Delete fine_tune_qg.py

fine_tune_qg.py

@@ -1,102 +0,0 @@
-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,  # vẫn truyền để bạn có thể eval thủ công sau
-        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()