README.md

---
license: mit
task_categories:
- question-answering
- text-generation
- table-question-answering
- sentence-similarity
- feature-extraction
language:
- vi
tags:
- question-generation
- nlp
- faq
- low-resource
- code
pretty_name: HVU_QA
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: 40k_train.json
---
# HVU_QA

**HVU_QA** is an open-source Vietnamese Question-Context-Answer (QCA) corpus, accompanied by supporting tools, created to facilitate the development of FAQ-style question generation and question answering systems, particularly for low-resource language settings. The dataset was developed by a research team at Hung Vuong University, Phu Tho, Vietnam, led by Dr. Ha Nguyen, Deputy Head of the Department of Engineering Technology. HVU_QA was constructed using a fully automated data-building pipeline that combines web crawling from reliable sources, semantic tag-based extraction, and AI-assisted filtering, helping ensure high factual accuracy, consistent structure, and practical usability for real-world applications.

## 📋 Dataset Description

- **Language:** Vietnamese
- **Format:** SQuAD-style JSON
- **Total samples:** 40,000 QCA triples (full corpus released)
- **Domains covered:** Social services, labor law, administrative processes, and other public service topics.
- **Structure of each sample:**
  - **Question:** Generated or extracted question
  - **Context:** Supporting text passage from which the answer is derived
  - **Answer:** Answer span within the context

## ⚙️ Creation Pipeline

The dataset was built using a 5-stage automated process:
1. **Selecting relevant QA websites** from trusted sources.
2. **Automated data crawling** to collect raw QA webpages.
3. **Extraction via semantic tags** to obtain clean Question-Context-Answer triples.
4. **AI-assisted filtering** to remove noisy or factually inconsistent samples.
5. **Final canonicalization and deduplication** to eliminate redundancy and maintain corpus diversity.

## 📊 Quality Evaluation
A fine-tuned `vit5-base` model trained on HVU_QA achieved:

| Metric                 | Score           |
|-------------------------|----------------|
| BLEU                    | 89.1           |
| Semantic similarity     | 91.5% (cos ≥ 0.8) |
| Human grammar score     | 4.58 / 5         |
| Human usefulness score  | 4.29 / 5         |

These results confirm that HVU_QA is a high-quality resource for developing robust FAQ-style question generation models.

## 📁 Project Structure
```text
HVU_QA/
├── backend/
│   ├── __init__.py
│   └── app.py
├── frontend/
│   ├── HVU.png
│   ├── index.html
│   ├── app.js
│   └── style.css
├── t5-viet-qg-finetuned/
├── fine_tune_qg.py
├── generate_question.py
├── HVU_QA_tool.py
├── main.py
├── 40k_train.json
├── requirements.txt
└── README.md
```
## 📁 Vietnamese Question Generation Toolkit

This repository includes four main entry points:

* `main.py`: starts the local Flask web application with the current `backend/` and `frontend/`.
* `fine_tune_qg.py`: fine-tunes the Vietnamese T5-based question generation model using `40k_train.json`.
* `generate_question.py`: command-line script for generating questions from a Vietnamese passage.
* `HVU_QA_tool.py`: one-file launcher that can prepare a standalone runtime, install missing dependencies, download the model from Hugging Face, and launch the web app automatically.

## 🛠️ Requirements

* Python 3.10+
* `pip`
* Optional: NVIDIA GPU with CUDA for faster inference

### 📦 Install Required Libraries

For running the full repository locally:

```bash
python -m venv venv

# Windows
venv\Scripts\activate

# macOS / Linux
source venv/bin/activate

python -m pip install --upgrade pip
python -m pip install -r requirements.txt
```

If you want to use NVIDIA GPU, install the PyTorch build that matches your CUDA setup from [pytorch.org](https://pytorch.org) before installing the remaining requirements.

`HVU_QA_tool.py` can also create its own virtual environment, prepare a standalone runtime, and sync the model automatically, so the manual installation steps above are mainly for direct repo usage.

### 📥 Load Dataset from Hugging Face Hub
```python
from datasets import load_dataset

ds = load_dataset("DANGDOCAO/GeneratingQuestions", split="train")
print(ds[0])
```
## 📚 Usage

* Run the local web interface for question generation.
* Generate questions from the command line.
* Fine-tune or evaluate the Vietnamese question generation model.

### 🔹 Run the web app

```bash
python main.py
```

Open `http://127.0.0.1:5000` in your browser.

### 🔹 Launch with `HVU_QA_tool.py`

```bash
python HVU_QA_tool.py
```

This launcher is useful when you want the project to set itself up automatically. It can detect the local repo, prepare a standalone runtime when needed, install missing dependencies, download the model, and then start the web application.

### 🔹 Fine-tuning

```bash
python fine_tune_qg.py
```

This will:

1. Load the dataset from `40k_train.json`.  
2. Fine-tune `VietAI/vit5-base`.  
3. Save the trained model into `t5-viet-qg-finetuned/`.  

*(Or download the pre-trained model: [t5-viet-qg-finetuned](https://huggingface.co/datasets/DANGDOCAO/GeneratingQuestions/tree/main).)*

### 🔹 Generating Questions
```bash
python generate_question.py
```

**Example:**
```
Input passage:
Cà phê sữa đá là một loại đồ uống nổi tiếng ở Việt Nam
(Iced milk coffee is a famous drink in Vietnam)

Number of questions: 5
```
**Output:**
```
1. Loại cà phê nào nổi tiếng ở Việt Nam?  
   (What type of coffee is famous in Vietnam?)  
2. Tại sao cà phê sữa đá lại phổ biến?  
   (Why is iced milk coffee popular?)  
3. Cà phê sữa đá bao gồm những nguyên liệu gì?  
   (What ingredients are included in iced milk coffee?)  
4. Cà phê sữa đá có nguồn gốc từ đâu?  
   (Where does iced milk coffee originate from?)  
5. Cà phê sữa đá Việt Nam được pha chế như thế nào?  
   (How is Vietnamese iced milk coffee prepared?)  
```
**You can adjust** in `generate_question.py`:

* `--model_dir`, `--num_questions`, `--max_source_length`, `--max_new_tokens`, `--device`, `GENERATION_PASSES`, `no_repeat_ngram_size`, `repetition_penalty`

## 📌 Citation
If you use **HVU_QA** in your research, please cite:

```bibtex
@inproceedings{nguyen2025method,
  author    = {Ha Nguyen and Phuc Le and Dang Do and Cuong Nguyen and Chung Mai},
  title     = {A Method for Building QA Corpora for Low-Resource Languages},
  booktitle = {Proceedings of the 2025 International Symposium on Information and Communication Technology (SOICT 2025)},
  year      = {2025},
  publisher = {Springer},
  series    = {Communications in Computer and Information Science (CCIS)},
  address   = {Nha Trang, Vietnam},
  note      = {To appear}
}
```
## ❤️ Support / Funding

If you find **HVU_QA** useful, please consider supporting our work.  
Your contributions help us maintain the dataset, improve quality, and release new versions (cleaning, expansion, benchmarks, and tools).

### 🇻🇳 Donate via VietQR (scan to support)
This **VietQR / NAPAS 247** code can be scanned by Vietnamese banking apps and some international payment apps that support QR bank transfers.

<img src="QRtk.jpg" alt="VietQR Support" width="320"/>

- 🏦 **Bank:** VietinBank (Vietnam)  
- 👤 **Account name:** NGUYEN TIEN HA  
- 💳 **Account number:** 103004492490  
- 📍 **Branch:** VietinBank CN PHU THO - HOI SO

### 🌍 International Support (Quick card payment)
If you are outside Vietnam, you can support this project via **Buy Me a Coffee**  
(no PayPal account needed - pay directly with a credit/debit card):
- BuyMeACoffee: https://buymeacoffee.com/hanguyen0408

### 🌍 International Support (PayPal)
If you prefer PayPal, you can also support us here:
- PayPal.me: https://paypal.me/HaNguyen0408

### ✨ Other ways to support
- ⭐ Star this repository / dataset on Hugging Face  
- 📌 Cite our paper if you use it in your research  
- 🐛 Open issues / pull requests to improve the dataset and tools

## 📬 Contact / Maintainers
For questions, feedback, collaborations, or issue reports related to HVU_QA, please contact:
Dr. Ha Nguyen (Project Lead)
Hung Vuong University, Phu Tho, Vietnam
Email: nguyentienha@hvu.edu.vn