Delete HVU_QA/fine_tune_qg.py

HVU_QA/fine_tune_qg.py

@@ -1,125 +0,0 @@
-import os, json
-from datasets import Dataset
-from sklearn.model_selection import train_test_split
-from transformers import T5Tokenizer, T5ForConditionalGeneration, TrainingArguments, Trainer, DataCollatorForSeq2Seq
-
-
-def load_squad(path: str):
-    with open(path, "r", encoding="utf-8") as f:
-        d = json.load(f)
-
-    data = []
-    for a in d.get("data", []):
-        for p in a.get("paragraphs", []):
-            ctx = p.get("context", "")
-            for qa in p.get("qas", []):
-                if qa.get("is_impossible") or not qa.get("answers"):
-                    continue
-                ans = qa["answers"][0].get("text", "")
-                q = qa.get("question", "")
-                if ans and q and ctx:
-                    data.append({"input": f"answer: {ans} context: {ctx}", "target": q})
-    return data
-
-
-def tokenize(batch, tok, max_in=512, max_out=64):
-    x = tok(batch["input"], max_length=max_in, truncation=True)
-    y = tok(text_target=batch["target"], max_length=max_out, truncation=True)
-    x["labels"] = y["input_ids"]
-    return x
-
-
-def latest_ckpt(out_dir: str):
-    if not os.path.isdir(out_dir):
-        return None
-
-    best_step, best_path = -1, None
-    for name in os.listdir(out_dir):
-        if not name.startswith("checkpoint-"):
-            continue
-        try:
-            step = int(name.split("-")[-1])
-        except ValueError:
-            continue
-        if step > best_step:
-            best_step, best_path = step, os.path.join(out_dir, name)
-
-    return best_path
-
-
-def main():
-    data_path = "39k_train.json"
-    out_dir = "t5-viet-qg-finetuned"
-    logs_dir = "logs"
-    model_name = "VietAI/vit5-base"
-
-    print("Tải mô hình và tokenizer...")
-    tok = T5Tokenizer.from_pretrained(model_name)
-    model = T5ForConditionalGeneration.from_pretrained(model_name)
-
-    print("Đọc và chia dữ liệu...")
-    data = load_squad(data_path)
-    tr, va = train_test_split(data, test_size=0.2, random_state=42)
-
-    print("Tokenize dữ liệu...")
-    tr_ds = Dataset.from_list(tr).map(
-        lambda b: tokenize(b, tok),
-        batched=True,
-        remove_columns=["input", "target"],
-    )
-    va_ds = Dataset.from_list(va).map(
-        lambda b: tokenize(b, tok),
-        batched=True,
-        remove_columns=["input", "target"],
-    )
-
-    print("Cấu hình huấn luyện (checkpoint + resume)...")
-    args = TrainingArguments(
-        output_dir=out_dir,
-        overwrite_output_dir=False,
-        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,
-        save_strategy="steps",
-        save_steps=500,
-        save_total_limit=100,
-        eval_strategy="steps",
-        eval_steps=500,
-        load_best_model_at_end=True,
-        metric_for_best_model="eval_loss",
-        greater_is_better=False,
-        logging_dir=logs_dir,
-        logging_steps=10,
-        fp16=True,
-        report_to="none",
-    )
-
-    trainer = Trainer(
-        model=model,
-        args=args,
-        train_dataset=tr_ds,
-        eval_dataset=va_ds,
-        tokenizer=tok,
-        data_collator=DataCollatorForSeq2Seq(tokenizer=tok, model=model),
-    )
-
-    ckpt = latest_ckpt(out_dir)
-    if ckpt:
-        print(f"Phát hiện checkpoint: {ckpt} → Resume training")
-        trainer.train(resume_from_checkpoint=ckpt)
-    else:
-        print("Không có checkpoint → Train từ đầu")
-        trainer.train()
-
-    print("Lưu mô hình cuối cùng...")
-    trainer.save_model(out_dir)
-    tok.save_pretrained(out_dir)
-
-    print("Huấn luyện hoàn tất!")
-
-
-if __name__ == "__main__":
-    main()