Delete HVU_QA/README.md

HVU_QA/README.md

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-# 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:** 39,000 QCA triples (full corpus released)
-- **Domains covered:** Social services, labor law, administrative processes, and other public service topics.
-- **Structure of each sample:**
-  - **Question:** Generated or extracted question
-  - **Context:** Supporting text passage from which the answer is derived
-  - **Answer:** Answer span within the context
-
-## ⚙️ 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 Question–Context–Answer triples.
-4. **AI-assisted filtering** to remove noisy or factually inconsistent samples.
-
-## 📊 Quality Evaluation
-A fine-tuned `vit5-base` model trained on HVU_QA achieved:
-
-| Metric                 | Score           |
-|-------------------------|----------------|
-| BLEU                    | 89.1           |
-| Semantic similarity     | 91.5% (cos ≥ 0.8) |
-| Human grammar score     | 4.58 / 5         |
-| Human usefulness score  | 4.29 / 5         |
-
-These results confirm that HVU_QA is a high-quality resource for developing robust FAQ-style question generation models.
-
-## 📁 Dataset Structure
-```
-.HVU_QA
-├── t5-viet-qg-finetuned/
-├── fine_tune_qg.py
-├── generate_question.py
-├── 39k_train.json
-└── README.md
-```
-## 📁 Vietnamese Question Generation Tool
-
-## 🛠️ Requirements
-
-* Python 3.8+
-* PyTorch >= 1.9
-* Transformers >= 4.30
-* scikit-learn
-
-### 📦 Install Required Libraries
-
-```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])
-```
-## 📚 Usage
-
-* Train and evaluate a question generation model.
-* Develop Vietnamese NLP tools.
-* Conduct linguistic research.
-
-### 🔹 Fine-tuning
-
-```bash
-python fine_tune_qg.py
-```
-
-This will:
-
-1. Load the dataset from `39k_train.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:
-Cà phê sữa đá là một loại đồ uống nổi tiếng ở Việt Nam
-(Iced milk coffee is a famous drink in Vietnam)
-
-Number of questions: 5
-```
-**Output:**
-```
-1. Loại cà phê nào nổi tiếng ở Việt Nam?  
-   (What type of coffee is famous in Vietnam?)  
-2. Tại sao cà phê sữa đá lại phổ biến?  
-   (Why is iced milk coffee popular?)  
-3. Cà phê sữa đá bao gồm những nguyên liệu gì?  
-   (What ingredients are included in iced milk coffee?)  
-4. Cà phê sữa đá có nguồn gốc từ đâu?  
-   (Where does iced milk coffee originate from?)  
-5. Cà phê sữa đá Việt Nam được pha chế như thế nào?  
-   (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{nguyen2025method,
-  author    = {Ha Nguyen and Phuc Le and Dang Do and Cuong Nguyen and Chung Mai},
-  title     = {A Method for Building QA Corpora for Low-Resource Languages},
-  booktitle = {Proceedings of the 2025 International Symposium on Information and Communication Technology (SOICT 2025)},
-  year      = {2025},
-  publisher = {Springer},
-  series    = {Communications in Computer and Information Science (CCIS)},
-  address   = {Nha Trang, Vietnam},
-  note      = {To appear}
-}
-```