HVU_QA/fine_tune_qg.py

import json
from datasets import Dataset
from sklearn.model_selection import train_test_split
from transformers import (
    T5Tokenizer,
    T5ForConditionalGeneration,
    TrainingArguments,
    Trainer
)

def load_squad_data(file_path):
    
    with open(file_path, "r", encoding="utf-8") as f:
        squad_data = json.load(f)

    data = []
    for article in squad_data["data"]:
        for paragraph in article["paragraphs"]:
            context = paragraph.get("context", "")
            for qa in paragraph["qas"]:
                if not qa.get("is_impossible", False) and qa.get("answers"):
                    answer = qa["answers"][0]["text"]
                    question = qa["question"]
                    input_text = f"answer: {answer} context: {context}"
                    data.append({"input": input_text, "target": question})
    return data

def preprocess_function(example, tokenizer, max_input_length=512, max_target_length=64):
    
    model_inputs = tokenizer(
        example["input"],
        max_length=max_input_length,
        padding="max_length",
        truncation=True,
    )
    labels = tokenizer(
        text_target=example["target"],
        max_length=max_target_length,
        padding="max_length",
        truncation=True,
    )
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

def main():
    data_path = "30ktrain.json"  
    output_dir = "t5-viet-qg-finetuned"
    logs_dir = "logs"
    model_name = "VietAI/vit5-base"

    print("Tải mô hình và tokenizer...")
    tokenizer = T5Tokenizer.from_pretrained(model_name)
    model = T5ForConditionalGeneration.from_pretrained(model_name)

    print("Đọc và chia dữ liệu...")
    raw_data = load_squad_data(data_path)  
    train_data, val_data = train_test_split(raw_data, test_size=0.2, random_state=42)

    train_dataset = Dataset.from_list(train_data)
    val_dataset = Dataset.from_list(val_data)

    tokenized_train = train_dataset.map(
        lambda x: preprocess_function(x, tokenizer),
        batched=True,
        remove_columns=["input", "target"]
    )
    tokenized_val = val_dataset.map(
        lambda x: preprocess_function(x, tokenizer),
        batched=True,
        remove_columns=["input", "target"]
    )

    print("Cấu hình huấn luyện...")
    training_args = TrainingArguments(
        output_dir=output_dir,
        overwrite_output_dir=True,
        per_device_train_batch_size=1,
        gradient_accumulation_steps=1,
        num_train_epochs=3,
        learning_rate=2e-4,
        weight_decay=0.01,
        warmup_steps=0,
        logging_dir=logs_dir,
        logging_steps=10,
        fp16=False
    )

    print("Huấn luyện mô hình...")
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_train,
        eval_dataset=tokenized_val,
        tokenizer=tokenizer,
    )
    trainer.train()

    print("Lưu mô hình...")
    model.save_pretrained(output_dir)
    tokenizer.save_pretrained(output_dir)
    print("Huấn luyện hoàn tất!")

if __name__ == "__main__":
    main()