TEN KOD DZIAŁA
This commit is contained in:
l.gabrysiak
parent
999eded568
commit
9004cd8cc1
412
hft.py
412
hft.py
|
|
@ -1,35 +1,21 @@
|
|||
import nltk
|
||||
nltk.download('averaged_perceptron_tagger', quiet=True)
|
||||
nltk.download('wordnet', quiet=True)
|
||||
nltk.download('punkt', quiet=True)
|
||||
|
||||
import os
|
||||
import torch
|
||||
import random
|
||||
import torch.nn as nn
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
|
||||
from datasets import Dataset
|
||||
import re
|
||||
import json
|
||||
import PyPDF2
|
||||
import docx2txt
|
||||
import pytesseract
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from collections import defaultdict
|
||||
from multiprocessing import cpu_count
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
AutoModelForCausalLM,
|
||||
TrainingArguments,
|
||||
Trainer,
|
||||
DataCollatorForLanguageModeling
|
||||
)
|
||||
from datasets import Dataset
|
||||
from nlpaug.augmenter.word import SynonymAug
|
||||
from huggingface_hub import login
|
||||
|
||||
# Konfiguracja
|
||||
os.environ['TORCH_USE_CUDA_DSA'] = '1'
|
||||
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
||||
login(token="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") # Zastąp swoim tokenem
|
||||
login(token="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")
|
||||
|
||||
class SourceMapper:
|
||||
def __init__(self):
|
||||
|
|
@ -47,237 +33,227 @@ class SourceMapper:
|
|||
def get_source(self, idx):
|
||||
return self.idx_to_source.get(idx, "Unknown")
|
||||
|
||||
class LegalProcessor:
|
||||
def __init__(self, catalog_path):
|
||||
self.catalog = self.load_catalog(catalog_path)
|
||||
self.augmenter = SynonymAug(aug_src='wordnet', aug_max=3, lang='pol')
|
||||
def load_file_catalog(catalog_path):
|
||||
try:
|
||||
with open(catalog_path, 'r', encoding='utf-8') as file:
|
||||
return json.load(file)
|
||||
except Exception as e:
|
||||
print(f"Błąd wczytywania katalogu plików: {str(e)}")
|
||||
return {}
|
||||
|
||||
def identify_legal_document(filename, file_catalog):
|
||||
base_name = os.path.splitext(filename)[0].lower()
|
||||
return file_catalog.get(base_name, "Opracowanie własne")
|
||||
|
||||
def extract_text_from_file(file_path):
|
||||
try:
|
||||
_, ext = os.path.splitext(file_path)
|
||||
ext = ext.lower()
|
||||
|
||||
def load_catalog(self, path):
|
||||
try:
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except:
|
||||
return defaultdict(str)
|
||||
|
||||
def process_file(self, file_path):
|
||||
text = self.extract_text(file_path)
|
||||
if not text:
|
||||
return []
|
||||
|
||||
doc_type = self.identify_doc_type(file_path)
|
||||
return self.split_content(text, doc_type)
|
||||
|
||||
def extract_text(self, file_path):
|
||||
ext = os.path.splitext(file_path)[1].lower()
|
||||
try:
|
||||
if ext == '.pdf':
|
||||
return self.extract_pdf(file_path)
|
||||
elif ext in ['.doc', '.docx']:
|
||||
return docx2txt.process(file_path)
|
||||
elif ext in ['.jpg', '.jpeg', '.png']:
|
||||
return self.extract_image(file_path)
|
||||
else:
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
return f.read()
|
||||
except Exception as e:
|
||||
print(f"Błąd przetwarzania {file_path}: {str(e)}")
|
||||
if ext in ['.txt', '.md']:
|
||||
with open(file_path, 'r', encoding='utf-8') as file:
|
||||
return file.read()
|
||||
elif ext == '.pdf':
|
||||
text = ""
|
||||
try:
|
||||
with open(file_path, 'rb') as file:
|
||||
reader = PyPDF2.PdfReader(file)
|
||||
for page in reader.pages:
|
||||
text += page.extract_text() or ""
|
||||
except Exception as e:
|
||||
print(f"Błąd PDF: {str(e)}")
|
||||
return text
|
||||
elif ext in ['.doc', '.docx']:
|
||||
return docx2txt.process(file_path)
|
||||
elif ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
|
||||
return pytesseract.image_to_string(Image.open(file_path))
|
||||
else:
|
||||
print(f"Nieobsługiwany format pliku: {ext}")
|
||||
return ""
|
||||
|
||||
def extract_pdf(self, path):
|
||||
text = ""
|
||||
with open(path, 'rb') as f:
|
||||
reader = PyPDF2.PdfReader(f)
|
||||
for page in reader.pages:
|
||||
text += page.extract_text() + "\n"
|
||||
return re.sub(r'\s+', ' ', text)
|
||||
|
||||
def extract_image(self, path):
|
||||
return pytesseract.image_to_string(
|
||||
Image.open(path),
|
||||
config='--psm 4 --oem 3 -c preserve_interword_spaces=1'
|
||||
)
|
||||
|
||||
def identify_doc_type(self, file_path):
|
||||
base = os.path.splitext(os.path.basename(file_path))[0].lower()
|
||||
return self.catalog.get(base, "Custom")
|
||||
|
||||
def split_content(self, text, doc_type):
|
||||
if doc_type == "Custom":
|
||||
return self.split_custom(text)
|
||||
return self.split_legal(text, doc_type)
|
||||
|
||||
def split_legal(self, text, doc_type):
|
||||
pattern = r'(?i)(Art[\.\s]*\d+[a-z]*|§\s*\d+|Rozdział\s+[IVXLCDM]+)'
|
||||
parts = re.split(pattern, text)
|
||||
results = []
|
||||
current_header = ""
|
||||
|
||||
for part in parts:
|
||||
if not part:
|
||||
continue
|
||||
if re.match(pattern, part):
|
||||
if current_header:
|
||||
results.append(current_header)
|
||||
current_header = f"[{doc_type}] {part.strip()}"
|
||||
else:
|
||||
if current_header:
|
||||
results.append(f"{current_header}: {part.strip()}")
|
||||
current_header = ""
|
||||
else:
|
||||
results.append(part.strip())
|
||||
|
||||
return [text for text in results if len(text) > 50]
|
||||
|
||||
def split_custom(self, text):
|
||||
clean_text = re.sub(r'\s+', ' ', text).strip()
|
||||
chunk_size = 384
|
||||
overlap = 64
|
||||
|
||||
chunks = []
|
||||
start = 0
|
||||
while start < len(clean_text):
|
||||
end = start + chunk_size
|
||||
chunks.append(clean_text[start:end])
|
||||
start = end - overlap
|
||||
|
||||
return [f"[Custom] {chunk}" for chunk in chunks if chunk.strip()]
|
||||
except Exception as e:
|
||||
print(f"Błąd ekstrakcji tekstu: {str(e)}")
|
||||
return ""
|
||||
|
||||
class CustomModel(torch.nn.Module):
|
||||
def __init__(self, model_name):
|
||||
super().__init__()
|
||||
self.base_model = AutoModelForCausalLM.from_pretrained(model_name)
|
||||
self.source_emb = torch.nn.Embedding(1000, self.base_model.config.hidden_size)
|
||||
|
||||
# Zamrożenie parametrów bazowych
|
||||
for param in self.base_model.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
# Odmrożenie ostatnich warstw
|
||||
for layer in self.base_model.transformer.h[-2:]:
|
||||
for param in layer.parameters():
|
||||
param.requires_grad = True
|
||||
|
||||
self.base_model.get_output_embeddings().requires_grad_(True)
|
||||
|
||||
def forward(self, input_ids, attention_mask, labels, source_idx):
|
||||
inputs_embeds = self.base_model.get_input_embeddings()(input_ids)
|
||||
source_emb = self.source_emb(source_idx.clamp(0, 999)).unsqueeze(1)
|
||||
inputs_embeds += source_emb
|
||||
|
||||
return self.base_model(
|
||||
inputs_embeds=inputs_embeds,
|
||||
attention_mask=attention_mask,
|
||||
labels=labels
|
||||
)
|
||||
|
||||
def main():
|
||||
# Inicjalizacja komponentów
|
||||
source_mapper = SourceMapper()
|
||||
processor = LegalProcessor("file_catalog.json")
|
||||
tokenizer = AutoTokenizer.from_pretrained("crumb/nano-mistral")
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
|
||||
# Przetwarzanie danych
|
||||
def prepare_dataset(directory, catalog_path, source_mapper):
|
||||
file_catalog = load_file_catalog(catalog_path)
|
||||
data = []
|
||||
|
||||
def process_and_augment(file_path):
|
||||
try:
|
||||
items = processor.process_file(file_path)
|
||||
for text in items:
|
||||
source = text.split("]")[0][1:]
|
||||
source_mapper.add_source(source)
|
||||
|
||||
# Oryginalny tekst
|
||||
data.append({
|
||||
"text": text,
|
||||
"source_idx": source_mapper.get_idx(source)
|
||||
})
|
||||
|
||||
# Augmentacja
|
||||
augmented = processor.augmenter.augment(text)
|
||||
if augmented != text:
|
||||
data.append({
|
||||
"text": augmented,
|
||||
"source_idx": source_mapper.get_idx(source)
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"Błąd przetwarzania {file_path}: {str(e)}")
|
||||
print(f"\n{'='*50}\nDIAGNOSTYKA DANYCH\n{'='*50}")
|
||||
|
||||
# Przetwarzanie wielowątkowe
|
||||
with ThreadPoolExecutor(max_workers=cpu_count()) as executor:
|
||||
futures = []
|
||||
for root, _, files in os.walk("files"):
|
||||
for file in files:
|
||||
file_path = os.path.join(root, file)
|
||||
futures.append(executor.submit(process_and_augment, file_path))
|
||||
for root, _, files in os.walk(directory):
|
||||
for file in files:
|
||||
file_path = os.path.join(root, file)
|
||||
print(f"\nPrzetwarzanie pliku: {file_path}")
|
||||
|
||||
try:
|
||||
text = extract_text_from_file(file_path)
|
||||
if not text.strip():
|
||||
print("Pominięto - brak tekstu")
|
||||
continue
|
||||
|
||||
print(f"Długość tekstu: {len(text)} znaków")
|
||||
|
||||
for future in futures:
|
||||
future.result()
|
||||
doc_type = identify_legal_document(file, file_catalog)
|
||||
print(f"Rozpoznany typ dokumentu: {doc_type}")
|
||||
|
||||
if doc_type != "Opracowanie własne":
|
||||
articles = re.split(r'(?i)(Art[\.\s]+\d+[\.\s]?)', text)
|
||||
articles = [a.strip() for a in articles if a.strip()]
|
||||
|
||||
print(f"Znaleziono {len(articles)} fragmentów")
|
||||
|
||||
for i in range(0, len(articles)-1, 2):
|
||||
article_number = articles[i]
|
||||
article_content = articles[i+1]
|
||||
|
||||
if len(article_content) < 50:
|
||||
continue
|
||||
|
||||
source = f"{doc_type}, {article_number}"
|
||||
source_mapper.add_source(source)
|
||||
data.append({
|
||||
"text": f"{article_number} {article_content}",
|
||||
"source_idx": source_mapper.get_idx(source)
|
||||
})
|
||||
else:
|
||||
clean_text = re.sub(r'\s+', ' ', text).strip()
|
||||
chunks = [clean_text[i:i+512] for i in range(0, len(clean_text), 512)]
|
||||
chunks = [c for c in chunks if c.strip()]
|
||||
|
||||
for chunk in chunks:
|
||||
data.append({
|
||||
"text": chunk,
|
||||
"source_idx": -1
|
||||
})
|
||||
print(f"Dodano {len(chunks)} chunków")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Błąd podczas przetwarzania pliku: {str(e)}")
|
||||
continue
|
||||
|
||||
print(f"\nPodsumowanie przygotowania danych:")
|
||||
print(f"Łączna liczba przykładów: {len(data)}")
|
||||
if data:
|
||||
print("Przykładowy wpis:")
|
||||
print(json.dumps(data[0], indent=2, ensure_ascii=False))
|
||||
else:
|
||||
print("BRAK DANYCH - sprawdź diagnostykę powyżej")
|
||||
|
||||
return data
|
||||
|
||||
print(f"\nPrzygotowano {len(data)} przykładów treningowych")
|
||||
print("Przykładowe dane:")
|
||||
for example in random.sample(data, 3):
|
||||
print(f"\nŹródło: {source_mapper.get_source(example['source_idx'])}")
|
||||
print(f"Tekst: {example['text'][:150]}...")
|
||||
class CustomModel(nn.Module):
|
||||
def __init__(self, model_name, config):
|
||||
super().__init__()
|
||||
self.base_model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
|
||||
self.source_embedding = nn.Embedding(10000, config.hidden_size, padding_idx=-1)
|
||||
|
||||
for param in self.base_model.parameters():
|
||||
param.requires_grad = False
|
||||
for param in self.base_model.get_output_embeddings().parameters():
|
||||
param.requires_grad = True
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None, **kwargs):
|
||||
if source_idx is not None:
|
||||
valid_indices = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings-1)
|
||||
source_embeds = self.source_embedding(valid_indices).unsqueeze(1)
|
||||
inputs_embeds = self.base_model.get_input_embeddings()(input_ids) + source_embeds
|
||||
return self.base_model(
|
||||
inputs_embeds=inputs_embeds,
|
||||
attention_mask=attention_mask,
|
||||
labels=labels,
|
||||
**kwargs
|
||||
)
|
||||
return self.base_model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
labels=labels,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
def generate(self, *args, **kwargs):
|
||||
return self.base_model.generate(*args, **kwargs)
|
||||
|
||||
class CustomDataCollator(DataCollatorForLanguageModeling):
|
||||
def torch_call(self, examples):
|
||||
# Przetwórz podstawowe pola
|
||||
input_ids = torch.stack([torch.tensor(ex["input_ids"]) for ex in examples])
|
||||
attention_mask = torch.stack([torch.tensor(ex["attention_mask"]) for ex in examples])
|
||||
labels = torch.stack([torch.tensor(ex["labels"]) for ex in examples])
|
||||
|
||||
batch = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"labels": labels
|
||||
}
|
||||
|
||||
# Dodaj source_idx jeśli istnieje
|
||||
if "source_idx" in examples[0]:
|
||||
source_idx = torch.stack([torch.tensor(ex["source_idx"]) for ex in examples])
|
||||
batch["source_idx"] = source_idx
|
||||
|
||||
return batch
|
||||
|
||||
def main():
|
||||
source_mapper = SourceMapper()
|
||||
model_name = "crumb/nano-mistral"
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
|
||||
# Przygotowanie danych
|
||||
catalog_path = "file_catalog.json"
|
||||
data = prepare_dataset("files", catalog_path, source_mapper)
|
||||
|
||||
if not data:
|
||||
print("\nBrak danych do treningu!")
|
||||
return
|
||||
|
||||
# Przygotowanie datasetu
|
||||
dataset = Dataset.from_list(data)
|
||||
|
||||
def tokenize_fn(examples):
|
||||
|
||||
def tokenize_function(examples):
|
||||
tokenized = tokenizer(
|
||||
examples["text"],
|
||||
max_length=512,
|
||||
padding="max_length",
|
||||
truncation=True,
|
||||
padding="max_length",
|
||||
max_length=512,
|
||||
return_tensors="pt"
|
||||
)
|
||||
return {
|
||||
"input_ids": tokenized["input_ids"].squeeze(),
|
||||
"attention_mask": tokenized["attention_mask"].squeeze(),
|
||||
"labels": tokenized["input_ids"].squeeze(),
|
||||
"source_idx": examples["source_idx"]
|
||||
"labels": tokenized["input_ids"].squeeze().clone(),
|
||||
"source_idx": examples["source_idx"] # Dodano bez konwersji do tensora
|
||||
}
|
||||
|
||||
tokenized_ds = dataset.map(
|
||||
tokenize_fn,
|
||||
batched=True,
|
||||
batch_size=32,
|
||||
num_proc=4
|
||||
)
|
||||
|
||||
# Inicjalizacja modelu
|
||||
model = CustomModel("crumb/nano-mistral")
|
||||
tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=16)
|
||||
|
||||
model = CustomModel(model_name, AutoModelForCausalLM.from_pretrained(model_name).config)
|
||||
model.source_mapper = source_mapper
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
model.to(device)
|
||||
|
||||
# Konfiguracja treningu
|
||||
|
||||
training_args = TrainingArguments(
|
||||
output_dir="./wyniki",
|
||||
num_train_epochs=5,
|
||||
output_dir="./results",
|
||||
num_train_epochs=3,
|
||||
per_device_train_batch_size=2,
|
||||
gradient_accumulation_steps=8,
|
||||
gradient_accumulation_steps=4,
|
||||
learning_rate=2e-5,
|
||||
fp16=torch.cuda.is_available(),
|
||||
logging_steps=20,
|
||||
save_strategy="epoch",
|
||||
report_to="none"
|
||||
logging_steps=10,
|
||||
save_strategy="steps",
|
||||
save_steps=1000,
|
||||
report_to="none",
|
||||
remove_unused_columns=False
|
||||
)
|
||||
|
||||
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=tokenized_ds,
|
||||
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
|
||||
train_dataset=tokenized_dataset,
|
||||
data_collator=CustomDataCollator(tokenizer=tokenizer, mlm=False)
|
||||
)
|
||||
|
||||
# Trening
|
||||
print("\nRozpoczynanie treningu...")
|
||||
|
||||
print("\nRozpoczęcie treningu...")
|
||||
trainer.train()
|
||||
|
||||
# Zapis modelu
|
||||
model.save_pretrained("./trained_legal_model")
|
||||
tokenizer.save_pretrained("./trained_legal_model")
|
||||
print("Trening zakończony pomyślnie!")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue