| 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"]:
|
| context = article.get("title", "")
|
| for paragraph in article["paragraphs"]:
|
| 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()
|
|
|
