286 lines
11 KiB
Python
286 lines
11 KiB
Python
import os
|
|
import torch
|
|
import torch.nn as nn
|
|
import re
|
|
import json
|
|
import PyPDF2
|
|
import docx2txt
|
|
import pytesseract
|
|
from PIL import Image
|
|
from collections import defaultdict
|
|
from transformers import (
|
|
AutoTokenizer,
|
|
AutoModelForCausalLM,
|
|
TrainingArguments,
|
|
Trainer,
|
|
DataCollatorForLanguageModeling
|
|
)
|
|
from datasets import Dataset, Features, Value
|
|
from huggingface_hub import login
|
|
|
|
# Konfiguracja
|
|
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
|
login(token="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")
|
|
|
|
class LegalAITrainer:
|
|
def __init__(self):
|
|
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|
|
|
class SourceMapper:
|
|
def __init__(self):
|
|
self.source_to_idx = defaultdict(lambda: len(self.source_to_idx))
|
|
self.idx_to_source = {}
|
|
|
|
def add_source(self, source):
|
|
if source and source not in self.source_to_idx:
|
|
idx = self.source_to_idx[source]
|
|
self.idx_to_source[idx] = source
|
|
|
|
def get_idx(self, source):
|
|
return self.source_to_idx[source] if source else -1
|
|
|
|
def get_source(self, idx):
|
|
return self.idx_to_source.get(idx, "Unknown")
|
|
|
|
class LegalModel(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(100000, config.hidden_size, padding_idx=-1)
|
|
self.confidence_layer = nn.Linear(config.hidden_size, 1)
|
|
|
|
for param in self.base_model.parameters():
|
|
param.requires_grad = False
|
|
|
|
for layer in [self.source_embedding, self.confidence_layer]:
|
|
for param in layer.parameters():
|
|
param.requires_grad = True
|
|
|
|
def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None):
|
|
if source_idx is not None:
|
|
source_idx = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings-1)
|
|
source_embeds = self.source_embedding(source_idx).unsqueeze(1)
|
|
inputs_embeds = self.base_model.get_input_embeddings()(input_ids) + source_embeds
|
|
outputs = self.base_model(
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
labels=labels
|
|
)
|
|
else:
|
|
outputs = self.base_model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
labels=labels
|
|
)
|
|
|
|
confidence = torch.sigmoid(self.confidence_layer(outputs.hidden_states[-1].mean(dim=1)))
|
|
return {
|
|
"loss": outputs.loss,
|
|
"logits": outputs.logits,
|
|
"confidence": confidence,
|
|
"hidden_states": outputs.hidden_states
|
|
}
|
|
|
|
def load_file_catalog(self, catalog_path):
|
|
try:
|
|
with open(catalog_path, 'r', encoding='utf-8') as f:
|
|
return json.load(f)
|
|
except Exception as e:
|
|
print(f"Błąd ładowania katalogu: {str(e)}")
|
|
return {}
|
|
|
|
def extract_text(self, file_path):
|
|
ext = os.path.splitext(file_path)[1].lower()
|
|
try:
|
|
if ext in ['.txt', '.md']:
|
|
with open(file_path, 'r', encoding='utf-8') as f:
|
|
return f.read()
|
|
elif ext == '.pdf':
|
|
text = ""
|
|
with open(file_path, 'rb') as f:
|
|
reader = PyPDF2.PdfReader(f)
|
|
for page in reader.pages:
|
|
text += page.extract_text() or ""
|
|
return text
|
|
elif ext in ['.doc', '.docx']:
|
|
return docx2txt.process(file_path)
|
|
elif ext in ['.jpg', '.jpeg', '.png']:
|
|
return pytesseract.image_to_string(Image.open(file_path))
|
|
else:
|
|
return ""
|
|
except Exception as e:
|
|
print(f"Błąd przetwarzania {file_path}: {str(e)}")
|
|
return ""
|
|
|
|
def prepare_data(self, data_dir, catalog_path):
|
|
catalog = self.load_file_catalog(catalog_path)
|
|
data = []
|
|
source_mapper = self.SourceMapper()
|
|
|
|
for root, _, files in os.walk(data_dir):
|
|
for file in files:
|
|
file_path = os.path.join(root, file)
|
|
text = self.extract_text(file_path)
|
|
|
|
if not text:
|
|
continue
|
|
|
|
doc_type = catalog.get(os.path.splitext(file)[0].lower(), "Opracowanie własne")
|
|
|
|
if doc_type != "Opracowanie własne":
|
|
articles = re.split(r'(?i)(Art\.\s*\d+[a-z]*)', text)
|
|
for i in range(1, len(articles), 2):
|
|
art_num = articles[i].strip()
|
|
content = articles[i+1].strip()
|
|
|
|
if len(content) < 100:
|
|
continue
|
|
|
|
source = f"{doc_type}, {art_num}"
|
|
source_mapper.add_source(source)
|
|
data.append({
|
|
"text": f"[LEGAL] {art_num} {content}",
|
|
"source_idx": source_mapper.get_idx(source),
|
|
"is_legal": 1
|
|
})
|
|
else:
|
|
chunks = [f"[GENERAL] {text[i:i+512]}" for i in range(0, len(text), 512)]
|
|
for chunk in chunks:
|
|
data.append({
|
|
"text": chunk,
|
|
"source_idx": -1,
|
|
"is_legal": 0
|
|
})
|
|
|
|
features = Features({
|
|
"text": Value("string"),
|
|
"source_idx": Value("int32"),
|
|
"is_legal": Value("int32")
|
|
})
|
|
|
|
return Dataset.from_dict({
|
|
"text": [d["text"] for d in data],
|
|
"source_idx": [d["source_idx"] for d in data],
|
|
"is_legal": [d["is_legal"] for d in data]
|
|
}, features=features), source_mapper
|
|
|
|
def train(self, model_name="crumb/nano-mistral", data_dir="data", catalog_path="catalog.json"):
|
|
dataset, source_mapper = self.prepare_data(data_dir, catalog_path)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
def tokenize_fn(examples):
|
|
tokenized = tokenizer(
|
|
examples["text"],
|
|
padding="max_length",
|
|
truncation=True,
|
|
max_length=512,
|
|
return_tensors="pt"
|
|
)
|
|
return {
|
|
"input_ids": tokenized["input_ids"].squeeze(),
|
|
"attention_mask": tokenized["attention_mask"].squeeze(),
|
|
"labels": tokenized["input_ids"].squeeze().clone(),
|
|
"source_idx": torch.tensor(examples["source_idx"], dtype=torch.int32)
|
|
}
|
|
|
|
tokenized_dataset = dataset.map(tokenize_fn, batched=True, batch_size=16)
|
|
|
|
config = AutoModelForCausalLM.from_pretrained(model_name).config
|
|
model = self.LegalModel(model_name, config).to(self.device)
|
|
|
|
training_args = TrainingArguments(
|
|
output_dir="./legal_ai_model",
|
|
num_train_epochs=3,
|
|
per_device_train_batch_size=2,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=2e-5,
|
|
fp16=torch.cuda.is_available(),
|
|
logging_steps=50,
|
|
save_strategy="steps",
|
|
save_steps=500,
|
|
report_to="none",
|
|
remove_unused_columns=False
|
|
)
|
|
|
|
class LegalTrainer(Trainer):
|
|
def compute_loss(self, model, inputs, return_outputs=False):
|
|
outputs = model(**inputs)
|
|
loss = outputs["loss"]
|
|
|
|
target_conf = (inputs["source_idx"] != -1).float()
|
|
conf_loss = nn.BCELoss()(outputs["confidence"].squeeze(), target_conf)
|
|
|
|
total_loss = loss + 0.7 * conf_loss
|
|
return (total_loss, outputs) if return_outputs else total_loss
|
|
|
|
trainer = LegalTrainer(
|
|
model=model,
|
|
args=training_args,
|
|
train_dataset=tokenized_dataset,
|
|
data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
|
|
)
|
|
|
|
print("Rozpoczęcie treningu...")
|
|
trainer.train()
|
|
|
|
model.save_pretrained("./trained_legal_ai")
|
|
tokenizer.save_pretrained("./trained_legal_ai")
|
|
with open("./trained_legal_ai/source_mapper.json", "w") as f:
|
|
json.dump(source_mapper.idx_to_source, f)
|
|
|
|
print("Trening zakończony!")
|
|
|
|
def generate_response(self, prompt, confidence_threshold=0.65):
|
|
model = self.LegalModel.from_pretrained(
|
|
"./trained_legal_ai",
|
|
config=AutoModelForCausalLM.from_pretrained("crumb/nano-mistral").config
|
|
).to(self.device)
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("./trained_legal_ai")
|
|
|
|
with open("./trained_legal_ai/source_mapper.json", "r") as f:
|
|
source_mapper = json.load(f)
|
|
|
|
inputs = tokenizer(
|
|
f"[PROMPT] {prompt} [RESPONSE]",
|
|
return_tensors="pt",
|
|
max_length=512,
|
|
truncation=True
|
|
).to(self.device)
|
|
|
|
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
|
|
)
|
|
|
|
full_text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
|
|
confidence = torch.sigmoid(outputs.scores[-1][:, tokenizer.eos_token_id]).item()
|
|
|
|
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())]
|
|
|
|
if confidence < confidence_threshold or not verified:
|
|
return "Nie mogę udzielić jednoznacznej odpowiedzi na podstawie dostępnych danych."
|
|
else:
|
|
return f"{full_text}\n\nPotwierdzone źródła: {', '.join(verified)}"
|
|
|
|
if __name__ == "__main__":
|
|
legal_ai = LegalAITrainer()
|
|
|
|
legal_ai.train(
|
|
model_name="crumb/nano-mistral",
|
|
data_dir="./legal_docs",
|
|
catalog_path="./catalog.json"
|
|
)
|
|
|
|
test_prompt = "Jakie są kary za prowadzenie pojazdu pod wpływem alkoholu?"
|
|
print(legal_ai.generate_response(test_prompt)) |