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---
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
---
# 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 is 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:** 39,000 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 4-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.
## 📊 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.
## 📁 Dataset Structure
```
.HVU_QA
├── t5-viet-qg-finetuned/
├── fine_tune_qg.py
├── generate_question.py
├── 39k_train.json
└── README.md
```
## 📁 Vietnamese Question Generation Tool
## 🛠️ Requirements
* Python 3.8+
* PyTorch >= 1.9
* Transformers >= 4.30
* scikit-learn
### 📦 Install Required Libraries
```bash
pip install datasets transformers sentencepiece safetensors accelerate evaluate sacrebleu rouge-score nltk scikit-learn
```
*(Install PyTorch separately from [pytorch.org](https://pytorch.org) if not installed yet.)*
### 📥 Load Dataset from Hugging Face Hub
```python
from datasets import load_dataset
ds = load_dataset("DANGDOCAO/GeneratingQuestions", split="train")
print(ds[0])
```
## 📚 Usage
* Train and evaluate a question generation model.
* Develop Vietnamese NLP tools.
* Conduct linguistic research.
### 🔹 Fine-tuning
```bash
python fine_tune_qg.py
```
This will:
1. Load the dataset from `39k_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`:
- `top_k`, `top_p`, `temperature`, `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 |