mod
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l.gabrysiak
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hft.py
212
hft.py
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@ -11,12 +11,11 @@ import pytesseract
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from PIL import Image
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from collections import defaultdict
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from huggingface_hub import login
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from torch.utils.data import DataLoader
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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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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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def __init__(self):
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@ -98,39 +97,39 @@ def prepare_dataset(directory, catalog_path, source_mapper):
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print(f"Rozpoznany typ dokumentu: {doc_type}")
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if doc_type != "Opracowanie własne":
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# Ulepszone wyrażenie regularne dla różnych formatów
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articles = re.split(r'(?i)(Art[^\S\n]*\.?[^\S\n]*\d+[^\S\n]*\.?)', text)
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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)//2} artykułów")
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print(f"Znaleziono {len(articles)} fragmentów")
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# Generowanie większej liczby przykładów
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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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print(f"Pominięto zbyt krótki artykuł: {article_number}")
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continue
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for chunk_size in [256, 512, 1024]: # Różne rozmiary chunków
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article_number = articles[i]
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article_content = articles[i+1]
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source = f"{doc_type}, {article_number}"
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print(f"Dodano artykuł: {source}")
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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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chunks = [article_content[j:j+chunk_size] for j in range(0, len(article_content), chunk_size//2)]
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chunks = [c for c in chunks if len(c) > 100]
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for chunk in chunks:
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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} {chunk}",
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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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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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data.append({
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"text": chunk,
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"source_idx": -1
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})
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print(f"Dodano {len(chunks)} chunków")
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for chunk_size in [256, 512, 768]: # Trzy różne rozmiary
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chunks = [clean_text[i:i+chunk_size] for i in range(0, len(clean_text), chunk_size//2)]
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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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data.append({
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"text": chunk,
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"source_idx": -1
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})
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print(f"Dodano {len(chunks)*3} 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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@ -150,44 +149,32 @@ class CustomModel(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(1000, config.hidden_size, padding_idx=-1)
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self.source_embedding = nn.Embedding(10000, config.hidden_size, padding_idx=-1)
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# Zamrożenie warstw bazowego modelu
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# Fine-tuning części modelu
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for param in self.base_model.parameters():
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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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param.requires_grad = True
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for param in self.base_model.get_input_embeddings().parameters():
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param.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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valid_indices = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings-1)
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source_embeds = torch.nn.functional.normalize(
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self.source_embedding(valid_indices),
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p=2,
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dim=-1
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).unsqueeze(1)
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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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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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return self.base_model(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels, **kwargs)
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return self.base_model(input_ids=input_ids, attention_mask=attention_mask, labels=labels, **kwargs)
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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 CustomTrainer(Trainer):
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def __init__(self, *args, **kwargs):
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self.tokenizer = kwargs.pop('tokenizer', None)
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super().__init__(*args, **kwargs)
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def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
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labels = inputs.pop("labels")
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source_idx = inputs.pop("source_idx", None)
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@ -195,72 +182,54 @@ class CustomTrainer(Trainer):
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return (outputs.loss, outputs) if return_outputs else outputs.loss
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def evaluate(self):
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val_questions = {
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"art1": "Jakie są prawa pracownika według art. 1?",
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"art2": "Kto jest pracownikiem według art. 2?",
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"art3": "Jakie są obowiązki pracodawcy według art. 3?"
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}
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questions = [
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"Jakie są prawa pracownika według art. 1?",
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"Kto jest pracownikiem według art. 2?",
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"Jakie są obowiązki pracodawcy według art. 3?"
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]
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model.eval()
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results = {}
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for key, question in val_questions.items():
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result = self.generate_answer(question)
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results[key] = result
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print("\nWyniki walidacji:")
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for key, val in results.items():
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print(f"\n{val_questions[key]}")
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print(f"Odpowiedź: {val['answer'][:200]}...")
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print(f"Źródła: {val['sources']}")
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print("\n" + "="*50 + "\nEWALUACJA\n" + "="*50)
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for q in questions:
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result = self.generate_answer(q)
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print(f"\nPYTANIE: {q}")
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print(f"ODPOWIEDŹ: {result['answer'][:500]}")
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print(f"ŹRÓDŁA: {', '.join(result['sources'])}")
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print("-"*80)
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return {"loss": 0.0}
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def generate_answer(self, question):
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tokenizer = self.tokenizer
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model = self.model
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device = model.base_model.device
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prompt = f"[PYTANIE PRAWNE] {question} [KONTEKST]"
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inputs = tokenizer(
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prompt,
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inputs = self.tokenizer(
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f"[PYTANIE] {question} [KONTEKST]",
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return_tensors="pt",
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truncation=True,
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max_length=512
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).to(device)
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).to(self.model.base_model.device)
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with torch.no_grad():
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outputs = model.generate(
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outputs = self.model.generate(
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**inputs,
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max_new_tokens=150,
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temperature=0.3,
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top_k=50,
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top_p=0.95,
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repetition_penalty=1.8,
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max_new_tokens=200,
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temperature=0.5,
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top_p=0.9,
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repetition_penalty=2.0,
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num_beams=3,
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no_repeat_ngram_size=4,
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early_stopping=True,
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pad_token_id=tokenizer.eos_token_id
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no_repeat_ngram_size=3
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)
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answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
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answer = answer.replace(prompt, "").strip()
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answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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answer = answer.split("[KONTEKST]")[-1].strip()
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sources = set()
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for match in re.finditer(r'(?i)art\.?\s*\d+\.?', answer):
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article_ref = match.group(0).strip().rstrip('.')
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for source in self.model.source_mapper.idx_to_source.values():
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for match in re.finditer(r'(?i)art\.?\s*\d+', answer):
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article_ref = match.group(0).strip()
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for idx, source in self.model.source_mapper.idx_to_source.items():
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if article_ref.lower() in source.lower():
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sources.add(source)
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return {
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"answer": answer,
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"sources": list(sources) if sources else ["Opracowanie własne"]
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}
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return {"answer": answer, "sources": list(sources)}
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def main():
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# Inicjalizacja
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source_mapper = SourceMapper()
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model_name = "crumb/nano-mistral"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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@ -271,70 +240,53 @@ def main():
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data = prepare_dataset("files", catalog_path, source_mapper)
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if not data:
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print("\nBrak danych do treningu! Sprawdź pliki w katalogu 'files' i diagnostykę powyżej.")
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print("\nBrak danych do treningu!")
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return
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dataset = Dataset.from_list(data)
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def tokenize_function(examples):
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tokenized = tokenizer(
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def tokenize(examples):
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return tokenizer(
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examples["text"],
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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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)
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return {
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"input_ids": tokenized["input_ids"][0],
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"attention_mask": tokenized["attention_mask"][0],
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"labels": tokenized["input_ids"][0].clone(),
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"source_idx": examples["source_idx"]
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}
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tokenized_dataset = dataset.map(tokenize, batched=True, batch_size=16)
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tokenized_dataset = dataset.map(tokenize_function, batched=False)
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def custom_collate_fn(features):
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return {
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"input_ids": torch.stack([torch.tensor(f["input_ids"]) for f in features]),
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"attention_mask": torch.stack([torch.tensor(f["attention_mask"]) for f in features]),
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"labels": torch.stack([torch.tensor(f["labels"]) for f in features]),
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"source_idx": torch.tensor([f["source_idx"] for f in features], dtype=torch.long)
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}
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# Model
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config = AutoModelForCausalLM.from_pretrained(model_name).config
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model = CustomModel(model_name, config)
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model.source_mapper = source_mapper # Dodanie mapowania źródeł do modelu
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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# Trening
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training_args = TrainingArguments(
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output_dir="./results",
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num_train_epochs=5,
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num_train_epochs=8,
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per_device_train_batch_size=4,
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gradient_accumulation_steps=2,
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learning_rate=1e-5,
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gradient_accumulation_steps=8,
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learning_rate=5e-6,
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weight_decay=0.01,
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warmup_ratio=0.1,
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fp16=torch.cuda.is_available(),
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logging_steps=10,
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logging_steps=50,
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save_strategy="epoch",
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evaluation_strategy="steps",
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eval_steps=500,
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eval_strategy="no",
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report_to="none",
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remove_unused_columns=False
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)
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model = CustomModel(model_name, AutoModelForCausalLM.from_pretrained(model_name).config)
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model.source_mapper = source_mapper
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model.to("cuda" if torch.cuda.is_available() else "cpu")
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trainer = CustomTrainer(
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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=custom_collate_fn,
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tokenizer=tokenizer
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)
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print("\nRozpoczęcie treningu...")
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trainer.train()
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print("\nKońcowa ewaluacja...")
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trainer.evaluate()
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if __name__ == "__main__":
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