TEN KOD DZIAŁA
This commit is contained in:
l.gabrysiak
parent
999eded568
commit
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346
hft.py
346
hft.py
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@ -1,35 +1,21 @@
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import nltk
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nltk.download('averaged_perceptron_tagger', quiet=True)
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nltk.download('wordnet', quiet=True)
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nltk.download('punkt', quiet=True)
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import os
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import os
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import torch
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import torch
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import random
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import torch.nn as nn
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from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
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from datasets import Dataset
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import re
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import re
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import json
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import json
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import PyPDF2
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import PyPDF2
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import docx2txt
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import docx2txt
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import pytesseract
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import pytesseract
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import numpy as np
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from PIL import Image
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from PIL import Image
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from collections import defaultdict
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from collections import defaultdict
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from multiprocessing import cpu_count
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from concurrent.futures import ThreadPoolExecutor
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from transformers import (
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AutoTokenizer,
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AutoModelForCausalLM,
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TrainingArguments,
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Trainer,
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DataCollatorForLanguageModeling
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)
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from datasets import Dataset
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from nlpaug.augmenter.word import SynonymAug
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from huggingface_hub import login
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from huggingface_hub import login
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# Konfiguracja
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# Konfiguracja
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os.environ['TORCH_USE_CUDA_DSA'] = '1'
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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login(token="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") # Zastąp swoim tokenem
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login(token="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")
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class SourceMapper:
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class SourceMapper:
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def __init__(self):
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def __init__(self):
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@ -47,237 +33,227 @@ class SourceMapper:
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def get_source(self, idx):
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def get_source(self, idx):
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return self.idx_to_source.get(idx, "Unknown")
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return self.idx_to_source.get(idx, "Unknown")
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class LegalProcessor:
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def load_file_catalog(catalog_path):
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def __init__(self, catalog_path):
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self.catalog = self.load_catalog(catalog_path)
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self.augmenter = SynonymAug(aug_src='wordnet', aug_max=3, lang='pol')
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def load_catalog(self, path):
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try:
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try:
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with open(path, 'r', encoding='utf-8') as f:
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with open(catalog_path, 'r', encoding='utf-8') as file:
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return json.load(f)
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return json.load(file)
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except:
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except Exception as e:
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return defaultdict(str)
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print(f"Błąd wczytywania katalogu plików: {str(e)}")
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return {}
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def process_file(self, file_path):
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def identify_legal_document(filename, file_catalog):
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text = self.extract_text(file_path)
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base_name = os.path.splitext(filename)[0].lower()
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if not text:
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return file_catalog.get(base_name, "Opracowanie własne")
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return []
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doc_type = self.identify_doc_type(file_path)
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def extract_text_from_file(file_path):
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return self.split_content(text, doc_type)
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def extract_text(self, file_path):
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ext = os.path.splitext(file_path)[1].lower()
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try:
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try:
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if ext == '.pdf':
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_, ext = os.path.splitext(file_path)
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return self.extract_pdf(file_path)
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ext = ext.lower()
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if ext in ['.txt', '.md']:
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with open(file_path, 'r', encoding='utf-8') as file:
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return file.read()
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elif ext == '.pdf':
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text = ""
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try:
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with open(file_path, 'rb') as file:
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reader = PyPDF2.PdfReader(file)
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for page in reader.pages:
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text += page.extract_text() or ""
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except Exception as e:
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print(f"Błąd PDF: {str(e)}")
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return text
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elif ext in ['.doc', '.docx']:
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elif ext in ['.doc', '.docx']:
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return docx2txt.process(file_path)
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return docx2txt.process(file_path)
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elif ext in ['.jpg', '.jpeg', '.png']:
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elif ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
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return self.extract_image(file_path)
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return pytesseract.image_to_string(Image.open(file_path))
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else:
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else:
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with open(file_path, 'r', encoding='utf-8') as f:
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print(f"Nieobsługiwany format pliku: {ext}")
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return f.read()
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return ""
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except Exception as e:
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except Exception as e:
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print(f"Błąd przetwarzania {file_path}: {str(e)}")
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print(f"Błąd ekstrakcji tekstu: {str(e)}")
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return ""
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return ""
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def extract_pdf(self, path):
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def prepare_dataset(directory, catalog_path, source_mapper):
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text = ""
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file_catalog = load_file_catalog(catalog_path)
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with open(path, 'rb') as f:
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data = []
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reader = PyPDF2.PdfReader(f)
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for page in reader.pages:
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text += page.extract_text() + "\n"
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return re.sub(r'\s+', ' ', text)
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def extract_image(self, path):
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print(f"\n{'='*50}\nDIAGNOSTYKA DANYCH\n{'='*50}")
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return pytesseract.image_to_string(
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Image.open(path),
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config='--psm 4 --oem 3 -c preserve_interword_spaces=1'
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)
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def identify_doc_type(self, file_path):
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for root, _, files in os.walk(directory):
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base = os.path.splitext(os.path.basename(file_path))[0].lower()
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for file in files:
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return self.catalog.get(base, "Custom")
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file_path = os.path.join(root, file)
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print(f"\nPrzetwarzanie pliku: {file_path}")
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def split_content(self, text, doc_type):
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try:
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if doc_type == "Custom":
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text = extract_text_from_file(file_path)
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return self.split_custom(text)
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if not text.strip():
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return self.split_legal(text, doc_type)
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print("Pominięto - brak tekstu")
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def split_legal(self, text, doc_type):
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pattern = r'(?i)(Art[\.\s]*\d+[a-z]*|§\s*\d+|Rozdział\s+[IVXLCDM]+)'
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parts = re.split(pattern, text)
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results = []
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current_header = ""
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for part in parts:
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if not part:
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continue
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continue
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if re.match(pattern, part):
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if current_header:
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results.append(current_header)
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current_header = f"[{doc_type}] {part.strip()}"
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else:
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if current_header:
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results.append(f"{current_header}: {part.strip()}")
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current_header = ""
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else:
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results.append(part.strip())
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return [text for text in results if len(text) > 50]
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print(f"Długość tekstu: {len(text)} znaków")
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def split_custom(self, text):
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doc_type = identify_legal_document(file, file_catalog)
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print(f"Rozpoznany typ dokumentu: {doc_type}")
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if doc_type != "Opracowanie własne":
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articles = re.split(r'(?i)(Art[\.\s]+\d+[\.\s]?)', text)
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articles = [a.strip() for a in articles if a.strip()]
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print(f"Znaleziono {len(articles)} fragmentów")
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for i in range(0, len(articles)-1, 2):
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article_number = articles[i]
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article_content = articles[i+1]
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if len(article_content) < 50:
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continue
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source = f"{doc_type}, {article_number}"
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source_mapper.add_source(source)
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data.append({
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"text": f"{article_number} {article_content}",
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"source_idx": source_mapper.get_idx(source)
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})
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else:
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clean_text = re.sub(r'\s+', ' ', text).strip()
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clean_text = re.sub(r'\s+', ' ', text).strip()
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chunk_size = 384
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chunks = [clean_text[i:i+512] for i in range(0, len(clean_text), 512)]
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overlap = 64
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chunks = [c for c in chunks if c.strip()]
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chunks = []
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for chunk in chunks:
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start = 0
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data.append({
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while start < len(clean_text):
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"text": chunk,
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end = start + chunk_size
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"source_idx": -1
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chunks.append(clean_text[start:end])
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})
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start = end - overlap
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print(f"Dodano {len(chunks)} chunków")
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return [f"[Custom] {chunk}" for chunk in chunks if chunk.strip()]
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except Exception as e:
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print(f"Błąd podczas przetwarzania pliku: {str(e)}")
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continue
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class CustomModel(torch.nn.Module):
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print(f"\nPodsumowanie przygotowania danych:")
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def __init__(self, model_name):
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print(f"Łączna liczba przykładów: {len(data)}")
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if data:
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print("Przykładowy wpis:")
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print(json.dumps(data[0], indent=2, ensure_ascii=False))
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else:
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print("BRAK DANYCH - sprawdź diagnostykę powyżej")
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return data
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class CustomModel(nn.Module):
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def __init__(self, model_name, config):
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super().__init__()
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super().__init__()
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self.base_model = AutoModelForCausalLM.from_pretrained(model_name)
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self.base_model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
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self.source_emb = torch.nn.Embedding(1000, self.base_model.config.hidden_size)
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self.source_embedding = nn.Embedding(10000, config.hidden_size, padding_idx=-1)
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# Zamrożenie parametrów bazowych
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for param in self.base_model.parameters():
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for param in self.base_model.parameters():
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param.requires_grad = False
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param.requires_grad = False
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for param in self.base_model.get_output_embeddings().parameters():
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# Odmrożenie ostatnich warstw
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for layer in self.base_model.transformer.h[-2:]:
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for param in layer.parameters():
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param.requires_grad = True
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param.requires_grad = True
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self.base_model.get_output_embeddings().requires_grad_(True)
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def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None, **kwargs):
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if source_idx is not None:
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def forward(self, input_ids, attention_mask, labels, source_idx):
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valid_indices = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings-1)
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inputs_embeds = self.base_model.get_input_embeddings()(input_ids)
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source_embeds = self.source_embedding(valid_indices).unsqueeze(1)
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source_emb = self.source_emb(source_idx.clamp(0, 999)).unsqueeze(1)
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inputs_embeds = self.base_model.get_input_embeddings()(input_ids) + source_embeds
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inputs_embeds += source_emb
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return self.base_model(
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return self.base_model(
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inputs_embeds=inputs_embeds,
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inputs_embeds=inputs_embeds,
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attention_mask=attention_mask,
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attention_mask=attention_mask,
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labels=labels
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labels=labels,
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**kwargs
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)
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return self.base_model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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labels=labels,
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**kwargs
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)
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)
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def generate(self, *args, **kwargs):
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return self.base_model.generate(*args, **kwargs)
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class CustomDataCollator(DataCollatorForLanguageModeling):
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def torch_call(self, examples):
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# Przetwórz podstawowe pola
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input_ids = torch.stack([torch.tensor(ex["input_ids"]) for ex in examples])
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attention_mask = torch.stack([torch.tensor(ex["attention_mask"]) for ex in examples])
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labels = torch.stack([torch.tensor(ex["labels"]) for ex in examples])
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batch = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"labels": labels
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}
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# Dodaj source_idx jeśli istnieje
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if "source_idx" in examples[0]:
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source_idx = torch.stack([torch.tensor(ex["source_idx"]) for ex in examples])
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batch["source_idx"] = source_idx
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return batch
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def main():
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def main():
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# Inicjalizacja komponentów
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source_mapper = SourceMapper()
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source_mapper = SourceMapper()
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processor = LegalProcessor("file_catalog.json")
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model_name = "crumb/nano-mistral"
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tokenizer = AutoTokenizer.from_pretrained("crumb/nano-mistral")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.pad_token = tokenizer.eos_token
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# Przetwarzanie danych
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# Przygotowanie danych
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data = []
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catalog_path = "file_catalog.json"
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data = prepare_dataset("files", catalog_path, source_mapper)
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def process_and_augment(file_path):
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if not data:
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try:
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print("\nBrak danych do treningu!")
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items = processor.process_file(file_path)
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return
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for text in items:
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source = text.split("]")[0][1:]
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source_mapper.add_source(source)
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# Oryginalny tekst
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data.append({
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"text": text,
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"source_idx": source_mapper.get_idx(source)
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})
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# Augmentacja
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augmented = processor.augmenter.augment(text)
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if augmented != text:
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data.append({
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"text": augmented,
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"source_idx": source_mapper.get_idx(source)
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})
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except Exception as e:
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print(f"Błąd przetwarzania {file_path}: {str(e)}")
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# Przetwarzanie wielowątkowe
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with ThreadPoolExecutor(max_workers=cpu_count()) as executor:
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futures = []
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for root, _, files in os.walk("files"):
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for file in files:
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file_path = os.path.join(root, file)
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futures.append(executor.submit(process_and_augment, file_path))
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for future in futures:
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future.result()
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print(f"\nPrzygotowano {len(data)} przykładów treningowych")
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print("Przykładowe dane:")
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for example in random.sample(data, 3):
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print(f"\nŹródło: {source_mapper.get_source(example['source_idx'])}")
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print(f"Tekst: {example['text'][:150]}...")
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# Przygotowanie datasetu
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dataset = Dataset.from_list(data)
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dataset = Dataset.from_list(data)
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def tokenize_fn(examples):
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def tokenize_function(examples):
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tokenized = tokenizer(
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tokenized = tokenizer(
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examples["text"],
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examples["text"],
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max_length=512,
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padding="max_length",
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truncation=True,
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truncation=True,
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padding="max_length",
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max_length=512,
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return_tensors="pt"
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return_tensors="pt"
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)
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)
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return {
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return {
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"input_ids": tokenized["input_ids"].squeeze(),
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"input_ids": tokenized["input_ids"].squeeze(),
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"attention_mask": tokenized["attention_mask"].squeeze(),
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"attention_mask": tokenized["attention_mask"].squeeze(),
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"labels": tokenized["input_ids"].squeeze(),
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"labels": tokenized["input_ids"].squeeze().clone(),
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"source_idx": examples["source_idx"]
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"source_idx": examples["source_idx"] # Dodano bez konwersji do tensora
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}
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}
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|
||||||
tokenized_ds = dataset.map(
|
tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=16)
|
||||||
tokenize_fn,
|
|
||||||
batched=True,
|
|
||||||
batch_size=32,
|
|
||||||
num_proc=4
|
|
||||||
)
|
|
||||||
|
|
||||||
# Inicjalizacja modelu
|
model = CustomModel(model_name, AutoModelForCausalLM.from_pretrained(model_name).config)
|
||||||
model = CustomModel("crumb/nano-mistral")
|
model.source_mapper = source_mapper
|
||||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||||
model.to(device)
|
model.to(device)
|
||||||
|
|
||||||
# Konfiguracja treningu
|
|
||||||
training_args = TrainingArguments(
|
training_args = TrainingArguments(
|
||||||
output_dir="./wyniki",
|
output_dir="./results",
|
||||||
num_train_epochs=5,
|
num_train_epochs=3,
|
||||||
per_device_train_batch_size=2,
|
per_device_train_batch_size=2,
|
||||||
gradient_accumulation_steps=8,
|
gradient_accumulation_steps=4,
|
||||||
learning_rate=2e-5,
|
learning_rate=2e-5,
|
||||||
fp16=torch.cuda.is_available(),
|
fp16=torch.cuda.is_available(),
|
||||||
logging_steps=20,
|
logging_steps=10,
|
||||||
save_strategy="epoch",
|
save_strategy="steps",
|
||||||
report_to="none"
|
save_steps=1000,
|
||||||
|
report_to="none",
|
||||||
|
remove_unused_columns=False
|
||||||
)
|
)
|
||||||
|
|
||||||
trainer = Trainer(
|
trainer = Trainer(
|
||||||
model=model,
|
model=model,
|
||||||
args=training_args,
|
args=training_args,
|
||||||
train_dataset=tokenized_ds,
|
train_dataset=tokenized_dataset,
|
||||||
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
|
data_collator=CustomDataCollator(tokenizer=tokenizer, mlm=False)
|
||||||
)
|
)
|
||||||
|
|
||||||
# Trening
|
print("\nRozpoczęcie treningu...")
|
||||||
print("\nRozpoczynanie treningu...")
|
|
||||||
trainer.train()
|
trainer.train()
|
||||||
|
|
||||||
# Zapis modelu
|
|
||||||
model.save_pretrained("./trained_legal_model")
|
|
||||||
tokenizer.save_pretrained("./trained_legal_model")
|
|
||||||
print("Trening zakończony pomyślnie!")
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
main()
|
main()
|
||||||
Loading…
Reference in New Issue