Bielik_(4_bit)_Text_Improvement.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<p align=\"center\">\n",
        "  <img src=\"https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png\">\n",
        "</p>"
      ],
      "metadata": {
        "id": "FpkKRgIFN-s0"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WS_Bvrb6NuL5"
      },
      "source": [
        "### Wstęp\n",
        "Poniższy notebook jest kolejnym przykładem użycia polskiego modelu językowego Bielik-v2 do praktycznych zastosowań. Celem notebooka jest pokazanie przykładowego kodu w języku Python (3.10), który rozwiązuje konkretny problem i jest łatwy do zrozumienia i odtworzenia dzięki opisowi kodu.\n",
        "\n",
        "Poniższy use case dotyczy poprawy tekstu napisanego w języku polskim. Użyjemy modelu Bielik-11B-v2.2-Instruct, aby pomógł nam poprawić tekst elminując z niego różnego rodzaju błędy językowe (m.in. ortograficzne i interpunkcyjne) oraz zmieniając styl wypowiedzi napisanego tekstu na wybrany przez użytkownika.\n",
        "\n",
        "Zapraszam!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q11GCpO_NuL8"
      },
      "source": [
        "### 1. Pobieranie pakietów\n",
        "\n",
        "Zaczniemy od pobrania pakietów. Jeżeli ich nie mamy, trzeba je zainstalować poprzez <code>pip</code> lub <code>conda</code> w zależności od naszego w jaki sposób Python został zainstalowany na naszym sprzęcie.\n",
        "Do pracy będzie na również potrzebna \"CUDA\", która należy pobrać i zainstalować z oficjalnej strony nvidi [używając tego linku](https://developer.nvidia.com/cuda-downloads).\n",
        "\n",
        "Następnie należy zainstalować odpowiednia wersje pakietu 'torch' z 'cuda' - polecam wersje 2.3.1 której używa (2.4 wyrzuca czasami problemy szczególnie na Windowsie) z wersja cuda 12.1 - można to zrobić przykładowo przy użyciu takiej komendy\n",
        "<code>conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia</code>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Jako, że model Bielik-v2 jest tzw. \"Gated model\", potrzebujemy zaakceptować Term of Use oraz autoryzywać się na stronę Hugging Face. Musimy utworzyć token i ustawić na naszym środowisku autoryzacje przy użyciu komendy <code>huggingface-cli login</code> w konsoli. Szerszą instrukcje można znaleźć [tutaj](https://huggingface.co/docs/huggingface_hub/quick-start)."
      ],
      "metadata": {
        "id": "ODfs4Ec8QAkL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U \"huggingface_hub[cli]\" -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Av08bq3XQABv",
        "outputId": "6bace034-73eb-4e4f-815b-67dc08a3abe1"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/67.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.7/67.7 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/417.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m409.6/417.5 kB\u001b[0m \u001b[31m32.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m417.5/417.5 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQ3gZBKHQMpt",
        "outputId": "09e05a87-ad60-4b72-de25-569395219edc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Należy również rozważyć aktualizację biblioteki bitsandbytes w przypadku egzekucji kodu w środowisku Google Colab"
      ],
      "metadata": {
        "id": "aqG6CB8Tpmib"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U bitsandbytes -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZJnOFNcR_V6",
        "outputId": "ea9ab226-4fec-4584-c516-49cb2d80f80a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.5/137.5 MB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Na końcu dla zwiększenia czytelności ignorujemy ostrzeżenia przy wczytywaniu pakietów."
      ],
      "metadata": {
        "id": "naEsnq61QI7J"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "wTbZ4UrjNuL-"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import warnings\n",
        "\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AepsmAd3NuMB"
      },
      "source": [
        "### 2. Pobieranie Bielika\n",
        "\n",
        "Zanim przejdziemy do ustawienia naszego Bielika sprawdźmy czy nasze GPU jest rozpoznawane przez torch.\n",
        "Po tym etapie wpisujemy kilka parametrów do konfiguracji:\n",
        "* <code>device</code>, które na stałe zostawiamy jako <code>cuda</code>,\n",
        "* <code>model_name</code>, którym jest nazwa modelu z HuggingFace, modele Bielik niezależnie od wersji zawsze zaczynają się od <code>speakleash/</code> a następnie wklejamy nazwę modelu. W razie wątpliwości co do nazw i dostępnych modeli, polecam odwiedzić [Speakleash](https://huggingface.co/speakleash) na stronie Hugging Face. Użyjemy naszego nowego modelu o technicznej nazwie <code>Bielik-11B-v2.2-Instruct</code>,\n",
        "* <code>max_tokens</code> czyli maksymalna długość odpowiedzi liczona w tokenach,\n",
        "* <code>temperature, top_p, top_k</code> parametry związane z kreatywnością modelu. Więcej o parametrach można przeczytać [tutaj](https://aviralrma.medium.com/understanding-llm-parameters-c2db4b07f0ee).\n",
        "\n",
        "Pozostała część poniższego kodu wczytuje i ustawiam nam model - korzystamy z ładowania w wersji 4bit aby model był lżejszy i odpowiadał szybciej przy jednoczesnej małej stracie na jakości odpowiedzi (jeżeli mamy dobry sprzęt można pokusić się o lepszą wersję, a jeżeli nas sprzęt jest słaby a zależy nam na szybim czasie odpowiedzi może rozważyć wersje 2bit).\n",
        "\n",
        "Uwaga: W przypadku korzystania kodu na Google Colab, należy wbyrać środowisko wykonawcze jako T4 GPU, lub inny rodzaj GPU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qO_rXyc0NuMC",
        "outputId": "537ebe17-8af9-436c-b18a-0239ac83e5f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using GPU: Tesla T4\n"
          ]
        }
      ],
      "source": [
        "if torch.cuda.is_available():\n",
        "    device = torch.device(\"cuda\")\n",
        "    print(\"Using GPU:\", torch.cuda.get_device_name(0))\n",
        "else:\n",
        "    device = torch.device(\"cpu\")\n",
        "    print(\"CUDA is not available. Using CPU.\")"
      ]
    },
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        },
        "id": "-IvP3YycNuMD",
        "outputId": "cf7858ce-6901-46ca-c025-e2cfea1182c0"
      },
      "outputs": [
        {
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
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          "metadata": {}
        }
      ],
      "source": [
        "device = \"cuda\"\n",
        "model_name = \"speakleash/Bielik-11B-v2.2-Instruct\"\n",
        "\n",
        "max_tokens = 5000\n",
        "\n",
        "temperature = 0\n",
        "top_k = 0\n",
        "top_p = 0\n",
        "\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",
        "                                            low_cpu_mem_usage = True\n",
        "                                            )\n",
        "\n",
        "model.generation_config.pad_token_id = tokenizer.pad_token_id"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKagl-gQNuMD"
      },
      "source": [
        "### 3. Testowanie działania\n",
        "Model wczytany, więc zanim przejdziemy do naszego zadania poprawy tekstu napiszmy funkcję <code>generate</code> która będzie udzielała odpowiedzi z Belika na zadany prompt. W ten sposób sprawdzimy czy wszystko działa poprawnie."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "qmjKJmaXNuME"
      },
      "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",
        "  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",
        "  answer = tokenizer.batch_decode(outputs, skip_special_tokens = False)\n",
        "\n",
        "  return answer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0xKWpNb6NuME",
        "outputId": "81d8360f-f44c-49d8-bd06-9a2c31932d2c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Odpowiedź:\n",
            "Jestem zaawansowanym modelem językowym AI stworzonym przez SpeakLeash i ACK Cyfronet AGH. Moim głównym zadaniem jest udzielanie odpowiedzi na pytania w języku polskim. Staram się dostarczać informacje w sposób zwięzły i zrozumiały, korzystając z mojej wiedzy i umiejętności przetwarzania języka naturalnego.\n",
            "\n",
            "Jeśli masz jakieś pytania lub potrzebujesz pomocy, śmiało pytaj! Postaram się udzielić jak najlepszej odpowiedzi w możliwie najkrótszym czasie.\n"
          ]
        }
      ],
      "source": [
        "prompt = \"Kim jesteś?\"\n",
        "system = \"Jesteś chatboem udzielającym odpowiedzi na pytania w języku polskim\"\n",
        "output = generate(prompt, system)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9uPcFG44NuMF"
      },
      "source": [
        "### 4. Funkcja na poprawe tekstu\n",
        "Model działa prawidłowo, więc przejdźmy do sedna, czyli jak poprawiać tekst. Napiszemy kompleksową funkcję, która na wejściu będzie miała tekst podany przez użytkownika, który wymaga poprawy a na wyjściu poprawiony tekst.\n",
        "\n",
        "Zaczynamy od definicji funkcji, która będzie posiadała 3 parametry: treść wymagając zmian (w postaci <code>string</code>), listę w której użytkownik może wskazać pod jakim względem chce poprawić tekst oraz 'one_shot' w którym wzmocnimy nasz prompt przykładem prawidłowo wykonanego zadania co powinno zwiększyć jakość naszej odpowiedzi, w postaci <code>True</code> lub <code>False</code>.\n",
        "\n",
        "Zaczynamy od zdefiniowania systemu, gdzie nasz model ma się wcielić w rolę eksperta do edycji polskiego tekstu. Następnie rozpoczynamy nasz prompt od jasnej definicji zadania po której następuje dodanie komponentów którymi są kategorie błędów (patrz: źródła 3). Decydujemy się na 7 popularnych kategorii, które są związane z formą pisemną polskiego języka czyli \"literowka\", \"ortografia\", \"interpunkcja\", \"fleksja\", \"skladnia\", \"leksykalne\", \"stylistyka\". Następnie do promptu dokładamy przykład z poprawą zdania, demonstrując modelowi z jakimi problemami może się zmagać i do jakiego rezultatu powinien w tym przypadku dojść a na koniec dodajemy zabezpieczenie przed zmianą treści (nie chcemy, żeby model zmieniał sens wypowiedzi, a jedynie znajdował i rozwiązywał problemy językowe z nim związane). Na samym końcu wskazujemy mu treść tekstu który ma poprawić.\n",
        "\n",
        "Użytkownik może wybrać od 1 do 7 kategorii błędów, a domyślną opcję zostawiamy na \"wszystko\", w której model ma sprawdzić tekst pod każdym z 7 rodzajów błędów.\n",
        "\n",
        "Mając gotowy cały prompt przechodzimy do generowania odpowiedzi bazując na przygotowanej już wcześniej funkcji <code>generate</code>. Jako, że chcemy aby model nie zmieniał treści a jedynie ją poprawiał, kreatywność jest bardziej naszym przeciwnikiem niż sprzymierzeńcem w związku z czym ustawiamy wartości wszystkich 3 parametrów <code>temperature</code>, <code>top_p</code> oraz <code>top_k</code> na 0. Jako, że na wejściu oczekujemy poprawionego tekstu to deklarujemy wartość <code>max_tokens</code> na 5000, aby nie został on ucięty w trakcie generowania (jeśli mamy dłuższe teksty ustaw wartość na 10 000 lub więcej)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "vC--WZyUNuMG"
      },
      "outputs": [],
      "source": [
        "def resolve_mistakes(text: str, opcje = ['wszystko'], one_shot = True):\n",
        "    system = \"Jesteś ekspertem zajmującym się poprawą tesktów w języku polskim.\"\n",
        "\n",
        "    prompt_poczatek = \"Sprawdź poniższy tekst w języku polskim i popraw go. Zwróć szczególną uwagę na:\\n\\n\"\n",
        "\n",
        "    # Mozliwe opcje do wpisania w drugim argumencie: \"literowka\", \"ortografia\", \"interpunkcja\", \"fleksja\", \"skladnia\", \"leksykalne\", \"stylistyka\"\n",
        "    prompt_literowka = \"* Literówki (pominięcie, powtórzenie lub wstawienie niewłaściwego znaku)\\n\\n\"\n",
        "    prompt_ortograficzny = \"* Błędy ortograficzne (błędny zapis liter lub ich połączeń, błędna pisownia małą lub wielką literą, błędne stosowanie kropki w skrótach, błędy w pisowni łącznej)\\n\\n\"\n",
        "    prompt_interpunkcyjny = \"* Błędy interpunkcyjne (zbędne użycie znaku interpunkcyjnego, brak znaku interpunkcyjnego, błędne zapisy w przypadku zbiegu znaków interpunkcyjnych, użycie niewłaściwego znaku interpunkcyjnego)\\n\\n\"\n",
        "    prompt_fleksyjny = \"* Błędy fleksyjne (związane z wyborem niewłaściwej formy wyrazu, błędną odmianą lub z błędnym brakiem odmiany)\\n\\n\"\n",
        "    prompt_skladniowe = \"* Błędy składniowe (używanie błędnych form lub konstrukcji w miejscach, których uzupełnienia wymagają wyrazy nadrzędne, błędy w szyku, błędne użycie imiesłowowych równoważników zdań)\\n\\n\"\n",
        "    prompt_leksykalne = \"* Błędy leksykalne (błędne użycie słowa w danej konstrukcji, mylenie wyrazów podobnych brzmieniowo, używanie zbędnych zapożyczeń lub nadużywanie wyrazów obcych, błędy słowotwórcze i powstałe w wyniku skojarzeń słowotwórczych, zniekształcenia związków frazeologicznych)\\n\\n\"\n",
        "    prompt_stylistyczne = \"* Błędy stylistyczne (niedostosowanie form językowych do charakteru i funkcji korespondencji, niezamierzone uczynienie tekstu dwuznacznym)\\n\\n\"\n",
        "\n",
        "    prompt_one_shot = \"Poniżej masz przykład: \\n\\nTeskt do popawy: 'janusz siedział skólnoy za stołm i milczał .'\\n\\nPrawdiłowy poprawiony teskt: 'Janusz siedział skulony za stołem i milczał.'\"\n",
        "    promt_koniec = \"Pamietaj, aby ingerencja w treść była związana tylko z poprawą błędu i nie ingerowala w sens pierwotnej treści. Zwróć tylko poprawiony tekst bez dodatkowych komentarzy. Poniżej tekst do poprawy:\\n\\n\"\n",
        "\n",
        "    if opcje[0] == 'wszystko':\n",
        "        opcje = ['literowka', 'ortografia', 'interpunkcja', 'fleksja', 'skladnia', 'leksykalne', 'stylistyka']\n",
        "\n",
        "    prompt_calosc = prompt_poczatek + (prompt_literowka if 'literowka' in opcje else '') + (prompt_ortograficzny if 'ortografia' in opcje else '') + (prompt_interpunkcyjny if 'interpunkcja' in opcje else '') + (prompt_fleksyjny if 'fleksja' in opcje else '') + (prompt_skladniowe if 'skladnia' in opcje else '') + (prompt_leksykalne if 'leksykalne' in opcje else '') + (prompt_stylistyczne if 'stylistyka' in opcje else '') + (prompt_one_shot if one_shot else '') + promt_koniec + text\n",
        "\n",
        "    messages = []\n",
        "    messages.append({\"role\": \"system\", \"content\": system})\n",
        "    messages.append({\"role\": \"user\", \"content\": str(prompt_calosc)})\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",
        "    outputs = model.generate(model_input_ids,\n",
        "                           attention_mask=model_attention_mask,\n",
        "                           streamer = streamer,\n",
        "                           max_new_tokens=5000,\n",
        "                           do_sample=True if temperature else False,\n",
        "                           temperature = 0,\n",
        "                           top_k = 0,\n",
        "                           top_p = 0)\n",
        "\n",
        "    answer = tokenizer.batch_decode(outputs, skip_special_tokens = False)\n",
        "\n",
        "    return answer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4xW-Hq-NuMG"
      },
      "source": [
        "### 5. Funkcja na zmiane stylu\n",
        "Funkcja do zadania poprawy błędów gotowa, czas więc na drugą funkcję która pozwoli nam zmieniać styl wypowiedzi. Wejście i wyjście z naszej funkcji jest podobne jak w poprzednim przypadku: na wejściu oczekujemy tekstu do poprawy a na wyjściu poprawiony tekst. Dodatkowo od użytkownika wymagamy podania jeszcze jednego argumentu, czyli stylu. Użytkownik będzie mógł wybrać jeden z pięciu stylów: \"potoczny\", \"urzedowy\", \"publicystyczny\", \"naukowy\" i \"artystyczny\". W tym przypadku nie decydujemy się na deklaracje domyślnej wartości, a oba wejścia mają forme <code>string</code>.\n",
        "\n",
        "Ustawiamy system jako eksperta od zmiany stylu i rozpoczynamy prompt od przedstawienia zadania oraz wyboru stylu (tym razem użytkownik może wybrać tylko 1 z 5 dostępnych). Na koniec ponownie zabezpieczenie przed zmianą treści oraz \"doklejenie\" treści tekstu do poprawy. Generowanie odpowiedzi odbywa się w taki sam sposób jak w porzedniej funkcji, jednak tym razem zwiększamy nieco wartości <code>temperature</code>, <code>top_p</code> oraz <code>top_k</code> na nieco większe, gdyż to zadanie wymaga pewnej kreatywności (jednak zbyt duża wartość grozi zmianą sensu wypowiedzi, więc przyjmujemy ostrożne wartości)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "DzeSlNWaNuMH"
      },
      "outputs": [],
      "source": [
        "def change_style(text: str, styl: str):\n",
        "    system = \"Jesteś ekspertem zajmującym się zmianą stylu tekstu w języku polskim.\"\n",
        "\n",
        "    prompt_poczatek = \"Zmień styl napisanego tekstu na \"\n",
        "\n",
        "    # Mozliwe opcje do wpisania w drugim argumencie: \"potoczny\", \"urzedowy\", \"publicystyczny\", \"naukowy\", \"artystyczny\"\n",
        "    prompt_potoczny = \"potoczny.\"\n",
        "    prompt_urzedowy = \"urzędowy.\"\n",
        "    prompt_publicystyczny = \"publicystyczny.\"\n",
        "    prompt_naukowy = \"naukowy.\"\n",
        "    prompt_artystyczny = \"artystyczny.\"\n",
        "\n",
        "    promt_koniec = \"\\n\\nPamietaj, aby ingerencja w treść była związana tylko ze zmianą i nie ingerowala w sens pierwotnej treści. Zwróć tylko poprawiony tekst bez dodatkowych komentarzy. Poniżej tekst do do zmiany stylu:\\n\\n\"\n",
        "\n",
        "    prompt_calosc = prompt_poczatek + (prompt_potoczny if styl == 'potoczny' else '') + (prompt_urzedowy if styl == 'urzedowy' else '') + (prompt_publicystyczny if styl == 'publiczystyczny' else '') + (prompt_naukowy if styl == 'naukowy' else '') + (prompt_artystyczny if styl == 'artystyczny' else '') + promt_koniec + text\n",
        "\n",
        "    messages = []\n",
        "    messages.append({\"role\": \"system\", \"content\": system})\n",
        "    messages.append({\"role\": \"user\", \"content\": prompt_calosc})\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",
        "      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",
        "    outputs = model.generate(model_input_ids,\n",
        "                           attention_mask=model_attention_mask,\n",
        "                           streamer = streamer,\n",
        "                           max_new_tokens=5000,\n",
        "                           do_sample=True if temperature else False,\n",
        "                           temperature = 0.1,\n",
        "                           top_k = 5,\n",
        "                           top_p = 0.1)\n",
        "\n",
        "    answer = tokenizer.batch_decode(outputs, skip_special_tokens=False)\n",
        "\n",
        "    return answer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LIIJbFdNuMH"
      },
      "source": [
        "## 6. Przykład 1: e-mail\n",
        "Tekstów napisanych z błędami nie brakuje - z pewnością ten notebook również je zawiera, taka nasza natura, nie wszystko jesteśmy w stanie za każdym razem robić perfekcyjnie. Klasyczny przykładem z życia codziennego (zarówno służbowego jak i prywatnego) są wiadomości email. Kontaktowałem się ostatnio z recepcją swojego dentysty gdyż zauważyłem że nie ma mojego adresu na fakturze, który jest mi potrzebny. Jadąc w tramwaju przygotowałem więc niedbale w notatniku e-mail o następującej treści (niektóre dane zanonimizowane i dołożyłem dodatkowe błędy na potrzeby demonstracji).\n",
        "\n",
        "Może go zobaczyć poniżej a zawiera on po kolei następujące błędy:\n",
        "* Spacja przed przecinkiem (błąd interpunkcyjny),\n",
        "* Rozpoczęcia zdania od małej litery (błąd ortograficzny),\n",
        "* Pominięte \"e\" w \"zamiszkania\" (literówka),\n",
        "* \"proźba\" (błąd ortograficzny),\n",
        "* Błędna odmiana słowa \"faktura\" (błąd fleksyjny).\n",
        "\n",
        "Użyjemy naszej funkcji na poprawę tekstu z domyślną opcją \"wszystko\" oraz spróbujemy włączyć argument \"one_shot\" licząc na jak najlepszą odpowiedź."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "8CieyAPANuMI"
      },
      "outputs": [],
      "source": [
        "email = \"\"\"\n",
        "Dzień dobry,\n",
        "Po wtorkowej wizycie otrzymałem fakturę, jednak potrzebuje małej korekty.\n",
        "potrzebuje aby w danych nabywcy był dodatkowo adres zamieszkania.\n",
        "\n",
        "Mój adres zamiszkania (jeżeli kiedyś podawałem , to prawdopodobnie jest nieaktualny bo go niedawno zmieniłem, więc proźba o użycie tego):\n",
        "\n",
        "ul. Marszałkowska 11C/24\n",
        "00-001 Warszawa\n",
        "\n",
        "W załączniku skan aktualnej faktura.\n",
        "\n",
        "Pozdrawiam\n",
        "Adam Kowalski\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yy-etMvKNuMI",
        "outputId": "e914309d-460b-4a5f-d164-6e5ac1f0ddfc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Poprawiony tekst:\n",
            "\n",
            "Dzień dobry,\n",
            "Po wtorkowej wizycie otrzymałem fakturę, jednak potrzebuję małej korekty.\n",
            "Potrzebuję, aby w danych nabywcy był dodatkowo adres zamieszkania.\n",
            "\n",
            "Mój adres zamieszkania (jeżeli kiedyś podawałem, to prawdopodobnie jest nieaktualny, bo go niedawno zmieniłem, więc proszę o użycie tego):\n",
            "\n",
            "ul. Marszałkowska 11C/24\n",
            "00-001 Warszawa\n",
            "\n",
            "W załączniku skan aktualnej faktury.\n",
            "\n",
            "Pozdrawiam\n",
            "Adam Kowalski\n"
          ]
        }
      ],
      "source": [
        "email_poprawiony = resolve_mistakes(email, opcje = ['wszystko'], one_shot = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iS2CEkUsNuMJ"
      },
      "source": [
        "Idąc po kolei, udało się usunąć zbędną spację, zamienić literę małą na dużą, edytować słową z błędem ortograficzny,przy okazji poprawiając stylistykę oraz poprawnie odmienić słowa \"faktura\". Bielik poradził sobię z zadaniem bezbłędnie a do tego przekaz i sens oryginalnego tekstu został zachowany."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pG4Z_QMsNuMJ",
        "outputId": "dc63f381-d26b-47de-b621-389067b41166"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Zmieniony tekst w stylu urzędowym:\n",
            "\n",
            "Szanowni Państwo,\n",
            "\n",
            "W nawiązaniu do faktury z dnia [data] wystawionej po wizycie w dniu [data], zwracam się z uprzejmą prośbą o dokonanie korekty w danych nabywcy.\n",
            "\n",
            "Proszę o uzupełnienie adresu zamieszkania w następujący sposób:\n",
            "\n",
            "ul. Marszałkowska 11C/24\n",
            "00-001 Warszawa\n",
            "\n",
            "Jednocześnie informuję, że wcześniej podany adres zamieszkania jest nieaktualny.\n",
            "\n",
            "W załączeniu przesyłam skan aktualnej faktury.\n",
            "\n",
            "Z poważaniem,\n",
            "Adam Kowalski\n"
          ]
        }
      ],
      "source": [
        "email_nowy_styl = change_style(email, styl = 'urzedowy')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_yngJqNNuMJ"
      },
      "source": [
        "Zadanie zakończone sukcesem - udało się zmienić ton wypowiedzi na bardziej formalny, jednocześnie nie ingerując w danę i potrzebę zawartą w tekście."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EsI2CVhcNuMK"
      },
      "source": [
        "### 7. Przykład 2: dokumentacja kodu\n",
        "Drugi przykład to popularne README będące dokumentacją kodu programistycznego. Nie raz zdarzania się pisać taką dokumentacje niedbalne, gdyż goni nas czas, więc jej jakość może nie być najwyższa, a z naszymi stworzonymi przed chwilą funkcjami możemy ją bardzo szybko podnieść.\n",
        "\n",
        "Poniżej mamy kawałek README z [repozutorium biblioteki PyTorch](https://github.com/pytorch/pytorch/blob/main/README.md) z którego wycięto kilka fragmentów i przetłumaczono na język polski przy użyciu Google Translate. Sztucznie dodano również kilka błędów (ta dokumentacja jest akurat dobrze przygotowana, a tłumaczenie nie wygenerowało zbyt wielu błędów):\n",
        "* Niepotrzebny przecinek po słowie \"pełni\" (błąd interpunkcyjny),\n",
        "* \"nie zależnie\" (błąd ortograficzny),\n",
        "* Niepotrzebna spacja przed \":\" (literówka),\n",
        "* Przestawienie szyku liter w słowe \"zogdny\" (literówka),\n",
        "* Nieuzasadnione powtórzenie \"obsługuje\" dwa razy z rzędu (błąd stylistyczny).\n",
        "\n",
        "Zaczniemy od poprawy błędów, ale tym razem przyjmiemy inne założenie - jako użytkownik chce, aby model sprawdził mi jedynie literówki i błędy interpunkcyjne, dlatego w funkcji w argumencie <code>opcje</code> definiuje dwuelementową listę z wymienionymi opcjami błędów."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "X1S_YVbLNuMK"
      },
      "outputs": [],
      "source": [
        "dokumentacja = \"\"\"\n",
        "**Instalacja**\n",
        "Jeśli instalujesz ze źródła, będziesz potrzebować:\n",
        "\n",
        "* Python 3.8 lub nowszy (dla Linuksa wymagany jest Python 3.8.1+)\n",
        "* Kompilator w pełni, obsługujący C++17, taki jak clang lub gcc (wymagany jest gcc 9.4.0 lub nowszy)\n",
        "* Zdecydowanie zalecamy zainstalowanie środowiska Anaconda. Otrzymasz wysokiej jakości bibliotekę BLAS (MKL) i kontrolowane wersje zależności nie zależnie od dystrybucji Linuksa.\n",
        "\n",
        "**Obsługa NVIDIA CUDA**\n",
        "Jeśli chcesz kompilować z obsługą CUDA, wybierz obsługiwaną wersję CUDA z naszej macierzy obsługi, a następnie zainstaluj następujące elementy:\n",
        "\n",
        "* NVIDIA CUDA\n",
        "* NVIDIA cuDNN v8.5 lub nowsza\n",
        "* Kompilator zogdny z CUDA\n",
        "Uwaga : Możesz zapoznać się z macierzą obsługi cuDNN dla wersji cuDNN z różnymi obsługiwanymi CUDA, sterownikiem CUDA i sprzętem NVIDIA\n",
        "\n",
        "Jeśli chcesz wyłączyć obsługę CUDA, wyeksportuj zmienną środowiskową USE_CUDA=0. Inne potencjalnie przydatne zmienne środowiskowe można znaleźć w pliku setup.py.\n",
        "\n",
        "Należy pamiętać, że począwszy od PyTorch 2.5, kompilacja PyTorch z XPU obsługuje zarówno nowe, jak i obsługuje stare ABI C++. Wcześniej XPU obsługiwało tylko nowe ABI C++. Jeśli chcesz kompilować z obsługą Intel GPU, postępuj zgodnie z Intel GPU Support.\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kFVqBMovNuMK",
        "outputId": "fd5f5597-776d-4b7f-a7f2-a487c3ea86a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Przeprowadziłem poprawę tekstu zgodnie z Twoimi wymaganiami:\n",
            "\n",
            "**Instalacja**\n",
            "\n",
            "Jeśli instalujesz ze źródła, będziesz potrzebować:\n",
            "\n",
            "* Python 3.8 lub nowszy (dla Linuksa wymagany jest Python 3.8.1+)\n",
            "* Kompilator w pełni, obsługujący C++17, taki jak clang lub gcc (wymagany jest gcc 9.4.0 lub nowszy)\n",
            "* Zdecydowanie zalecamy zainstalowanie środowiska Anaconda. Otrzymasz wysokiej jakości bibliotekę BLAS (MKL) i kontrolowane wersje zależności niezależnie od dystrybucji Linuksa.\n",
            "\n",
            "**Obsługa NVIDIA CUDA**\n",
            "\n",
            "Jeśli chcesz kompilować z obsługą CUDA, wybierz obsługiwaną wersję CUDA z naszej macierzy obsługi, a następnie zainstaluj następujące elementy:\n",
            "\n",
            "* NVIDIA CUDA\n",
            "* NVIDIA cuDNN v8.5 lub nowsza\n",
            "* Kompilator zgodny z CUDA\n",
            "\n",
            "Uwaga: Możesz zapoznać się z macierzą obsługi cuDNN dla wersji cuDNN z różnymi obsługiwanymi CUDA, sterownikiem CUDA i sprzętem NVIDIA.\n",
            "\n",
            "Jeśli chcesz wyłączyć obsługę CUDA, wyeksportuj zmienną środowiskową USE_CUDA=0. Inne potencjalnie przydatne zmienne środowiskowe można znaleźć w pliku setup.py.\n",
            "\n",
            "Należy pamiętać, że począwszy od PyTorch 2.5, kompilacja PyTorch z XPU obsługuje zarówno nowe, jak i obsługuje stare ABI C++. Wcześniej XPU obsługiwało tylko nowe ABI C++. Jeśli chcesz kompilować z obsługą Intel GPU, postępuj zgodnie z Intel GPU Support.\n"
          ]
        }
      ],
      "source": [
        "dokumentacja_poprawiona = resolve_mistakes(dokumentacja, opcje = ['literowka', 'interpunkcja'], one_shot = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pC8eG_2NuML"
      },
      "source": [
        "Bielik bez problemu naprawił 4 z 5 błędów i ponownie nie zmienił sensu treści. Tym razem nie wychwycił niepotrzebnego przecinka, co może wynikać ze specyfiki tekstu (język techniczny i dużo nazw własnych). Model też nie ingerował w podział tekstu na akapity czy znaki z markdown (takie jak \"*\", \"**\") co również jest działanie pożadanym."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NQca47B1NuML",
        "outputId": "34f1de61-caa2-43da-948c-57d0fa7cb4a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Oto poprawiony tekst:\n",
            "\n",
            "**Instalacja**\n",
            "\n",
            "Aby prawidłowo zainstalować program, wykonaj następujące kroki:\n",
            "\n",
            "1. Upewnij się, że masz zainstalowanego Pythona w wersji 3.8 lub nowszej (dla Linuksa wymagany jest Python 3.8.1+).\n",
            "\n",
            "2. Zainstaluj kompilator C++ obsługujący standard C++17, np. clang lub gcc (wymagany jest gcc 9.4.0 lub nowszy).\n",
            "\n",
            "3. Zalecamy zainstalowanie środowiska Anaconda, które zapewni wysokiej jakości bibliotekę BLAS (MKL) oraz kontrolowane wersje zależności niezależnie od dystrybucji Linuksa.\n",
            "\n",
            "**Obsługa NVIDIA CUDA**\n",
            "\n",
            "Jeśli chcesz kompilować z obsługą CUDA:\n",
            "\n",
            "1. Wybierz obsługiwaną wersję CUDA z naszej macierzy obsługi.\n",
            "\n",
            "2. Zainstaluj NVIDIA CUDA oraz NVIDIA cuDNN v8.5 lub nowszą.\n",
            "\n",
            "3. Upewnij się, że masz zainstalowany kompilator zgodny z CUDA.\n",
            "\n",
            "Uwaga: Możesz zapoznać się z macierzą obsługi cuDNN dla wersji cuDNN z różnymi obsługiwanymi CUDA, sterownikiem CUDA i sprzętem NVIDIA.\n",
            "\n",
            "Jeśli chcesz wyłączyć obsługę CUDA, wyeksportuj zmienną środowiskową USE_CUDA=0. Inne przydatne zmienne środowiskowe można znaleźć w pliku setup.py.\n",
            "\n",
            "Należy pamiętać, że począwszy od PyTorch 2.5, kompilacja PyTorch z XPU obsługuje zarówno nowe, jak i stare ABI C++. Wcześniej XPU obsługiwało tylko nowe ABI C++. Jeśli chcesz kompilować z obsługą Intel GPU, postępuj zgodnie z instrukcjami Intel GPU Support.\n"
          ]
        }
      ],
      "source": [
        "dokumentacja_nowy_styl = change_style(dokumentacja, styl = 'publicystyczny')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gPfVyuHNuML"
      },
      "source": [
        "Widzimy zmianę koncepcji przedstawienia treści, użycie punktowania i rozdzielenie tekstu na dwie części wraz z uwagami. Ingerencja jest dosyć znacząca, jednak ponownie widać zachowanie sensu i przekazu oraz brak zmiany treści kiedy jest to zupełnie niewskazane."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XXpcKTsyNuMM"
      },
      "source": [
        "### Podsumowanie\n",
        "W powyższym notebooku udało nam się stworzyć i przetestować dwie funkcje do poprawy polskiego tekstu: usuwanie błędów językowych oraz zmiana stylu. Jak widzimy model Bielik-v2 dał sobie radę z tym zdaniem, które mimo że nie jest skomplikowane to dotyczy wielu sytuacji wziętych z życia codziennego i biznesu.\n",
        "\n",
        "Aby nieco ostudzić entuzjazm to pamiętajmy, że Bielik-v2 to nie oczywiście jeden z wielu dużych modeli językowych które potrafią wykonać takie zadanie, jednak fakt uczenia Bielika w dużo większej liczbie polskich treści sprawia, że model mógł \"zobaczyć\" więcej przykładów tekstów napisanych poprawnie w ojczystym języku niż ma to miejsce w innych modelach oraz poradzi sobie z dłuższymi tekstami dzięki kontekstowi wielkości 32k tokenów - to więcej niż chociażby gemma-7b czy Llama-3-8B.\n",
        "\n",
        "Podobne zadania wykonują również inne narzędzia (w szczególności poprawa błędów jak chociaż autokorekty w wielu narzędziach takich jak Microsoft Word oraz różnego rodzaju wtyczki jak Grammarly). Stworzone funkcje jednak mają przewagę, że wymagają jedynie podania tekstu do poprawy i pod jakim kątem chcemy sprawdzić tekst - użytkownik nie musi \"przeklikać\" poprawek (co oczywiście może nie za sobą również pewne wady). Bielik-v2 dodatkowo jest w pełni darmowy - nie trzeba go kupić, ani subskrybować :)\n",
        "\n",
        "Notebook oczywiście można rozwinąć, udokumentowanie funkcji na pewno pomoże w jej używaniu dla nowych użytkowników a weryfikacja skuteczności tych funkcji na większej liczbie realnych przykładów pozwoli wyciągnąć wnioski na ile tak naprawdę użyty model rozwiązuje przytoczone problemy."
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        "Dziękuje za zapoznanie się z materiałem.\n",
        "\n",
        "*Autor notebooka: Michał Bogacz*\n",
        "\n",
        "W razie pytań problemów zapraszam do kontaktu [na moim Linkedin](https://www.linkedin.com/in/michalbogacz1/).\n",
        "\n",
        "To nie jedyny przykład użycia naszego Orła, zapoznaj sie z innymi przykładami jego użycia [na naszym repozytorium](https://github.com/speakleash/Bielik-how-to-start)."
      ]
    },
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        "Źrodła:\n",
        "1. https://github.com/speakleash/Bielik-how-to-start\n",
        "2. https://dobryslownik.pl/kompendium-regul-jezykowych/regula/492/\n",
        "3. https://sjp.pwn.pl/poradnia/haslo/Literowka;22233.html\n",
        "4. https://www.atelier-redakcji.eu/blog/blad-jezykowy/\n",
        "5. https://contentwriter.pl/style-jezykowe/#32\n",
        "6. https://github.com/pytorch/pytorch/blob/main/README.md\n",
        "7. https://huggingface.co/spaces/speakleash/open_pl_llm_leaderboard\n",
        "8. https://huggingface.co/speakleash"
      ]
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