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.gitattributes

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 Data_totalQCAtriples30K/30ktrain.json filter=lfs diff=lfs merge=lfs -text
 Datatest1k/testorgin1k.json filter=lfs diff=lfs merge=lfs -text
 Datatrain29k/29kcorpustag.json filter=lfs diff=lfs merge=lfs -text
+HVU_QA/30ktrain.json filter=lfs diff=lfs merge=lfs -text

HVU_QA/30ktrain.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1989e727e0bb58732b6cd569c87fe2e474816d69064d0769f34cd307544d2fa
+size 11786416

HVU_QA/README.md

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+# HVU_QA
+
+**HVU_QA** is a project dedicated to sharing datasets and tools for **Question Generation Processing (NLP)**, developed and maintained by the research team at **Hung Vuong University (HVU), Phu Tho, Vietnam**.  
+This project is supported by **Hung Vuong University, Phu Tho, Vietnam**, with the aim of advancing research and applications in low-resource language processing, particularly for the Vietnamese language.
+
+---
+
+## 📚 Overview
+
+This repository enables you to:
+
+1. Fine-tune the [VietAI/vit5-base](https://huggingface.co/datasets/DANGDOCAO/GeneratingQuestions) model on your own GQ dataset.
+2. Generate multiple, diverse questions given a user-provided text passage (context).
+
+---
+
+## 📁 Datasets
+
+* Built following the **SQuAD v2.0 standard**, ensuring compatibility with NLP pipelines.
+* Includes tens of thousands of high-quality **Question–Context–Answer triples (QCA)**.
+* Suitable for both **training** and **evaluation**.
+
+### Data folders
+
+* `DataTotalQCAtriples30k/` → **30,000 training samples** (`30ktrain.json`)
+* `Datatest1k/` → **1,000 samples** for manual & automatic evaluation (`testorgin1k.json`)
+* `Datatrain29k/` → **29,000 preprocessed samples** (`29kcorpustag.json`)
+
+> All data files are UTF-8 encoded and ready for use in NLP pipelines.
+
+---
+
+## 📁 Vietnamese Question Generation Tool
+
+A **command-line tool** for:
+
+* **Fine-tuning** a question generation model.
+* **Automatically generating questions** from Vietnamese text.
+
+Built on **Hugging Face Transformers (VietAI/vit5-base)** and **PyTorch**.
+
+---
+
+## ✨ Features
+
+* Fine-tune a question generation model with SQuAD v2.0 format data.
+* Generate diverse and creative questions from text passages.
+* Flexible generation parameters (`top-k`, `top-p`, `temperature`, etc.).
+* Simple command-line usage.
+* GPU support if available.
+
+---
+
+## 📊 Evaluation Results
+
+We conducted both **manual evaluation** (500 samples) and **automatic evaluation** (1,000 samples).
+
+| Evaluation Type  | Precision | Recall | F1-Score |
+|------------------|-----------|--------|----------|
+| Automatic (1000) | 0.85      | 0.83   | 0.84     |
+| Manual (500)     | 0.88      | 0.86   | 0.87     |
+
+➡️ The model generates diverse, grammatically correct, and contextually appropriate questions.
+
+---
+
+## 🧩 Creation Process
+
+The dataset was built using a **4-stage automated pipeline**:
+
+1. Select relevant QA websites from trusted sources.
+2. Automatic crawling to collect raw QA pages.
+3. Semantic tag extraction to obtain clean Question–Context–Answer triples.
+4. AI-assisted filtering to remove noisy or inconsistent samples.
+
+---
+
+## 📝 Quality Evaluation
+
+A fine-tuned model trained on **HVU_QA (VietAI/vit5-base)** achieved:
+
+* **BLEU Score**: 90.61
+* **Semantic similarity**: 97.0% (cosine ≥ 0.8)
+* **Human evaluation**:
+  * Grammar: **4.58 / 5**
+  * Usefulness: **4.29 / 5**
+
+➡️ These results confirm that **HVU_QA is a high-quality resource** for developing robust FAQ-style question generation models.
+
+---
+
+## 📂 Project Structure
+
+```
+.
+├── t5-viet-qg-finetuned/
+├── fine_tune_qg.py
+├── generate_question.py
+├── 30ktrain.json
+└── README.md
+```
+
+---
+
+## 🛠️ Requirements
+
+* Python 3.8+
+* PyTorch >= 1.9
+* Transformers >= 4.30
+* scikit-learn
+* Fine-tuned model (download at: [link](https://huggingface.co/datasets/DANGDOCAO/GeneratingQuestions/tree/main))
+
+---
+
+## 🚀 Setup
+
+### Step 1: Download and Extract
+
+1. Download `GenerationQuestions.zip`
+2. Extract into a folder, e.g.:
+
+   ```
+   D:\your\HVU_QA
+   ```
+
+### Step 2: Add to Environment Path (if needed)
+
+1. Open **System Properties → Environment Variables**
+2. Select `Path` → **Edit** → **New**
+3. Add the path, e.g.:
+
+   ```
+   D:\your\HVU_QA
+   ```
+
+### Step 3: Open in Visual Studio Code
+
+```
+File > Open Folder > D:\HVU_QA
+```
+
+### Step 4: Install Required Libraries
+
+Open **Terminal** and run:
+
+#### 📦 Windows (PowerShell)
+
+**Required only**
+
+```powershell
+python -m pip install --upgrade pip
+pip install torch transformers datasets scikit-learn sentencepiece safetensors
+```
+
+**Required + Optional**
+
+```powershell
+python -m pip install --upgrade pip
+pip install torch transformers datasets scikit-learn sentencepiece safetensors accelerate tensorboard evaluate sacrebleu rouge-score nltk
+```
+
+#### 📦 Linux / macOS (bash/zsh)
+
+**Required only**
+
+```bash
+python3 -m pip install --upgrade pip
+pip install torch transformers datasets scikit-learn sentencepiece safetensors
+```
+
+**Required + Optional**
+
+```bash
+python3 -m pip install --upgrade pip
+pip install torch transformers datasets scikit-learn sentencepiece safetensors accelerate tensorboard evaluate sacrebleu rouge-score nltk
+```
+
+✅ Verify installation:
+
+* Windows (PowerShell)
+
+```powershell
+python -c "import torch, transformers, datasets, sklearn, sentencepiece, safetensors, accelerate, tensorboard, evaluate, sacrebleu, rouge_score, nltk; print('✅ All dependencies installed correctly!')"
+```
+
+* Linux/macOS
+
+```bash
+python3 -c "import torch, transformers, datasets, sklearn, sentencepiece, safetensors, accelerate, tensorboard, evaluate, sacrebleu, rouge_score, nltk; print('✅ All dependencies installed correctly!')"
+```
+
+---
+
+## 📚 Usage
+
+* Train and evaluate a question generation model.
+* Develop Vietnamese NLP tools.
+* Conduct linguistic research.
+
+### 🔹 Training (Fine-tuning)
+
+When you run `fine_tune_qg.py`, the script will:
+
+1. Load the dataset from **`30ktrain.json`**
+2. Fine-tune the `VietAI/vit5-base` model
+3. Save the trained model into a new folder named **`t5-viet-qg-finetuned/`**
+
+Run:
+
+```bash
+python fine_tune_qg.py
+```
+
+### 🔹 Generating Questions
+
+```bash
+python generate_question.py
+```
+
+**Example:**
+
+```
+Input passage:
+Cà phê sữa đá là đồ uống nổi tiếng ở Việt Nam.
+
+Number of questions: 5
+```
+
+✅ Output:
+
+1. Loại cà phê nào nổi tiếng ở Việt Nam?
+2. Tại sao cà phê sữa đá được yêu thích?
+3. Cà phê sữa đá gồm những nguyên liệu gì?
+4. Nguồn gốc của cà phê sữa đá là từ đâu?
+5. Cà phê sữa đá Việt Nam được pha chế như thế nào?
+
+---
+
+## ⚙️ Generation Settings
+
+In `generate_question.py`, you can adjust:
+
+* `top_k`, `top_p`, `temperature`, `no_repeat_ngram_size`, `repetition_penalty`
+
+---
+
+## 🤝 Contribution
+
+We welcome contributions:
+
+* Open issues
+* Submit pull requests
+* Suggest improvements or add datasets
+
+---
+
+## 📄 Citation
+
+If you use this repository or datasets in research, please cite:
+
+**Ha Nguyen-Tien, Phuc Le-Hong, Dang Do-Cao, Cuong Nguyen-Hung, Chung Mai-Van. 2025. A Method to Build QA Corpora for Low-Resource Languages. Proceedings of KSE 2025. ACM TALLIP.**
+
+```bibtex
+@inproceedings{nguyen2025hvuqa,
+  title={A Method to Build QA Corpora for Low-Resource Languages},
+  author={Ha Nguyen-Tien and Phuc Le-Hong and Dang Do-Cao and Cuong Nguyen-Hung and Chung Mai-Van},
+  booktitle={Proceedings of KSE 2025},
+  year={2025}
+}
+```
+
+---
+
+## 📬 Contact
+
+* **Ha Nguyen-Tien** (Corresponding author)  
+  📧 [nguyentienha@hvu.edu.vn](mailto:nguyentienha@hvu.edu.vn)
+
+* **Phuc Le-Hong**  
+  📧 [Lehongphuc20021408@gmail.com](mailto:Lehongphuc20021408@gmail.com)
+
+* **Dang Do-Cao**  
+  📧 [docaodang532001@gmail.com](mailto:docaodang532001@gmail.com)
+
+📍 Faculty of Engineering and Technology, Hung Vuong University, Phu Tho, Vietnam  
+🌐 [https://hvu.edu.vn](https://hvu.edu.vn)
+
+---
+
+*This repository is part of our ongoing effort to support Vietnamese NLP and make language technology more accessible for low-resource and underrepresented languages.*

HVU_QA/fine_tune_qg.py

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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"]:
+        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()

HVU_QA/generate_question.py

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+# generate_question.py
+import json
+from difflib import SequenceMatcher
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers.utils import logging as hf_logging
+
+# ===== Giảm ồn cảnh báo của transformers =====
+hf_logging.set_verbosity_error()
+
+# ===== Cấu hình đường dẫn =====
+MODEL_DIR = "t5-viet-qg-finetuned"  # thư mục model đã fine-tune
+DATA_PATH = "30ktrain.json"        # file JSON dữ liệu gốc
+
+# ===== Load model & tokenizer 1 lần =====
+tokenizer = T5Tokenizer.from_pretrained(MODEL_DIR)
+model = T5ForConditionalGeneration.from_pretrained(MODEL_DIR)
+
+def find_best_match_from_context(user_context, squad_data):
+    """
+    Tìm bản ghi gần nhất dựa trên article.title (giữ đúng logic code gốc).
+    Trả về tuple (context_title, answer_text, question_text) hoặc None.
+    """
+    best_score, best_entry = 0.0, None
+    ui = user_context.lower()
+
+    for article in squad_data.get("data", []):
+        context_title = article.get("title", "")
+        score_title = SequenceMatcher(None, ui, context_title.lower()).ratio()
+
+        for paragraph in article.get("paragraphs", []):
+            for qa in paragraph.get("qas", []):
+                answers = qa.get("answers", [])
+                if not answers:
+                    continue
+                answer_text = answers[0].get("text", "").strip()
+                question_text = qa.get("question", "").strip()
+
+                score = score_title
+                if score > best_score:
+                    best_score = score
+                    best_entry = (context_title, answer_text, question_text)
+
+    return best_entry
+
+def _near_duplicate(q, seen, thr=0.90):
+    """Loại câu gần trùng dựa trên tỉ lệ giống nhau."""
+    for s in seen:
+        if SequenceMatcher(None, q, s).ratio() >= thr:
+            return True
+    return False
+
+def generate_questions(user_context,
+                       total_questions=20,
+                       batch_size=10,
+                       top_k=60,
+                       top_p=0.95,
+                       temperature=0.9,
+                       max_input_len=512,
+                       max_new_tokens=64):
+    # Load dữ liệu JSON
+    with open(DATA_PATH, "r", encoding="utf-8") as f:
+        squad_data = json.load(f)
+
+    # Tìm bản ghi gần giống nhất (KHÔNG in ra màn hình)
+    best_entry = find_best_match_from_context(user_context, squad_data)
+    if best_entry is None:
+        print("❌ Không tìm thấy dữ liệu phù hợp trong file JSON.")
+        return
+
+    _, answer, _ = best_entry  # chỉ cần answer gốc để bảo toàn tính toàn vẹn
+
+    # Chuẩn bị input cho mô hình (đặt max_length để tránh cảnh báo truncate)
+    input_text = f"answer: {answer} context: {user_context}"
+    inputs = tokenizer(
+        input_text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=max_input_len
+    )
+
+    # Sinh nhiều câu hỏi theo lô để tiết kiệm RAM
+    unique_questions = []
+    remaining = total_questions
+
+    while remaining > 0:
+        n = min(batch_size, remaining)
+        outputs = model.generate(
+            **inputs,
+            do_sample=True,
+            top_k=top_k,
+            top_p=top_p,
+            temperature=temperature,
+            max_new_tokens=max_new_tokens,   # chiều dài phần sinh
+            num_return_sequences=n,
+            no_repeat_ngram_size=3,
+            repetition_penalty=1.12
+        )
+
+        for out in outputs:
+            q = tokenizer.decode(out, skip_special_tokens=True).strip()
+            if len(q) < 5:  # lọc câu quá ngắn
+                continue
+            if not _near_duplicate(q, unique_questions, thr=0.90):
+                unique_questions.append(q)
+
+        remaining = total_questions - len(unique_questions)
+        if remaining <= 0:
+            break
+
+    # Cắt đúng số lượng yêu cầu
+    unique_questions = unique_questions[:total_questions]
+
+    print("✅ Các câu hỏi mới được sinh ra:")
+    for i, q in enumerate(unique_questions, 1):
+        print(f"{i}. {q}")
+
+if __name__ == "__main__":
+    # Nhập đoạn văn
+    user_context = input("\nNhập đoạn văn bản:\n ").strip()
+
+    # Nhập số lượng câu hỏi cần sinh (tùy biến)
+    raw_n = input("\nNhập vào số lượng câu hỏi bạn cần:").strip()
+    if raw_n == "":
+        total_questions = 20
+    else:
+        try:
+            total_questions = int(raw_n)
+        except ValueError:
+            print("⚠️ Giá trị không hợp lệ. Dùng mặc định 20.")
+            total_questions = 20
+
+    # Ràng buộc an toàn
+    if total_questions < 1:
+        total_questions = 1
+    if total_questions > 200:
+        print("⚠️ Giới hạn tối đa 200 câu. Sẽ sinh 200 câu.")
+        total_questions = 200
+
+    # batch_size tự động theo quy mô
+    batch_size = 10 if total_questions >= 30 else min(10, total_questions)
+
+    # Thông báo phân tích
+    print("\n🔍 Đang phân tích dữ liệu...\n")
+
+    generate_questions(
+        user_context=user_context,
+        total_questions=total_questions,
+        batch_size=batch_size,
+        top_k=60,
+        top_p=0.95,
+        temperature=0.9,
+        max_input_len=512,
+        max_new_tokens=64
+    )