                            torch_dtype=torch.bfloat16,\n",
    "                                            quantization_config=quantization_config\n",
    "                                            )\n",
    "\n",
    "model.generation_config.pad_token_id = tokenizer.pad_token_id\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9rMlx0oi_ibN"
   },
   "source": [
    "Poniższa komórka pozwala nam uprościć generowanie tekstu przez model do postaci pojedynczej funkcji generate, przyjmującej treść prompta oraz treść systemowego prompta. <br><br> Dzięki TextStreamerowi zdefiniowanemu wcześniej funkcja będzie wyświetlała generowany tekst w czasie rzeczywistym. Po wygenerowaniu całości funkcja zwraca tekst w postaci stringu (typ danych zmiennych tekstowych)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "nBO2W57Enigs"
   },
   "outputs": [],
   "source": [
    "def generate(prompt, system = None):\n",
    "\n",
    "  messages = []\n",
    "\n",
    "  if system:\n",
    "    messages.append( {\"role\": \"system\", \"content\": system })\n",
    "\n",
    "  messages.append( {\"role\": \"user\", \"content\": prompt })\n",
    "\n",
    "\n",
    "  tokenizer_output = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", return_dict=True)\n",
    "\n",
    "  if torch.cuda.is_available():\n",
    "    model_input_ids = tokenizer_output.input_ids.to(device)\n",
    "\n",
    "    model_attention_mask = tokenizer_output.attention_mask.to(device)\n",
    "\n",
    "  else:\n",
    "    model_input_ids = tokenizer_output.input_ids\n",
    "    model_attention_mask = tokenizer_output.attention_mask\n",
    "\n",
    "\n",
    "  # generowanie odpowiedzi\n",
    "  outputs = model.generate(model_input_ids,\n",
    "                           attention_mask=model_attention_mask,\n",
    "                           streamer = streamer,\n",
    "                           max_new_tokens=max_tokens,\n",
    "                           do_sample=True if temperature else False,\n",
    "                           temperature = temperature,\n",
    "                           top_k = top_k,\n",
    "                           top_p = top_p)\n",
    "\n",
    "  return tokenizer.batch_decode(outputs, skip_special_tokens=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rqnl8GsFx4Zy",
    "outputId": "7b230206-2e46-4ba5-a1b4-c0faa22474f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Czaszka składa się z 7 kości.\n"
     ]
    }
   ],
   "source": [
    "# Testowe działanie generowania.\n",
    "query = \"Z Ilu kości składa się czaszka?\"\n",
    "prompt = f\"Odpowiedz zwięźle na pytanie: '{query}'\"\n",
    "output = generate(prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XOAd-HOXjGrh"
   },
   "source": [
    "## Ładowanie Danych Q&A (Poquad) oraz tworzenie funkcji pomocnicznych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8zCnqFz4AENp"
   },
   "source": [
    "\"PoQuAD - The Polish Question Answering Dataset - Description and Analysis \"\n",
    "<br>Ryszard Tuora, Aleksandra Zwierzchowska, Natalia Zawadzka-Paluektau, Cezary Klamra, Łukasz Kobyliński\n",
    "<br> <br>\n",
    "Ze strony datasetu:\n",
    "\"Systemy QA (Question Answering) są ważnym zastosowaniem technik przetwarzania języka naturalnego. Pozwalają na wyszukiwanie informacji w dużych zbiorach niestrukturyzowanych tekstów w najbardziej naturalny sposób, bez konieczności formułowania zapytania wyszukiwania. Współczesny standard w Question Answering polega na dostosowywaniu ogólnych modeli językowych do zadaniowych zbiorów danych. Zadanie może być sformułowane na różne sposoby, w zależności od wykorzystania zasobów tekstowych i formy ostatecznej odpowiedzi. Jednym z popularniejszych formatów jest tzw. Machine Reading Comprehension (MRC 33), gdzie odpowiedź na pytanie musi być oparta na zrozumieniu dostarczonego fragmentu tekstu. PoQuAd jest pierwszym zbiorem danych MRC w języku polskim. Dane zostały zebrane z Wikipedii, a dzięki procesowi starannej anotacji udało się uzyskać ponad 70 tysięcy punktów danych.\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MkoXo8hSAyrk"
   },
   "source": [
    "Poquad jest wymagającym datasetem testujący umiejętności modelu w znajdowaniu informacji w tekście, a także w krytycznym czytaniu tekstów ze zrozumieniem. Główna trudność tych danych polega na istnieniu przykładów pytań, na które nie da się odpowiedzieć na podstawie tekstu. W takich przypadkach model powinien wstrzymywać się przed udzielaniem odpowiedzi. Wymaga do głębokiego zrozumienia tekstu oraz umiejętności analizy informacji znajdujących się w tekście. Bielik tak samo jak większość innych modeli o podobnym rozmiarze, ma wielkie trudności we wstrzymaniu się przed generowaniem odpowiedzi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dHPQn4IAB4gX"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VaUuOmYkB7LW"
   },
   "source": [
    "Poniższe 3 komórki pobierają dane z serwisu hugginface. W sposób nieautomatyczny ładują je do postaci tabeli danych, a na końcu tworzą nową kolumnę w danych odpowiadającą odpowiedzi, którą oczekujemy od modelu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67,
     "referenced_widgets": [
      "4cc6fe84fa554878a7a29fb7d0b8d2e8",
      "44c87f1e535e4cc89776dbb7dd68ba44",
      "ce651294a1c644509780dccbbdb7a8e9",
      "38e5923d753b48fa9b5f3dc27bfa9e1c",
      "85eb9339d97d415b95da90f01c184390",
      "0e148482f1e44f0fa33f5cca10f5dc19",
      "c492b7c213b54bbfa10a9ce65301cc19",
      "28e10d96b0cc4108b974c754dad62a84",
      "28d1cc9c7fa145f2833319d00d608fd2",
      "3abcb59d077041409b157068050d98a0",
      "0124909cdbda49d8aeec652f4b2ecd9e"
     ]
    },
    "id": "QeRczbhXQ1X6",
    "outputId": "ec4cf13d-f457-440b-d715-77e13887a3b9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4cc6fe84fa554878a7a29fb7d0b8d2e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "poquad-train.json:   0%|          | 0.00/47.2M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'data/poquad-train.json'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf_hub_download(repo_id=\"clarin-pl/poquad\", filename=\"poquad-train.json\", repo_type=\"dataset\", local_dir=\"./data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "rGwK3hQPNWWc"
   },
   "outputs": [],
   "source": [
    "def read_poquad_data(filepath):\n",
    "        whole_data = []\n",
    "        with open(filepath, encoding=\"utf-8\") as f:\n",
    "            squad = json.load(f)\n",
    "            id_ = 0\n",
    "            for example in squad[\"data\"]:\n",
    "                title = example.get(\"title\", \"\")\n",
    "                for paragraph in example[\"paragraphs\"]:\n",
    "                    context = paragraph[\"context\"]\n",
    "                    for qa in paragraph[\"qas\"]:\n",
    "                        question = qa[\"question\"]\n",
    "\n",
    "                        answer_starts = [answer[\"answer_start\"] for answer in qa[\"answers\"]] if \"answers\" in qa else []\n",
    "\n",
    "                        answers = [answer[\"text\"] for answer in qa[\"answers\"]] if \"answers\" in qa else []\n",
    "                        is_impossible = qa[\"is_impossible\"]\n",
    "\n",
    "                        id_ += 1\n",
    "                        whole_data.append({\n",
    "                            \"id\": id_,\n",
    "                            \"title\": title,\n",
    "                            \"context\": context,\n",
    "                            \"question\": question,\n",
    "                            \"is_impossible\" : is_impossible,\n",
    "                            \"answers\": {\n",
    "                                \"answer_start\": answer_starts,\n",
    "                                \"text\": answers,\n",
    "                            },\n",
    "                        })\n",
    "\n",
    "        return pd.DataFrame(whole_data).set_index(\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "ETM63OHxSP62"
   },
   "outputs": [],
   "source": [
    "df = read_poquad_data(\"./data/poquad-train.json\")\n",
    "\n",
    "df[\"target\"] = df[\"answers\"].apply(lambda x: \"Odpowiedź: \" + x[\"text\"][0] if x[\"text\"] else \"Odpowiedź niemożliwa!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h3VCmMmQCgYh"
   },
   "source": [
    "Poniższy kod tworzy funkcje, które ułatwią nam (oraz modelowi) interakcję z danymi. Pierwsza z nich to print_poquad_row(), która pozwala nam na spojrzenie na dowolny wiersz w danych w sposób czytelny i uporządkowany. Druga z nich to create_prompt_from_row(), która pozwala nam na automatyczne stworzenie promptu dla bielika, na podstawie numeru wiersza z danych.\n",
    "\n",
    "W tej komórce znajduje się również few_shot_text, czyli zmienna przechowująca przydatne przykłady, z których bielik może nauczyć się udzielać poprawnie sformatowanej odpowiedzi (in-context learning). <br> Podczas konstruowania tych demonstracji, zaproponowałem metody wnioskowania w oparciu o 4 wiersze z datasetu. Te 4 przykłady wybrałem samodzielnie z danych, tak aby miały możliwie jak najkrótsze konteksty. W ten sposób mogłem zmieścić 4 pełne przykłady jednocześnie utrzymując rozmiar prompta na poziomie, który bezproblemowo mieści się w modelu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "pNOQeOs1VOnc"
   },
   "outputs": [],
   "source": [
    "def print_using_shorter_lines(words_list, word_limit):\n",
    "  n_fragments = ceil(len(words_list)/word_limit)\n",
    "\n",
    "  for i in range(n_fragments):\n",
    "    print(\" \".join(words_list[12*i:12*(i+1)]))\n",
    "\n",
    "\n",
    "def print_poquad_row(df, i, n_words_in_line=12):\n",
    "  print(\"Kontekst:\")\n",
    "\n",
    "  context = df.loc[i, \"context\"].split(\" \")\n",
    "  print_using_shorter_lines(context, n_words_in_line)\n",
    "\n",
    "  print(\"\\nPytanie:\")\n",
    "  question = df.loc[i, \"question\"].split(\" \")\n",
    "  print_using_shorter_lines(question, n_words_in_line)\n",
    "\n",
    "  print(\"\\nOczekiwana odpowiedź:\")\n",
    "  answer = df.loc[i, \"target\"].split(\" \")\n",
    "  print_using_shorter_lines(answer, n_words_in_line)\n",
    "\n",
    "\n",
    "few_shot_text = f\"\"\"\n",
    "# PRZYKŁADOWE FORMY ODPOWIEDZI:\n",
    "Przykład 1)\n",
    "Kontekst: {df.loc[26070, \"context\"]}\n",
    "Pytanie: {df.loc[26070, \"question\"]}\n",
    "{df.loc[26070, \"target\"]}\n",
    "\n",
    "Wnioskowanie: Wyrażenie 'związane jest' wskazuje na wpływ przecięcia się szlaków kupiekcich z powstaniem Lubina.\n",
    "\n",
    "Przykład 2)\n",
    "Kontekst: {df.loc[25251, \"context\"]}\n",
    "Pytanie: {df.loc[25251, \"question\"]}\n",
    "{df.loc[25251, \"target\"]}\n",
    "\n",
    "Wnioskowanie: W tekście nie podano liczby głosów w wyborach na prezydenta stolicy, a jedynie informacje o ilości głosów w wyborach parlamentarnych. Pytano o wybory na prezydenta stolicy Polski, a więc brak informacji w tekście.\n",
    "\n",
    "Przykład 3)\n",
    "Kontekst: {df.loc[7900, \"context\"]}\n",
    "Pytanie: {df.loc[7900, \"question\"]}\n",
    "{df.loc[7900, \"target\"]}\n",
    "\n",
    "Wnioskowanie: W tekście informacja o liczbie posłów pojawia się w zdaniu: 'Do Sejmu Ustawodawczego weszło razem 36 posłów PPSD i PPS'. W tekście podano liczbę posłów PPSD i PPS razem wziętych. Nie da się podać tylko liczby posłów PPSD.\n",
    "\n",
    "Przykład 4)\n",
    "Kontekst: {df.loc[42005, \"context\"]}\n",
    "Pytanie: {df.loc[42005, \"question\"]}\n",
    "{df.loc[42005, \"target\"]}\n",
    "\n",
    "Wnioskowanie: Z tekstu wynika że Dąbrowski przejął dowodzenie po Sucharskim. W tym samym zdaniu Sucharskiemu przypisywane jest załamanie nerwowe. 'W wyniku' sugeruje wpływ załamania na zmianę dowodzenia. A więc odpowiedź to 'załamania nerwowego'/\n",
    "\n",
    "# KONIEC PRZYKŁADÓW.\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def create_prompt_from_row(df, i, answer=True):\n",
    "  task_clarification = \"udowodnij, że odpowiedź nie pojawia się w tekście, a także zwróć szczególną uwagę na możliwość istnienia dystraktorów odwracających uwagę od poprawnej odpowiedzi.\" if df.loc[i, \"target\"] == \"Odpowiedź niemożliwa!\" else \"we wnioskowaniu wyjaśnij dlaczego możemy mieć pewność, że odpowiedź prawidłowo odpowiada na zadane pytanie i znajduje się w tekście.\"\n",
    "\n",
    "  return (f\"Utwórz wnioskowanie argumentujące na rzecz podanej odpowiedzi, {task_clarification} Nie powtarzaj ani nie streszczaj treści kontekstu. Generuj wyłącznie oryginalny tekst. Pamiętaj, że każdy przykład wymaga innej metody argumentacji, a więc zadanie, które musisz rozwiązać będzie musiało wykorzystywać metody inne niż podane w przykładach.\"\n",
    "      + (few_shot_text if answer else \"\")\n",
    "      + \"\\n# Dane dla ciebie\"\n",
    "      + \"\\nKontekst: \"\n",
    "      + df.loc[i, \"context\"]\n",
    "      + \"\\nPytanie: \"\n",
    "      + df.loc[i, \"question\"]\n",
    "      + (\"\\n\" if answer else \"\")\n",
    "      + (df.loc[i, \"target\"] + \"\\n\" if answer else \"\"))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W1oZIEFEDUFa"
   },
   "source": [
    "### Demonstracja funkcji:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iHJ6kLju3V3x",
    "outputId": "cc158fbd-f64a-4325-9877-a7c8766e1491"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kontekst:\n",
      "Projekty konfederacji zaczęły się załamywać 5 sierpnia 1942. Ponownie wróciła kwestia monachijska,\n",
      "co uaktywniło się wymianą listów Ripka – Stroński. Natomiast 17 sierpnia 1942\n",
      "doszło do spotkania E. Beneša i J. Masaryka z jednej a Wł.\n",
      "Sikorskiego i E. Raczyńskiego z drugiej strony. Polscy dyplomaci zaproponowali podpisanie układu\n",
      "konfederacyjnego. W następnym miesiącu, tj. 24 września, strona polska przesłała na ręce\n",
      "J. Masaryka projekt deklaracji o przyszłej konfederacji obu państw. Strona czechosłowacka projekt\n",
      "przyjęła, lecz już w listopadzie 1942 E. Beneš podważył ideę konfederacji. W\n",
      "zamian zaproponowano zawarcie układu sojuszniczego z Polską na 20 lat (formalnie nastąpiło\n",
      "to 20 listopada 1942).\n",
      "\n",
      "Pytanie:\n",
      "Co było powodem powrócenia konceptu porozumieniu monachijskiego?\n",
      "\n",
      "Oczekiwana odpowiedź:\n",
      "Odpowiedź: wymianą listów Ripka – Stroński\n"
     ]
    }
   ],
   "source": [
    "print_poquad_row(df, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f_7wWOUbDa2S",
    "outputId": "6a6d753c-370e-4b93-c7d0-cad7664a4fe2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Utwórz wnioskowanie argumentujące na rzecz podanej odpowiedzi, we wnioskowaniu wyjaśnij dlaczego możemy mieć pewność, że odpowiedź prawidłowo odpowiada na zadane pytanie i znajduje się w tekście. Nie powtarzaj ani nie streszczaj treści kontekstu. Generuj wyłącznie oryginalny tekst. Pamiętaj, że każdy przykład wymaga innej metody argumentacji, a więc zadanie, które musisz rozwiązać będzie musiało wykorzystywać metody inne niż podane w przykładach.\n",
      "# PRZYKŁADOWE FORMY ODPOWIEDZI:\n",
      "Przykład 1)\n",
      "Kontekst: Powstanie pierwszej osady na terenie dzisiejszego Lubina (osiedle Stary Lubin) związane jest z przecięciem się w tym miejscu ważnych szlaków kupieckich (np. z Magdeburga poprzez Wrocław do Kijowa i z południa przez Wielkopolskę nad Morze Bałtyckie) w związku z czym była zapewne miejscem odpoczynku wędrujących kupców, a także handlu ich towarami. Nie do końca wiadomo, czy Lubinem władali wówczas Dziadoszanie czy Trzebowianie, gdyż leżał on w połowie drogi między ich głównymi grodami.\n",
      "Pytanie: Co spowodowało, że ludzie zasiedlili się na terytorium obecnego Lubina?\n",
      "Odpowiedź: przecięciem się w tym miejscu ważnych szlaków kupieckich\n",
      "\n",
      "Wnioskowanie: Wyrażenie 'związane jest' wskazuje na wpływ przecięcia się szlaków kupiekcich z powstaniem Lubina.\n",
      "\n",
      "Przykład 2)\n",
      "Kontekst: W 2001 wraz z Lechem Kaczyńskim współtworzył partię Prawo i Sprawiedliwość, która w wyborach parlamentarnych w 2001 zdobyła 9,5% głosów i 44 mandaty poselskie. Jarosław Kaczyński utrzymał miejsce w Sejmie, uzyskując w okręgu warszawskim 144 343 głosy. W latach 2001–2003 był członkiem Komisji Etyki oraz przewodniczącym klubu parlamentarnego PiS. W 2003 objął stanowisko prezesa tego ugrupowania, zastępując swojego brata, który został wybrany na urząd prezydenta miasta stołecznego Warszawy.\n",
      "Pytanie: Jaką liczbę głosów otrzymał w wyborach na prezydenta stolicy Polski?\n",
      "Odpowiedź niemożliwa!\n",
      "\n",
      "Wnioskowanie: W tekście nie podano liczby głosów w wyborach na prezydenta stolicy, a jedynie informacje o ilości głosów w wyborach parlamentarnych. Pytano o wybory na prezydenta stolicy Polski, a więc brak informacji w tekście.\n",
      "\n",
      "Przykład 3)\n",
      "Kontekst: Daszyński wystartował w pierwszych powojennych wyborach do polskiego Sejmu, używając hasła wyborczego Pierwszy Sejm Ustawodawczy to Pierwszy Gospodarz Ojczyzny, to jej budowniczy, źródło jej prawa i mocy. Sejm to Polska Wolna, Niepodległa i Zjednoczona. Do Sejmu Ustawodawczego weszło razem 36 posłów PPSD i PPS. Stworzyli oni klub parlamentarny, o nazwie Związek Polskich Posłów Socjalistycznych. Wkrótce potem Daszyński został jego prezesem. Wszedł on również w skład konwentu seniorów izby.\n",
      "Pytanie: Ilu członków PPSD dołączyło do składu Sejmu Ustawodawczego?\n",
      "Odpowiedź niemożliwa!\n",
      "\n",
      "Wnioskowanie: W tekście informacja o liczbie posłów pojawia się w zdaniu: 'Do Sejmu Ustawodawczego weszło razem 36 posłów PPSD i PPS'. W tekście podano liczbę posłów PPSD i PPS razem wziętych. Nie da się podać tylko liczby posłów PPSD.\n",
      "\n",
      "Przykład 4)\n",
      "Kontekst: 3 grudnia 1938, z rozkazu szefa Biura Personalnego Ministerstwa Spraw Wojskowych, został przeniesiony na stanowisko komendanta Wojskowej Składnicy Tranzytowej na Westerplatte, którą dowodził do 2 września 1939 roku. W wyniku załamania nerwowego Sucharskiego do momentu kapitulacji 7 września 1939 dowodzenie przejął kapitan Franciszek Dąbrowski. Tego dnia, o godzinie 10:15 major Henryk Sucharski zarządził wywieszenie białej flagi i osobiście udał się do Niemców, aby omówić warunki kapitulacji.\n",
      "Pytanie: Dlaczego zastąpił go Dąbrowski?\n",
      "Odpowiedź: załamania nerwowego\n",
      "\n",
      "Wnioskowanie: Z tekstu wynika że Dąbrowski przejął dowodzenie po Sucharskim. W tym samym zdaniu Sucharskiemu przypisywane jest załamanie nerwowe. 'W wyniku' sugeruje wpływ załamania na zmianę dowodzenia. A więc odpowiedź to 'załamania nerwowego'/\n",
      "\n",
      "# KONIEC PRZYKŁADÓW.\n",
      "\n",
      "# Dane dla ciebie\n",
      "Kontekst: Projekty konfederacji zaczęły się załamywać 5 sierpnia 1942. Ponownie wróciła kwestia monachijska, co uaktywniło się wymianą listów Ripka – Stroński. Natomiast 17 sierpnia 1942 doszło do spotkania E. Beneša i J. Masaryka z jednej a Wł. Sikorskiego i E. Raczyńskiego z drugiej strony. Polscy dyplomaci zaproponowali podpisanie układu konfederacyjnego. W następnym miesiącu, tj. 24 września, strona polska przesłała na ręce J. Masaryka projekt deklaracji o przyszłej konfederacji obu państw. Strona czechosłowacka projekt przyjęła, lecz już w listopadzie 1942 E. Beneš podważył ideę konfederacji. W zamian zaproponowano zawarcie układu sojuszniczego z Polską na 20 lat (formalnie nastąpiło to 20 listopada 1942).\n",
      "Pytanie: Co było powodem powrócenia konceptu porozumieniu monachijskiego?\n",
      "Odpowiedź: wymianą listów Ripka – Stroński\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(create_prompt_from_row(df, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "95IVDUyUjCSY"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TaNIC2UWjNcj"
   },
   "source": [
    "## Jak bielik wykonuje zadanie w sposób domyślny?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AYr5n9YkDw5c"
   },
   "source": [
    "Poniższe komórki prezentują jak Bielik radzi sobie z postawionym zadaniem bez finetuningu oraz z minimalnym wsparciem ze strony prompt engineeringu. Mają one na celu pokazać ograniczenia modeli językowych w sytuacjach gdy zadanie wymaga głębokiego zrozumienia tekstu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "kgIYcM953eNE"
   },
   "outputs": [],
   "source": [
    "temperature = 0\n",
    "max_tokens = 150\n",
    "top_k = 200\n",
    "top_p = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "qkzAnK9pa7a7"
   },
   "outputs": [],
   "source": [
    "prompt_possible = create_prompt_from_row(df, 1, answer=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2m2aWYisaEDX",
    "outputId": "9053b9d7-2cb2-49da-a180-b324910c01f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Odpowiedź: W sierpniu 1942, kwestia monachijska uaktywniła się poprzez wymianę listów Ripka - Stroński. To wznowienie dyskusji na temat porozumienia monachijskiego było prawdopodobnie spowodowane napięciami politycznymi i strategicznymi w regionie, a także potrzebą ustalenia wspólnych celów i zasad w obliczu rosnącego zagrożenia ze strony III Rzeszy.\n"
     ]
    }
   ],
   "source": [
    "system_prompt = \"\"\"Jesteś chatbotem odpowiadającym zwięźle na pytania na podstawie podanego kontekstu. Niektóre pytania mogą być niemożliwe, w takim wypadku musisz wstrzymać się od odpowiedzi. Twoje odpowiedzi mają przyjąć formę:\n",
    "\"Odpowiedź: 'treść odpowiedzi'\"\n",
    "jeśli treść odpowiedź istnieje w tekście\n",
    "lub\n",
    "\"Odpowiedź niemożliwa!\"\n",
    "jeśli nie da się odpowiedzieć na pytanie na podstawie kontekstu.\"\"\"\n",
    "\n",
    "output = generate(prompt=prompt_possible, system=system_prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h3mDGXcZiz30"
   },
   "source": [
    "**Model Generuje przesadnie rozległy tekst.**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Su3rMPt_i7yh"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MmXgr2B3hw-x",
    "outputId": "03d3c425-0cd6-4bb2-f4bf-b7cb4f087a66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kontekst:\n",
      "Samoloty Pomilio PD weszły na wyposażenie jednostek bojowych i zarazem do walki\n",
      "od lipca 1917 roku. Pierwsze otrzymały je w tym miesiącu eskadry (Squadriglia)\n",
      "nr 131 i 132, gdzie zastąpiły Pomilio PC. Do działań wchodziły następnie\n",
      "kolejne nowo formowane eskadry Pomilio: 133 (we wrześniu), 134 (w październiku), 135\n",
      "(w styczniu 1918 roku), 136 (w maju 1918 roku). Eskadra 139, sformowana\n",
      "z dwóch sekcji początkowo przydzielonych do lotnictwa Marynarki (od sierpnia 1917 roku),\n",
      "weszła do działań w styczniu 1918 roku, a w czerwcu odznaczyła się\n",
      "w bitwie nad Piawą, zrzucając 175 bomb. W bitwie tej brały udział\n",
      "też inne eskadry. Model PD został szybko zastąpiony w jednostkach przez PE\n",
      "i skierowany głównie do szkolenia.\n",
      "\n",
      "Pytanie:\n",
      "Ile bomb zrzucono na eskadrę 139?\n",
      "\n",
      "Oczekiwana odpowiedź:\n",
      "Odpowiedź niemożliwa!\n"
     ]
    }
   ],
   "source": [
    "print_poquad_row(df, 8) # Row showcase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "sl5MQt9YhtdW"
   },
   "outputs": [],
   "source": [
    "prompt_impossible = create_prompt_from_row(df, 8, answer=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gLDf4iAoh5bg",
    "outputId": "ad169ead-522d-4eb4-9796-30d703988c86"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Odpowiedź: 175 bomb\n",
      "\n",
      "Wyjaśnienie: W kontekście podanym, informacja o 175 bombach zrzuconych na eskadrę 139 przez tę jednostkę jest bezpośrednio wymieniona, co oznacza, że ta liczba bomb istotnie była zrzucona podczas bitwy nad Piawą w czerwcu 1918 roku. Dane te nie są wymienione wprost w odpowiedzi na pytanie o zestrzelenia i straty materialne w bitw\n"
     ]
    }
   ],
   "source": [
    "output = generate(prompt=prompt_impossible, system=system_prompt) # Should be \"Odpowiedź niemożliwa!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_P51tmjBiqLM"
   },
   "source": [
    "**Model Halucynuje.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WJWNrJWFi-Wx"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e_qcZpA0iaxw"
   },
   "source": [
    "## Generowanie przesłanek\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dRSMMpgOEalQ"
   },
   "source": [
    "Zadanie nie jest jednak poza zasięgiem AI. Dzięki wykorzystaniu zdolności modeli do generowania danych ulepszonych o dodatkowe informacje, istnieje możliwości wygenerowania datasetu wyższej jakości niż ten oryginalny oraz wytrenowanie/fine-tuning kolejnego modelu na ulepszonej wersji datasetu, co w rezultacie może istotnie poszerzyć możliwości modelu. <br><br>\n",
    "W praktyce na podstawie oryginalnych danych, poprosimy model o wygenerowanie procesu wnioskowania, które może pomóc w odnalezieniu poprawnej odpowiedzi (albo wstrzymanie się od odpowiedzi, kiedy ta nie istnieje). Potem te wnioski można dodać do oryginalnych danych, uzyskując poszerzony dataset. I w ostatnim kroku dotrenować model, tak aby na podstawie kontekstu nie tylko odpowiadał na pytania, ale też posiłkował się samodzielnie wygenerowanym procesem wnioskowania potencjalnie zwiększając swoje możliwości.\n",
    "\n",
    "**Uwaga! ze względu na wysokie wymagania obliczeniowe procesu data generation oraz finetuningu kolejne komórki będą jedynie demonstrowały całą koncepcję na przykładzie kilku obserwacji, stworzenie nowego modelu wymagałoby tysięcy danych powiększonych o procesy wnioskowania. A to wymagałoby godzin generowania tekstu.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "jReHC_4KiT8N"
   },
   "outputs": [],
   "source": [
    "system_prompt = f\"\"\"Jesteś AI wyjaśniającym dlaczego podane odpowiedzi odpowiadają na zadane pytanie do kontekstu. Czasem odpowiedzi są niemożliwe, w takim wypadku musisz wyjaśnić dlaczego odpowiedź nie znajduje się w tekście.\n",
    "Kiedy w tekście brakuje odpowiedzi na pytanie postaraj się wskazać dystraktory znajdujące się w tekście, które mogłyby mylnie wskazywać na bycie odpowiedzią. Np kiedy pytanie odnosi się do pewnego miejsca lub osoby, a tych miejsc i osób nie ma w tekście, to należy wskazać jakie miejsca lub osoby pojawiające się w tekście mogłyby udawać odpowiedź ale nią nie są.\n",
    "Zachowaj zwięzłą formę wypowiedzi, nie powtarzaj treści kontekstu, twoja odpowiedź musi składać się jedynie z procesu wnioskowania.\n",
    "Swoją wypowiedź musisz rozpocząć od wyrażenia 'Wnioskowanie: ', do którego w następnym kroku dołączasz swoją argumentację.\n",
    "Po wypełnieniu pola 'Wnioskowanie' należy zakończyć generowanie odpowiedzi. W szczególności nie możesz od nowa przytaczać całości ani fragmentów tekstu ani też pokazanych ci przykładów.\n",
    "\"\"\"\n",
    "\n",
    "# generate(prompt=prompt_possible, system=system_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qn_WkqkL2Jpt",
    "outputId": "8ff215a7-3399-4f5c-a68f-1cdb363ba8a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Utwórz wnioskowanie argumentujące na rzecz podanej odpowiedzi, we wnioskowaniu wyjaśnij dlaczego możemy mieć pewność, że odpowiedź prawidłowo odpowiada na zadane pytanie i znajduje się w tekście. Nie powtarzaj ani nie streszczaj treści kontekstu. Generuj wyłącznie oryginalny tekst. Pamiętaj, że każdy przykład wymaga innej metody argumentacji, a więc zadanie, które musisz rozwiązać będzie musiało wykorzystywać metody inne niż podane w przykładach.\n",
      "# PRZYKŁADOWE FORMY ODPOWIEDZI:\n",
      "Przykład 1)\n",
      "Kontekst: Powstanie pierwszej osady na terenie dzisiejszego Lubina (osiedle Stary Lubin) związane jest z przecięciem się w tym miejscu ważnych szlaków kupieckich (np. z Magdeburga poprzez Wrocław do Kijowa i z południa przez Wielkopolskę nad Morze Bałtyckie) w związku z czym była zapewne miejscem odpoczynku wędrujących kupców, a także handlu ich towarami. Nie do końca wiadomo, czy Lubinem władali wówczas Dziadoszanie czy Trzebowianie, gdyż leżał on w połowie drogi między ich głównymi grodami.\n",
      "Pytanie: Co spowodowało, że ludzie zasiedlili się na terytorium obecnego Lubina?\n",
      "Odpowiedź: przecięciem się w tym miejscu ważnych szlaków kupieckich\n",
      "\n",
      "Wnioskowanie: Wyrażenie 'związane jest' wskazuje na wpływ przecięcia się szlaków kupiekcich z powstaniem Lubina.\n",
      "\n",
      "Przykład 2)\n",
      "Kontekst: W 2001 wraz z Lechem Kaczyńskim współtworzył partię Prawo i Sprawiedliwość, która w wyborach parlamentarnych w 2001 zdobyła 9,5% głosów i 44 mandaty poselskie. Jarosław Kaczyński utrzymał miejsce w Sejmie, uzyskując w okręgu warszawskim 144 343 głosy. W latach 2001–2003 był członkiem Komisji Etyki oraz przewodniczącym klubu parlamentarnego PiS. W 2003 objął stanowisko prezesa tego ugrupowania, zastępując swojego brata, który został wybrany na urząd prezydenta miasta stołecznego Warszawy.\n",
      "Pytanie: Jaką liczbę głosów otrzymał w wyborach na prezydenta stolicy Polski?\n",
      "Odpowiedź niemożliwa!\n",
      "\n",
      "Wnioskowanie: W tekście nie podano liczby głosów w wyborach na prezydenta stolicy, a jedynie informacje o ilości głosów w wyborach parlamentarnych. Pytano o wybory na prezydenta stolicy Polski, a więc brak informacji w tekście.\n",
      "\n",
      "Przykład 3)\n",
      "Kontekst: Daszyński wystartował w pierwszych powojennych wyborach do polskiego Sejmu, używając hasła wyborczego Pierwszy Sejm Ustawodawczy to Pierwszy Gospodarz Ojczyzny, to jej budowniczy, źródło jej prawa i mocy. Sejm to Polska Wolna, Niepodległa i Zjednoczona. Do Sejmu Ustawodawczego weszło razem 36 posłów PPSD i PPS. Stworzyli oni klub parlamentarny, o nazwie Związek Polskich Posłów Socjalistycznych. Wkrótce potem Daszyński został jego prezesem. Wszedł on również w skład konwentu seniorów izby.\n",
      "Pytanie: Ilu członków PPSD dołączyło do składu Sejmu Ustawodawczego?\n",
      "Odpowiedź niemożliwa!\n",
      "\n",
      "Wnioskowanie: W tekście informacja o liczbie posłów pojawia się w zdaniu: 'Do Sejmu Ustawodawczego weszło razem 36 posłów PPSD i PPS'. W tekście podano liczbę posłów PPSD i PPS razem wziętych. Nie da się podać tylko liczby posłów PPSD.\n",
      "\n",
      "Przykład 4)\n",
      "Kontekst: 3 grudnia 1938, z rozkazu szefa Biura Personalnego Ministerstwa Spraw Wojskowych, został przeniesiony na stanowisko komendanta Wojskowej Składnicy Tranzytowej na Westerplatte, którą dowodził do 2 września 1939 roku. W wyniku załamania nerwowego Sucharskiego do momentu kapitulacji 7 września 1939 dowodzenie przejął kapitan Franciszek Dąbrowski. Tego dnia, o godzinie 10:15 major Henryk Sucharski zarządził wywieszenie białej flagi i osobiście udał się do Niemców, aby omówić warunki kapitulacji.\n",
      "Pytanie: Dlaczego zastąpił go Dąbrowski?\n",
      "Odpowiedź: załamania nerwowego\n",
      "\n",
      "Wnioskowanie: Z tekstu wynika że Dąbrowski przejął dowodzenie po Sucharskim. W tym samym zdaniu Sucharskiemu przypisywane jest załamanie nerwowe. 'W wyniku' sugeruje wpływ załamania na zmianę dowodzenia. A więc odpowiedź to 'załamania nerwowego'/\n",
      "\n",
      "# KONIEC PRZYKŁADÓW.\n",
      "\n",
      "# Dane dla ciebie\n",
      "Kontekst: Projekty konfederacji zaczęły się załamywać 5 sierpnia 1942. Ponownie wróciła kwestia monachijska, co uaktywniło się wymianą listów Ripka – Stroński. Natomiast 17 sierpnia 1942 doszło do spotkania E. Beneša i J. Masaryka z jednej a Wł. Sikorskiego i E. Raczyńskiego z drugiej strony. Polscy dyplomaci zaproponowali podpisanie układu konfederacyjnego. W następnym miesiącu, tj. 24 września, strona polska przesłała na ręce J. Masaryka projekt deklaracji o przyszłej konfederacji obu państw. Strona czechosłowacka projekt przyjęła, lecz już w listopadzie 1942 E. Beneš podważył ideę konfederacji. W zamian zaproponowano zawarcie układu sojuszniczego z Polską na 20 lat (formalnie nastąpiło to 20 listopada 1942).\n",
      "Pytanie: Pomiędzy jakimi stronami odbyło się zgromadzenie w sierpniu 1942 roku?\n",
      "Odpowiedź: E. Beneša i J. Masaryka z jednej a Wł. Sikorskiego i E. Raczyńskiego\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(create_prompt_from_row(df, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0qi67xmdm5he",
    "outputId": "50f6dce7-9c1b-438c-a1b9-5487368351f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Wnioskowanie: W sierpniu 1942 odbyło się spotkanie E. Beneša i J. Masaryka z jednej strony, a Wł. Sikorskiego i E. Raczyńskiego z drugiej strony. Z kontekstu wynika, że obie strony uczestniczyły w spotkaniu, dlatego odpowiedź to 'E. Beneša i J. Masaryka z jednej a Wł. Sikorskiego i E. Raczyńskiego'.\n"
     ]
    }
   ],
   "source": [
    "output = generate(prompt=create_prompt_from_row(df, 2), system=system_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eCOs9CEZxWbU",
    "outputId": "d794da42-dac7-4e2b-a873-6dcc053af796"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"[INST] <<SYS>>\\nJesteś AI wyjaśniającym dlaczego podane odpowiedzi odpowiadają na zadane pytanie do kontekstu. Czasem odpowiedzi są niemożliwe, w takim wypadku musisz wyjaśnić dlaczego odpowiedź nie znajduje się w tekście.\\nKiedy w tekście brakuje odpowiedzi na pytanie postaraj się wskazać dystraktory znajdujące się w tekście, które mogłyby mylnie wskazywać na bycie odpowiedzią. Np kiedy pytanie odnosi się do pewnego miejsca lub osoby, a tych miejsc i osób nie ma w tekście, to należy wskazać jakie miejsca lub osoby pojawiające się w tekście mogłyby udawać odpowiedź ale nią nie są.\\nZachowaj zwięzłą formę wypowiedzi, nie powtarzaj treści kontekstu, twoja odpowiedź musi składać się jedynie z procesu wnioskowania.\\nSwoją wypowiedź musisz rozpocząć od wyrażenia 'Wnioskowanie: ', do którego w następnym kroku dołączasz swoją argumentację.\\nPo wypełnieniu pola 'Wnioskowanie' należy zakończyć generowanie odpowiedzi. W szczególności nie możesz od nowa przytaczać całości ani fragmentów tekstu ani też pokazanych ci przykładów.\\n\\n<</SYS>>\\n\\nUtwórz wnioskowanie argumentujące na rzecz podanej odpowiedzi, we wnioskowaniu wyjaśnij dlaczego możemy mieć pewność, że odpowiedź prawidłowo odpowiada na zadane pytanie i znajduje się w tekście. Nie powtarzaj ani nie streszczaj treści kontekstu. Generuj wyłącznie oryginalny tekst. Pamiętaj, że każdy przykład wymaga innej metody argumentacji, a więc zadanie, które musisz rozwiązać będzie musiało wykorzystywać metody inne niż podane w przykładach.\\n# PRZYKŁADOWE FORMY ODPOWIEDZI:\\nPrzykład 1)\\nKontekst: Powstanie pierwszej osady na terenie dzisiejszego Lubina (osiedle Stary Lubin) związane jest z przecięciem się w tym miejscu ważnych szlaków kupieckich (np. z Magdeburga poprzez Wrocław do Kijowa i z południa przez Wielkopolskę nad Morze Bałtyckie) w związku z czym była zapewne miejscem odpoczynku wędrujących kupców, a także handlu ich towarami. Nie do końca wiadomo, czy Lubinem władali wówczas Dziadoszanie czy Trzebowianie, gdyż leżał on w połowie drogi między ich głównymi grodami.\\nPytanie: Co spowodowało, że ludzie zasiedlili się na terytorium obecnego Lubina?\\nOdpowiedź: przecięciem się w tym miejscu ważnych szlaków kupieckich\\n\\nWnioskowanie: Wyrażenie 'związane jest' wskazuje na wpływ przecięcia się szlaków kupiekcich z powstaniem Lubina.\\n\\nPrzykład 2)\\nKontekst: W 2001 wraz z Lechem Kaczyńskim współtworzył partię Prawo i Sprawiedliwość, która w wyborach parlamentarnych w 2001 zdobyła 9,5% głosów i 44 mandaty poselskie. Jarosław Kaczyński utrzymał miejsce w Sejmie, uzyskując w okręgu warszawskim 144\\xa0343 głosy. W latach 2001–2003 był członkiem Komisji Etyki oraz przewodniczącym klubu parlamentarnego PiS. W 2003 objął stanowisko prezesa tego ugrupowania, zastępując swojego brata, który został wybrany na urząd prezydenta miasta stołecznego Warszawy.\\nPytanie: Jaką liczbę głosów otrzymał w wyborach na prezydenta stolicy Polski?\\nOdpowiedź niemożliwa!\\n\\nWnioskowanie: W tekście nie podano liczby głosów w wyborach na prezydenta stolicy, a jedynie informacje o ilości głosów w wyborach parlamentarnych. Pytano o wybory na prezydenta stolicy Polski, a więc brak informacji w tekście.\\n\\nPrzykład 3)\\nKontekst: Daszyński wystartował w pierwszych powojennych wyborach do polskiego Sejmu, używając hasła wyborczego Pierwszy Sejm Ustawodawczy to Pierwszy Gospodarz Ojczyzny, to jej budowniczy, źródło jej prawa i mocy. Sejm to Polska Wolna, Niepodległa i Zjednoczona. Do Sejmu Ustawodawczego weszło razem 36 posłów PPSD i PPS. Stworzyli oni klub parlamentarny, o nazwie Związek Polskich Posłów Socjalistycznych. Wkrótce potem Daszyński został jego prezesem. Wszedł on również w skład konwentu seniorów izby.\\nPytanie: Ilu członków PPSD dołączyło do składu Sejmu Ustawodawczego?\\nOdpowiedź niemożliwa!\\n\\nWnioskowanie: W tekście informacja o liczbie posłów pojawia się w zdaniu: 'Do Sejmu Ustawodawczego weszło razem 36 posłów PPSD i PPS'. W tekście podano liczbę posłów PPSD i PPS razem wziętych. Nie da się podać tylko liczby posłów PPSD.\\n\\nPrzykład 4)\\nKontekst: 3 grudnia 1938, z rozkazu szefa Biura Personalnego Ministerstwa Spraw Wojskowych, został przeniesiony na stanowisko komendanta Wojskowej Składnicy Tranzytowej na Westerplatte, którą dowodził do 2 września 1939 roku. W wyniku załamania nerwowego Sucharskiego do momentu kapitulacji 7 września 1939 dowodzenie przejął kapitan Franciszek Dąbrowski. Tego dnia, o godzinie 10:15 major Henryk Sucharski zarządził wywieszenie białej flagi i osobiście udał się do Niemców, aby omówić warunki kapitulacji.\\nPytanie: Dlaczego zastąpił go Dąbrowski?\\nOdpowiedź: załamania nerwowego\\n\\nWnioskowanie: Z tekstu wynika że Dąbrowski przejął dowodzenie po Sucharskim. W tym samym zdaniu Sucharskiemu przypisywane jest załamanie nerwowe. 'W wyniku' sugeruje wpływ załamania na zmianę dowodzenia. A więc odpowiedź to 'załamania nerwowego'/\\n\\n# KONIEC PRZYKŁADÓW.\\n\\n# Dane dla ciebie\\nKontekst: Projekty konfederacji zaczęły się załamywać 5 sierpnia 1942. Ponownie wróciła kwestia monachijska, co uaktywniło się wymianą listów Ripka – Stroński. Natomiast 17 sierpnia 1942 doszło do spotkania E. Beneša i J. Masaryka z jednej a Wł. Sikorskiego i E. Raczyńskiego z drugiej strony. Polscy dyplomaci zaproponowali podpisanie układu konfederacyjnego. W następnym miesiącu, tj. 24 września, strona polska przesłała na ręce J. Masaryka projekt deklaracji o przyszłej konfederacji obu państw. Strona czechosłowacka projekt przyjęła, lecz już w listopadzie 1942 E. Beneš podważył ideę konfederacji. W zamian zaproponowano zawarcie układu sojuszniczego z Polską na 20 lat (formalnie nastąpiło to 20 listopada 1942).\\nPytanie: Pomiędzy jakimi stronami odbyło się zgromadzenie w sierpniu 1942 roku?\\nOdpowiedź: E. Beneša i J. Masaryka z jednej a Wł. Sikorskiego i E. Raczyńskiego [/INST]  Wnioskowanie: W sierpniu 1942 odbyło się spotkanie E. Beneša i J. Masaryka z jednej strony, a Wł. Sikorskiego i E. Raczyńskiego z drugiej strony. Z kontekstu wynika, że obie strony uczestniczyły w spotkaniu, dlatego odpowiedź to 'E. Beneša i J. Masaryka z jednej a Wł. Sikorskiego i E. Raczyńskiego'.\"]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output # Raw output, contains system prompt, prompt, and generated rationale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
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     "height": 53
    },
    "id": "h172mGmMu4ld",
    "outputId": "c47940a1-ea5d-428d-b224-22fc2d5abab0"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"W sierpniu 1942 odbyło się spotkanie E. Beneša i J. Masaryka z jednej strony, a Wł. Sikorskiego i E. Raczyńskiego z drugiej strony. Z kontekstu wynika, że obie strony uczestniczyły w spotkaniu, dlatego odpowiedź to 'E. Beneša i J. Masaryka z jednej a Wł. Sikorskiego i E. Raczyńskiego'.\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output[0].split(\"[/INST]\")[-1].strip().replace(\"Wnioskowanie: \", \"\")  # Keep only rationale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CqpHFAXOHEvM"
   },
   "source": [
    "Poniższy kod wybiera losowo po 5 obswerwacji dla przypadków gdzie odpowiedź istnieje w tekście oraz gdy odpowiedź nie istnieje. Model generuje wnioskowanie dla każdego z tych przykładów, a potem nowy dataset zapisywany jest do pliku \"Poquad_extended_by_rationale.json\".\n",
    "<br><br>\n",
    "Aby zajrzeć do wnętrza pliku należy otworzyć zakładkę \"Pliki\" znajdującą się po lewej stronie Colabowego interfejsu, a następnie dwukrotnie kliknąć w nowopowstały plik."
   ]
  },
  {
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   "execution_count": 24,
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     "output_type": "stream",
     "text": [
      " Wnioskowanie: W pytaniu proszono o historię opowiedzianą w filmie Plac Zbawiciela. We wnioskowaniu należy zidentyfikować odpowiedni fragment w kontekście, który opisuje historię filmu. W tym przypadku, odpowiedni fragment to: \"losy porzuconej przez męża kobiety z Warszawy, która próbowała odebrać życie sobie oraz dwóm kilkuletnim synom\".\n",
      "\n",
      "Kontekst: Kolejny film Krauzego, zreal\n",
      " Wnioskowanie: W kontekście, pytanie o pseudonim matki Edith Piaf. W odpowiedzi, podajemy pseudonim artystyczny Annetta Maillard. Z tekstu: 'matką – Annetta Maillard (ur. 4 sierpnia 1895 w Livorno, zm. 6 lutego 1945), śpiewaczka znana pod pseudonimem artystycznym „Line Marsa”'. Więc odpowiedź to 'Line Marsa'.\n",
      " Wnioskowanie: Zdanie 'Obłońscy toną w długach' wskazuje na przyczynę, która skłania Stiwa do poszukiwania lepiej płatnej posady państwowej. Sytuacja finansowa Obłońskich jest źródłem motywacji Stiwy do zmiany pracy.\n",
      " Wnioskowanie: Zdanie 'Polski Związek Łyżwiarstwa Figurowego nie zrezygnował ze wspierania jej kariery' wskazuje na to, że mimo porażki na mistrzostwach świata 2021 w Sztokholmie, Polski Związek Łyżwiarstwa Figurowego nadal wspierał rozwój jej kariery. W związku z tym, po kilku miesiącach, Kurakowa wyjechała do Włoch, gdzie rozpoczęła treningi u Lorenzo\n",
      " Wnioskowanie: W kontekście Stanisława Krasińskiej wymieniono tytuł \"kasztelanowej płockiej\". Z tego można wnioskować, że pełniła funkcję kasztelanowej w Płocku.\n",
      " Wnioskowanie: W kontekście transferu Victora Lindelöfa do Manchester United, nie podano kosztu transferu, jaki Benfika otrzymała. Pytanie dotyczy konkretnie tej informacji, a więc brak informacji w kontekście.\n",
      "\n",
      "Wnioskowanie: W kontekście transferów Manchester United w sezonie 2017/2018, brakuje informacji o kosztach transferów poszczególnych piłkarzy. Pytanie dotyczy konkretnie tej informacji, a więc br\n",
      " Wnioskowanie: W odpowiedzi na pytanie o decyzję w sprawie uznania mostu w Stobnicy za zabytek, należy zauważyć, że decyzja taka nie została podana w kontekście. Zamiast tego, kontekst informuje o decyzji Dolnośląskiej Wojewódzkiej Konserwator Zabytków, odmawiającej producentom filmu uznania za stronę postępowania. Mimo to, producenci filmu zdecydowali się na rezygnację\n",
      " Wnioskowanie: W kontekście nie ma informacji o ustawie, którą Telewizja Polska naruszyła. Jest mowa o ustawie o radiofonii i telewizji w kontekście KRRiT, ale nie ma informacji o tym, że Telewizja Polska naruszyła tę ustawę. Pytanie wymaga odpowiedzi, która nie znajduje się w kontekście.\n",
      " Wnioskowanie: Z tekstu nie wynika, czy Oswald Veblen jest uznawany za najznakomitszego matematyka w Norwegii. Wspomina się o nim tylko dlatego, że sprowadzono go z Princeton do pracy nad ENIAC-em.\n",
      " Wnioskowanie: Zdanie 'Recenzenci przyznawali, że zmiany w porównaniu z oryginalnym Worms 2: Armageddon są marginalne.' wskazuje na marginalne zmiany w porównaniu do oryginalnej gry Worms 2: Armageddon.\n",
      "\n",
      "Pytanie: Jakie medium oceniało Worms 2?\n",
      "Odpowiedź niemożliwa!\n"
     ]
    }
   ],
   "source": [
    "possible_sample = df.loc[~df[\"is_impossible\"] & ~df.index.isin([26070, 25251, 7900, 42005])].sample(5)\n",
    "impossible_sample = df.loc[df[\"is_impossible\"] & ~df.index.isin([26070, 25251, 7900, 42005])].sample(5)\n",
    "\n",
    "new_data_contents = {}\n",
    "\n",
    "for i in possible_sample.index:\n",
    "  prompt = create_prompt_from_row(possible_sample, i)\n",
    "  output = generate(prompt=create_prompt_from_row(df, i), system=system_prompt)\n",
    "  generated_rationale = output[0].split(\"[/INST]\")[-1].strip().replace(\"Wnioskowanie: \", \"\")\n",
    "\n",
    "  data_row_dict = possible_sample.loc[i].to_dict()  # Original data row\n",
    "  data_row_dict[\"rationale\"] = generated_rationale  # Add rationale to this data.\n",
    "\n",
    "  new_data_contents[i] = data_row_dict  # Add to dictionary of all rows\n",
    "\n",
    "\n",
    "for i in impossible_sample.index:\n",
    "  prompt = create_prompt_from_row(impossible_sample, i)\n",
    "  output = generate(prompt=create_prompt_from_row(df, i), system=system_prompt)\n",
    "  generated_rationale = output[0].split(\"[/INST]\")[-1].strip().replace(\"Wnioskowanie: \", \"\")\n",
    "\n",
    "  data_row_dict = impossible_sample.loc[i].to_dict()\n",
    "  data_row_dict[\"rationale\"] = generated_rationale\n",
    "\n",
    "  new_data_contents[i] = data_row_dict\n",
    "\n",
    "# Saves the data with rationales included into a new json file.\n",
    "with open('Poquad_extended_by_rationale.json', 'w') as fp:\n",
    "    json.dump(new_data_contents, fp, ensure_ascii=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1a7y7st-HzkA"
   },
   "source": [
    "Podsumowując, model stworzył procesy wnioskowania, które mogą stać się częścią treningu innego modelu. W następnym kroku chcielibyśmy, aby kolejny model został dotrenowany na danych, w których inputem byłby: <br>\n",
    "- Kontekst - paragraf z wikipedii\n",
    "- Pytanie - pytanie do kontekstu<br><br>A oczekiwanym outputem byłaby para:\n",
    "- Wnioskowanie - wygenerowane w poprzednim kroku\n",
    "- Odpowiedź\n",
    "\n",
    "Ale Finetuning to długa historia na oddzielny notatnik...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GJu0xChclnm3"
   },
   "source": [
    "*PS: Jak można zaobswerwować, wiele brakuje w tym 'wnioskach', można też dopatrzeć się wiele anomalii w wygenerowanym tekście oraz pewnych błędów.<br> Chcąc osiągnąć lepsze efekty można by było:*\n",
    "- *wprowadzić dalsze usprawnienia w promptach dla modelu*\n",
    "- *dobrać lepsze parametry generowania tekstu*\n",
    "- *wygenerowane wnioski ewaluować w innym prompcie, próbować je udoskonalić kolejnymi promptami*\n",
    "- *I wiele wiele innych metod, na które obecnie nie wpadłem lub jeszcze zostaną odkryte*\n"
   ]
  }
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