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+---
+pretty_name: HVU_QA
+language:
+- vi
+tags:
+- question-generation
+- nlp
+- faq
+- low-resource
+license: cc-by-4.0
+task_categories:
+- question-generation
+task_ids:
+- text2text-generation
+size_categories:
+- 10K<n<100K
+dataset_info:
+  features:
+  - name: question
+    dtype: string
+  - name: context
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 30000
+---
+
+# HVU_QA
+
+**HVU_QA** is an open-source Vietnamese Question–Context–Answer (QCA) corpus for building FAQ-style question generation systems in low-resource languages.  
+It was created using a fully automated pipeline combining web crawling, semantic tag-based extraction, and AI-assisted filtering to ensure high factual accuracy.
+
+---
+
+## 📋 Dataset Description
+
+- **Language:** Vietnamese  
+- **Format:** SQuAD-style JSON  
+- **Size:** 30,000 QCA triples  
+- **Domains:** Social services, labor law, administrative processes, and public service topics.
+
+Each data sample contains:
+- `question`: The generated or extracted question
+- `context`: The supporting passage
+- `answer`: The answer span within the context
+
+---
+
+## ⚙️ Dataset Creation
+
+**Pipeline:**
+1. Selecting relevant QA websites from trusted sources  
+2. Automated crawling to collect raw QA webpages  
+3. Semantic tag-based extraction to get clean QCA triples  
+4. AI-assisted filtering to remove noisy or inconsistent samples
+
+**Annotation & Licensing:**  
+All data are collected from public-domain Vietnamese government and service portals, released under CC BY 4.0.
+
+---
+
+## 📊 Quality Evaluation
+
+A fine-tuned `VietAI/vit5-base` model trained on HVU_QA achieved:
+
+| Metric               | Score       |
+|-----------------------|-------------|
+| BLEU                  | 90.61        |
+| Semantic similarity   | 97.0% (cos ≥ 0.8) |
+| Human grammar         | 4.58 / 5     |
+| Human usefulness      | 4.29 / 5     |
+
+---
+
+## 📁 Data Fields
+
+```json
+{
+  "question": "string",
+  "context": "string",
+  "answer": "string"
+}
+```
+
+- `question`: The question text  
+- `context`: The paragraph containing the answer  
+- `answer`: The answer span
+
+---
+
+## 🧩 How to Use
+
+### Load from Hub
+
+```python
+from datasets import load_dataset
+ds = load_dataset("DANGDOCAO/GeneratingQuestions", split="train")
+print(ds[0])
+```
+
+### Install Dependencies
+
+```bash
+pip install datasets transformers sentencepiece safetensors accelerate evaluate sacrebleu rouge-score nltk scikit-learn
+```
+
+(Optional) Install PyTorch separately from [pytorch.org](https://pytorch.org)
+
+---
+
+## 🚀 Example Usage
+
+### Fine-tune a Question Generation Model
+
+```bash
+python fine_tune_qg.py
+```
+
+This will:
+1. Load data from `30ktrain.json`
+2. Fine-tune `VietAI/vit5-base`
+3. Save model to `t5-viet-qg-finetuned/`
+
+### Generate Questions
+
+```bash
+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**
+1. What type of coffee is famous in Vietnam?  
+2. Why is iced milk coffee popular?  
+3. What ingredients are included in iced milk coffee?  
+4. Where does iced milk coffee originate from?  
+5. 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:
+
+```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 the International Conference on Knowledge and Systems Engineering (KSE)},
+  year={2025}
+}
+```