update README.md

README.md

@@ -0,0 +1,124 @@
+# HVU_QA
+
+**HVU_QA** is an open-source Vietnamese Question–Context–Answer (QCA) corpus and supporting tools for building FAQ-style question generation systems in low-resource languages.  
+The dataset was created using a fully automated pipeline that combines web crawling from trustworthy sources, semantic tag-based extraction, and AI-assisted filtering to ensure high factual accuracy.
+
+---
+
+## 📋 Dataset Description
+
+- **Language:** Vietnamese  
+- **Format:** SQuAD-style JSON  
+- **Total samples:** 30,000 QCA triples  
+- **Domains covered:** Social services, labor law, administrative processes, and other public service topics.  
+
+Each data sample contains:
+- `question`: Generated or extracted question
+- `context`: Supporting text passage from which the answer is derived
+- `answer`: Answer span within the context
+
+---
+
+## ⚙️ Dataset Creation
+
+**Pipeline:**
+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
+
+**Licensing:** All data are collected from public-domain Vietnamese government/service portals and 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 score   | 4.58 / 5      |
+| Human usefulness      | 4.29 / 5      |
+
+These results confirm that HVU_QA is a high-quality resource for developing robust FAQ-style question generation models.
+
+---
+
+## ⚡ How to Use
+
+### 📦 Install Dependencies
+
+```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])
+```
+
+---
+
+## 🚀 Example Usage
+
+### Fine-tuning
+
+```bash
+python fine_tune_qg.py
+```
+
+This will:
+1. Load the dataset from `30ktrain.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:
+Iced milk coffee 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, please cite:
+
+```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}
+}
+```