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:

Fine-tune the VietAI/vit5-base model on your own GQ dataset.
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.

📁 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:

Select relevant QA websites from trusted sources.
Automatic crawling to collect raw QA pages.
Semantic tag extraction to obtain clean Question–Context–Answer triples.
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

.HVU_QA
├── t5-viet-qg-finetuned/
├── fine_tune_qg.py
├── generate_question.py
├── 30ktrain.json
└── README.md

All data files are UTF-8 encoded and ready for use in NLP pipelines.

🛠️ Requirements

Python 3.8+
PyTorch >= 1.9
Transformers >= 4.30
scikit-learn
Fine-tuned model (download at: link)

⚙️ Setup

🛠️ Step 1: Download and Extract

Download HVU_QA.zip
Extract into a folder, e.g.:
```
D:\your\HVU_QA
```

🛠️ Step 2: Add to Environment Path (if needed)

Open System Properties → Environment Variables
Select Path → Edit → New
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

python -m pip install --upgrade pip
pip install torch transformers datasets scikit-learn sentencepiece safetensors

Required + Optional

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

python3 -m pip install --upgrade pip
pip install torch transformers datasets scikit-learn sentencepiece safetensors

Required + Optional

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)

python -c "import torch, transformers, datasets, sklearn, sentencepiece, safetensors, accelerate, tensorboard, evaluate, sacrebleu, rouge_score, nltk; print('✅ All dependencies installed correctly!')"

Linux/macOS

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:

Load the dataset from 30ktrain.json
Fine-tune the VietAI/vit5-base model
Save the trained model into a new folder named t5-viet-qg-finetuned/

Run:

python fine_tune_qg.py

Generating Questions

python generate_question.py

Example:

Input passage:
Iced milk coffee (Cà phê sữa đá) is a famous drink in Vietnam.

Number of questions: 5

✅ Output:

What type of coffee is famous in Vietnam?
Why is iced milk coffee popular?
What ingredients are included in iced milk coffee?
Where does iced milk coffee originate from?
How is Vietnamese iced milk coffee prepared?

⚙️ 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
Phuc Le-Hong
📧 Lehongphuc20021408@gmail.com
Dang Do-Cao
📧 docaodang532001@gmail.com

📍 Faculty of Engineering and Technology, Hung Vuong University, Phu Tho, Vietnam
🌐 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.