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-# Dataset Card for 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 Summary
-
-- **Language:** Vietnamese  
-- **Format:** SQuAD-style JSON  
-- **Total samples:** 30,000 QCA triples (full corpus released)  
-- **Domains covered:** Social services, labor law, administrative processes, and other public service topics  
-
-Each entry in the dataset has the following structure:
-- **Question:** Generated or extracted question  
-- **Context:** Supporting text passage from which the answer is derived  
-- **Answer:** Answer span within the context  
-
----
-
-## Supported Tasks and Benchmarks
-
-- **Question Generation (QG)**  
-- **Question Answering (QA)**  
-- **FAQ-style dialogue systems**  
-
-A fine-tuned `VietAI/vit5-base` model trained on HVU_QA achieved:  
-- **BLEU:** 90.61  
-- **Semantic similarity:** 97.0% (cosine similarity ≥ 0.8)  
-- **Human evaluation:**  
-  - Grammaticality: 4.58 / 5  
-  - Usefulness: 4.29 / 5  
-
----
-
-## Languages
-
-- **Vietnamese** (primary)  
-
----
-
-## Dataset Structure
-
-### Data Fields
-
-Each sample contains:
-- `question`: A natural language question  
-- `context`: Supporting text passage  
-- `answer`: The extracted answer span  
-
-### Data Splits
-
-| Split | Size |
-|-------|------|
-| Train | 30,000 |
-
----
-
-## Dataset Creation
-
-### 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 Q–C–A triples  
-4. **AI-assisted filtering** to remove noisy or factually inconsistent samples  
-
----
-
-## Usage Example
-
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("DANGDOCAO/GeneratingQuestions")
-print(dataset["train"][0])
-```
-
-Example output:
-```json
-{
-  "question": "What type of coffee is famous in Vietnam?",
-  "context": "Iced milk coffee is a famous drink in Vietnam.",
-  "answer": "Iced milk coffee"
-}
-```
-
----
-
-## Training & Fine-tuning
-
-To fine-tune a question generation model:
-
-```bash
-python fine_tune_qg.py
-```
-
-- Loads `30ktrain.json`  
-- Fine-tunes `VietAI/vit5-base`  
-- Saves model as `t5-viet-qg-finetuned/`  
-
-👉 Alternatively, you can use the pre-trained model provided here:  
-[Pre-trained model link](https://huggingface.co/datasets/DANGDOCAO/GeneratingQuestions/tree/main)
-
----
-
-## Question Generation Example
-
-```bash
-python generate_question.py
-```
-
-**Input passage:**
-```
-Iced milk coffee is a famous drink in Vietnam.
-```
-
-**Generated questions:**
-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?  
-
----
-
-## 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}
-}
-```
-
----
-
-## License
-
-This dataset is released for **research purposes only** under the **CC BY-NC-SA 4.0 license**.