mod
This commit is contained in:
l.gabrysiak
parent
5f06f859a5
commit
b1512778d3
448
hft.py
448
hft.py
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@ -1,8 +1,6 @@
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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 torch.nn as nn
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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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@ -10,252 +8,286 @@ import docx2txt
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import pytesseract
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import pytesseract
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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 transformers import (
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AutoTokenizer,
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AutoModelForCausalLM,
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TrainingArguments,
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DataCollatorForLanguageModeling
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)
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from datasets import Dataset
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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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")
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login(token="TWÓJ_TOKEN_HUGGINGFACE")
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class SourceMapper:
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class LegalAITrainer:
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def __init__(self):
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def __init__(self):
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self.source_to_idx = defaultdict(lambda: len(self.source_to_idx))
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self.source_mapper = defaultdict(lambda: len(self.source_mapper))
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self.idx_to_source = {}
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self.idx_to_source = {}
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def add_source(self, source):
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if source and source not in self.source_to_idx:
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class SourceMapper:
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idx = self.source_to_idx[source]
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def __init__(self):
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self.idx_to_source[idx] = source
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self.source_to_idx = defaultdict(lambda: len(self.source_to_idx))
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self.idx_to_source = {}
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def add_source(self, source):
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if source and source not in self.source_to_idx:
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idx = self.source_to_idx[source]
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self.idx_to_source[idx] = source
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def get_idx(self, source):
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return self.source_to_idx[source] if source else -1
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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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class LegalModel(nn.Module):
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def __init__(self, model_name, config):
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super().__init__()
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self.base_model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
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self.source_embedding = nn.Embedding(100000, config.hidden_size, padding_idx=-1)
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self.confidence_layer = nn.Linear(config.hidden_size, 1)
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def get_idx(self, source):
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# Freeze base model
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return self.source_to_idx[source] if source else -1
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for param in self.base_model.parameters():
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param.requires_grad = False
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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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# Trainable components
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for layer in [self.source_embedding, self.confidence_layer]:
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for param in layer.parameters():
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param.requires_grad = True
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def load_file_catalog(catalog_path):
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def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None):
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try:
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if source_idx is not None:
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with open(catalog_path, 'r', encoding='utf-8') as file:
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source_idx = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings-1)
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return json.load(file)
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source_embeds = self.source_embedding(source_idx).unsqueeze(1)
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except Exception as e:
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inputs_embeds = self.base_model.get_input_embeddings()(input_ids) + source_embeds
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print(f"Błąd wczytywania katalogu plików: {str(e)}")
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outputs = self.base_model(
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return {}
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inputs_embeds=inputs_embeds,
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attention_mask=attention_mask,
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labels=labels
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)
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else:
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outputs = 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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)
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confidence = torch.sigmoid(self.confidence_layer(outputs.hidden_states[-1].mean(dim=1)))
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return {
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"loss": outputs.loss,
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"logits": outputs.logits,
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"confidence": confidence,
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"hidden_states": outputs.hidden_states
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}
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def identify_legal_document(filename, file_catalog):
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def load_file_catalog(self, catalog_path):
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base_name = os.path.splitext(filename)[0].lower()
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try:
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return file_catalog.get(base_name, "Opracowanie własne")
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with open(catalog_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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except Exception as e:
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print(f"Błąd ładowania katalogu: {str(e)}")
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return {}
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def extract_text_from_file(file_path):
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def extract_text(self, file_path):
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try:
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ext = os.path.splitext(file_path)[1].lower()
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_, ext = os.path.splitext(file_path)
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try:
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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 f:
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if ext in ['.txt', '.md']:
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return f.read()
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with open(file_path, 'r', encoding='utf-8') as file:
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elif ext == '.pdf':
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return file.read()
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text = ""
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elif ext == '.pdf':
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with open(file_path, 'rb') as f:
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text = ""
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reader = PyPDF2.PdfReader(f)
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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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for page in reader.pages:
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text += page.extract_text() or ""
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text += page.extract_text() or ""
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except Exception as e:
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return text
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print(f"Błąd PDF: {str(e)}")
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elif ext in ['.doc', '.docx']:
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return text
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return docx2txt.process(file_path)
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elif ext in ['.doc', '.docx']:
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elif ext in ['.jpg', '.jpeg', '.png']:
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return docx2txt.process(file_path)
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return pytesseract.image_to_string(Image.open(file_path))
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elif ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
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else:
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return pytesseract.image_to_string(Image.open(file_path))
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return ""
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else:
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except Exception as e:
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print(f"Nieobsługiwany format pliku: {ext}")
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print(f"Błąd przetwarzania {file_path}: {str(e)}")
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return ""
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return ""
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except Exception as e:
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print(f"Błąd ekstrakcji tekstu: {str(e)}")
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return ""
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def prepare_dataset(directory, catalog_path, source_mapper):
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def prepare_data(self, data_dir, catalog_path):
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file_catalog = load_file_catalog(catalog_path)
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catalog = self.load_file_catalog(catalog_path)
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data = []
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data = []
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source_mapper = self.SourceMapper()
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print(f"\n{'='*50}\nDIAGNOSTYKA DANYCH\n{'='*50}")
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for root, _, files in os.walk(data_dir):
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for root, _, files in os.walk(directory):
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for file in files:
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for file in files:
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file_path = os.path.join(root, file)
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file_path = os.path.join(root, file)
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text = self.extract_text(file_path)
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print(f"\nPrzetwarzanie pliku: {file_path}")
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try:
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text = extract_text_from_file(file_path)
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if not text.strip():
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print("Pominięto - brak tekstu")
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continue
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print(f"Długość tekstu: {len(text)} znaków")
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doc_type = identify_legal_document(file, file_catalog)
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if not text:
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print(f"Rozpoznany typ dokumentu: {doc_type}")
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continue
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doc_type = catalog.get(os.path.splitext(file)[0].lower(), "Opracowanie własne")
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if doc_type != "Opracowanie własne":
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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 = re.split(r'(?i)(Art\.\s*\d+[a-z]*)', text)
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articles = [a.strip() for a in articles if a.strip()]
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for i in range(1, len(articles), 2):
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art_num = articles[i].strip()
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print(f"Znaleziono {len(articles)} fragmentów")
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content = articles[i+1].strip()
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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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if len(content) < 100:
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continue
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continue
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source = f"{doc_type}, {article_number}"
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source = f"{doc_type}, {art_num}"
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source_mapper.add_source(source)
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source_mapper.add_source(source)
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data.append({
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data.append({
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"text": f"{article_number} {article_content}",
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"text": f"[LEGAL] {art_num} {content}",
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"source_idx": source_mapper.get_idx(source)
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"source_idx": source_mapper.get_idx(source),
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"is_legal": 1
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})
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})
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else:
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else:
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clean_text = re.sub(r'\s+', ' ', text).strip()
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chunks = [f"[GENERAL] {text[i:i+512]}" for i in range(0, len(text), 512)]
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chunks = [clean_text[i:i+512] for i in range(0, len(clean_text), 512)]
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chunks = [c for c in chunks if c.strip()]
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for chunk in chunks:
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for chunk in chunks:
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data.append({
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data.append({
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"text": chunk,
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"text": chunk,
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"source_idx": -1
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"source_idx": -1,
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"is_legal": 0
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})
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})
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print(f"Dodano {len(chunks)} chunków")
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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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print(f"\nPodsumowanie przygotowania danych:")
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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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return Dataset.from_dict({k: [d[k] for d in data] for k in data[0]}), source_mapper
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class CustomModel(nn.Module):
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def train(self, model_name="crumb/nano-mistral", data_dir="data", catalog_path="catalog.json"):
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def __init__(self, model_name, config):
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# Przygotowanie danych
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super().__init__()
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dataset, source_mapper = self.prepare_data(data_dir, catalog_path)
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self.base_model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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self.source_embedding = nn.Embedding(10000, config.hidden_size, padding_idx=-1)
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tokenizer.pad_token = tokenizer.eos_token
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for param in self.base_model.parameters():
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# Tokenizacja
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param.requires_grad = False
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def tokenize_fn(examples):
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for param in self.base_model.get_output_embeddings().parameters():
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tokenized = tokenizer(
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param.requires_grad = True
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examples["text"],
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padding="max_length",
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def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None, **kwargs):
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truncation=True,
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if source_idx is not None:
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max_length=512,
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valid_indices = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings-1)
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return_tensors="pt"
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source_embeds = self.source_embedding(valid_indices).unsqueeze(1)
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inputs_embeds = self.base_model.get_input_embeddings()(input_ids) + source_embeds
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return self.base_model(
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inputs_embeds=inputs_embeds,
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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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return self.base_model(
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return {
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input_ids=input_ids,
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"input_ids": tokenized["input_ids"].squeeze(),
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attention_mask=attention_mask,
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"attention_mask": tokenized["attention_mask"].squeeze(),
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labels=labels,
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"labels": tokenized["input_ids"].squeeze().clone(),
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**kwargs
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"source_idx": examples["source_idx"]
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}
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tokenized_dataset = dataset.map(tokenize_fn, batched=True, batch_size=16)
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# Inicjalizacja modelu
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config = AutoModelForCausalLM.from_pretrained(model_name).config
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model = self.LegalModel(model_name, config).to(self.device)
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# Konfiguracja treningu
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training_args = TrainingArguments(
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output_dir="./legal_ai_model",
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num_train_epochs=5,
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per_device_train_batch_size=2,
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gradient_accumulation_steps=4,
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learning_rate=2e-5,
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fp16=torch.cuda.is_available(),
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logging_steps=50,
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save_strategy="steps",
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save_steps=500,
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report_to="none",
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remove_unused_columns=False
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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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# Customowy Trainer
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def torch_call(self, examples):
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class LegalTrainer(Trainer):
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# Przetwórz podstawowe pola
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def compute_loss(self, model, inputs, return_outputs=False):
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input_ids = torch.stack([torch.tensor(ex["input_ids"]) for ex in examples])
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outputs = model(**inputs)
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attention_mask = torch.stack([torch.tensor(ex["attention_mask"]) for ex in examples])
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loss = outputs.loss
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labels = torch.stack([torch.tensor(ex["labels"]) for ex in examples])
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# Confidence loss
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target_conf = (inputs["source_idx"] != -1).float()
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conf_loss = nn.BCELoss()(outputs.confidence.squeeze(), target_conf)
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total_loss = loss + 0.7*conf_loss
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return (total_loss, outputs) if return_outputs else total_loss
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# Trening
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trainer = LegalTrainer(
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model=model,
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args=training_args,
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train_dataset=tokenized_dataset,
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data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
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)
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print("Rozpoczęcie treningu...")
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trainer.train()
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batch = {
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# Zapisz model
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"input_ids": input_ids,
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model.save_pretrained("./trained_legal_ai")
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"attention_mask": attention_mask,
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tokenizer.save_pretrained("./trained_legal_ai")
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"labels": labels
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with open("./trained_legal_ai/source_mapper.json", "w") as f:
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}
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json.dump(source_mapper.idx_to_source, f)
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print("Trening zakończony i model zapisany!")
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def generate_response(self, prompt, confidence_threshold=0.65):
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# Ładowanie modelu
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model = self.LegalModel.from_pretrained("./trained_legal_ai",
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config=AutoModelForCausalLM.from_pretrained("crumb/nano-mistral").config)
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tokenizer = AutoTokenizer.from_pretrained("./trained_legal_ai")
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model.to(self.device)
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# Dodaj source_idx jeśli istnieje
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# Ładowanie mapowania źródeł
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if "source_idx" in examples[0]:
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with open("./trained_legal_ai/source_mapper.json", "r") as f:
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source_idx = torch.stack([torch.tensor(ex["source_idx"]) for ex in examples])
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source_mapper = json.load(f)
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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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# Przygotowanie wejścia
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source_mapper = SourceMapper()
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inputs = tokenizer(
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model_name = "crumb/nano-mistral"
|
f"[PROMPT] {prompt} [RESPONSE]",
|
||||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
return_tensors="pt",
|
||||||
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
|
|
||||||
|
|
||||||
#dataset = Dataset.from_list(data)
|
|
||||||
dataset = Dataset.from_dict({k: [d[k] for d in data] for k in data[0]})
|
|
||||||
|
|
||||||
|
|
||||||
def tokenize_function(examples):
|
|
||||||
tokenized = tokenizer(
|
|
||||||
examples["text"],
|
|
||||||
truncation=True,
|
|
||||||
padding="max_length",
|
|
||||||
max_length=512,
|
max_length=512,
|
||||||
return_tensors="pt"
|
truncation=True
|
||||||
)
|
).to(self.device)
|
||||||
return {
|
|
||||||
"input_ids": tokenized["input_ids"].squeeze(),
|
|
||||||
"attention_mask": tokenized["attention_mask"].squeeze(),
|
|
||||||
"labels": tokenized["input_ids"].squeeze().clone(),
|
|
||||||
"source_idx": examples["source_idx"] # Dodano bez konwersji do tensora
|
|
||||||
}
|
|
||||||
|
|
||||||
tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=16)
|
# Generacja
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model.generate(
|
||||||
|
input_ids=inputs.input_ids,
|
||||||
|
attention_mask=inputs.attention_mask,
|
||||||
|
max_length=512,
|
||||||
|
do_sample=True,
|
||||||
|
temperature=0.7,
|
||||||
|
top_k=50,
|
||||||
|
pad_token_id=tokenizer.eos_token_id,
|
||||||
|
output_scores=True,
|
||||||
|
return_dict_in_generate=True
|
||||||
|
)
|
||||||
|
|
||||||
model = CustomModel(model_name, AutoModelForCausalLM.from_pretrained(model_name).config)
|
# Analiza wyników
|
||||||
model.source_mapper = source_mapper
|
full_text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
|
||||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
confidence = torch.sigmoid(outputs.scores[-1][:, tokenizer.eos_token_id]).item()
|
||||||
model.to(device)
|
|
||||||
|
# Ekstrakcja i weryfikacja źródeł
|
||||||
|
citations = list(set(re.findall(r"Art\.\s*\d+[a-z]*", full_text)))
|
||||||
|
verified = [c for c in citations if any(c in s for s in source_mapper.values())]
|
||||||
|
|
||||||
training_args = TrainingArguments(
|
if confidence < confidence_threshold or not verified:
|
||||||
output_dir="./results",
|
return "Nie mogę udzielić jednoznacznej odpowiedzi na podstawie dostępnych danych."
|
||||||
num_train_epochs=3,
|
else:
|
||||||
per_device_train_batch_size=2,
|
return f"{full_text}\n\nPotwierdzone źródła: {', '.join(verified)}"
|
||||||
gradient_accumulation_steps=4,
|
|
||||||
learning_rate=2e-5,
|
|
||||||
fp16=torch.cuda.is_available(),
|
|
||||||
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_dataset,
|
|
||||||
data_collator=CustomDataCollator(tokenizer=tokenizer, mlm=False)
|
|
||||||
)
|
|
||||||
|
|
||||||
print("\nRozpoczęcie treningu...")
|
|
||||||
trainer.train()
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
main()
|
legal_ai = LegalAITrainer()
|
||||||
|
|
||||||
|
# Etap 1: Trening
|
||||||
|
legal_ai.train(
|
||||||
|
model_name="crumb/nano-mistral",
|
||||||
|
data_dir="./legal_docs",
|
||||||
|
catalog_path="./catalog.json"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Etap 2: Testowanie
|
||||||
|
test_prompt = "Ile dni urlopu przysługuje po 5 latach pracy w pełnym wymiarze?"
|
||||||
|
print(legal_ai.generate_response(test_prompt))
|
||||||
Loading…
Reference in New Issue