update README.md

README.md

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----
-license: mit
-task_categories:
-- question-answering
-language:
-- vi
-tags:
-- ag
-- t5
-- vit5
-- squad-format
-- vietnamese
-- education
-- nlp
-pretty_name: Vietnamese Question Generation
-size_categories:
-- 10K<n<100K
----
-
-# 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}
-}
-```