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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()
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