mod
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l.gabrysiak
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9afa461252
172
hft.py
172
hft.py
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@ -1,7 +1,7 @@
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import os
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import torch
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import torch.nn as nn
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from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
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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 json
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@ -102,34 +102,30 @@ def prepare_dataset(directory, catalog_path, source_mapper):
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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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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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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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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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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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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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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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except Exception as e:
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print(f"Błąd podczas przetwarzania pliku: {str(e)}")
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@ -151,83 +147,37 @@ class CustomModel(nn.Module):
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self.base_model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
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self.source_embedding = nn.Embedding(10000, config.hidden_size, padding_idx=-1)
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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 = 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(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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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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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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def compute_loss(self, model, inputs, return_outputs=False):
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source_idx = inputs.pop("source_idx", None)
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outputs = model(**inputs, labels=labels, source_idx=source_idx)
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outputs = model(**inputs, source_idx=source_idx)
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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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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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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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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(self.model.base_model.device)
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with torch.no_grad():
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outputs = self.model.generate(
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**inputs,
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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=3
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)
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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()
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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 {"answer": answer, "sources": list(sources)}
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def main():
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source_mapper = SourceMapper()
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@ -245,49 +195,57 @@ def main():
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dataset = Dataset.from_list(data)
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def tokenize(examples):
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return tokenizer(
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def tokenize_function(examples):
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tokenized = 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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tokenized_dataset = dataset.map(tokenize, batched=True, batch_size=16)
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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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training_args = TrainingArguments(
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output_dir="./results",
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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=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=50,
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save_strategy="epoch",
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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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tokenized_dataset = dataset.map(tokenize_function, batched=False)
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data_collator = DataCollatorForLanguageModeling(
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tokenizer=tokenizer,
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mlm=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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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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training_args = TrainingArguments(
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output_dir="./results",
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num_train_epochs=3,
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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=10,
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save_strategy="steps",
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save_steps=1000,
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report_to="none",
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remove_unused_columns=False
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)
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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=data_collator,
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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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main()
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