HVU_QA

README.md

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+# HVU_QA
+
+**HVU_QA** is a project dedicated to sharing datasets and tools for **Question Generation Processing (NLP)**, developed and maintained by the research team at **Hung Vuong University (HVU), Phu Tho, Vietnam**.  
+This project is supported by **Hung Vuong University, Phu Tho, Vietnam**, with the aim of advancing research and applications in low-resource language processing, particularly for the Vietnamese language.
+
+---
+
+## 📚 Overview
+
+This repository enables you to:
+
+1. Fine-tune the [VietAI/vit5-base](https://huggingface.co/datasets/DANGDOCAO/GeneratingQuestions) model on your own GQ dataset.
+2. Generate multiple, diverse questions given a user-provided text passage (context).
+
+---
+
+## 📁 Datasets
+
+* Built following the **SQuAD v2.0 standard**, ensuring compatibility with NLP pipelines.
+* Includes tens of thousands of high-quality **Question–Context–Answer triples (QCA)**.
+* Suitable for both **training** and **evaluation**.
+
+---
+
+## 📁 Vietnamese Question Generation Tool
+
+A **command-line tool** for:
+
+* **Fine-tuning** a question generation model.
+* **Automatically generating questions** from Vietnamese text.
+
+Built on **Hugging Face Transformers (VietAI/vit5-base)** and **PyTorch**.
+
+---
+
+## Features
+
+* Fine-tune a question generation model with SQuAD v2.0 format data.
+* Generate diverse and creative questions from text passages.
+* Flexible generation parameters (`top-k`, `top-p`, `temperature`, etc.).
+* Simple command-line usage.
+* GPU support if available.
+
+---
+
+## 📊 Evaluation Results
+
+We conducted both **manual evaluation** (500 samples) and **automatic evaluation** (1,000 samples).
+
+| Evaluation Type  | Precision | Recall | F1-Score |
+|------------------|-----------|--------|----------|
+| Automatic (1000) | 0.85      | 0.83   | 0.84     |
+| Manual (500)     | 0.88      | 0.86   | 0.87     |
+
+➡️ The model generates diverse, grammatically correct, and contextually appropriate questions.
+
+---
+
+## Creation Process
+
+The dataset was built using a **4-stage automated pipeline**:
+
+1. Select relevant QA websites from trusted sources.
+2. Automatic crawling to collect raw QA pages.
+3. Semantic tag extraction to obtain clean Question–Context–Answer triples.
+4. AI-assisted filtering to remove noisy or inconsistent samples.
+
+---
+
+## 📝 Quality Evaluation
+
+A fine-tuned model trained on **HVU_QA (VietAI/vit5-base)** achieved:
+
+* **BLEU Score**: 90.61
+* **Semantic similarity**: 97.0% (cosine ≥ 0.8)
+* **Human evaluation**:
+  * Grammar: **4.58 / 5**
+  * Usefulness: **4.29 / 5**
+
+➡️ These results confirm that **HVU_QA is a high-quality resource** for developing robust FAQ-style question generation models.
+
+---
+
+## 📂 Project Structure
+
+```
+.HVU_QA
+├── t5-viet-qg-finetuned/
+├── fine_tune_qg.py
+├── generate_question.py
+├── 30ktrain.json
+└── README.md
+```
+> All data files are UTF-8 encoded and ready for use in NLP pipelines.
+
+---
+
+## 🛠️ Requirements
+
+* Python 3.8+
+* PyTorch >= 1.9
+* Transformers >= 4.30
+* scikit-learn
+* Fine-tuned model (download at: [link](https://huggingface.co/datasets/DANGDOCAO/GeneratingQuestions/tree/main))
+
+---
+
+## ⚙️ Setup
+
+### 🛠️ Step 1: Download and Extract
+
+1. Download `HVU_QA.zip`
+2. Extract into a folder, e.g.:
+
+   ```
+   D:\your\HVU_QA
+   ```
+
+### 🛠️ Step 2: Add to Environment Path (if needed)
+
+1. Open **System Properties → Environment Variables**
+2. Select `Path` → **Edit** → **New**
+3. Add the path, e.g.:
+
+   ```
+   D:\your\HVU_QA
+   ```
+
+### 🛠️ Step 3: Open in Visual Studio Code
+
+```
+File > Open Folder > D:\HVU_QA
+```
+
+### 🛠️ Step 4: Install Required Libraries
+
+Open **Terminal** and run:
+
+#### Windows (PowerShell)
+
+**Required only**
+
+```powershell
+python -m pip install --upgrade pip
+pip install torch transformers datasets scikit-learn sentencepiece safetensors
+```
+
+**Required + Optional**
+
+```powershell
+python -m pip install --upgrade pip
+pip install torch transformers datasets scikit-learn sentencepiece safetensors accelerate tensorboard evaluate sacrebleu rouge-score nltk
+```
+
+#### Linux / macOS (bash/zsh)
+
+**Required only**
+
+```bash
+python3 -m pip install --upgrade pip
+pip install torch transformers datasets scikit-learn sentencepiece safetensors
+```
+
+**Required + Optional**
+
+```bash
+python3 -m pip install --upgrade pip
+pip install torch transformers datasets scikit-learn sentencepiece safetensors accelerate tensorboard evaluate sacrebleu rouge-score nltk
+```
+
+✅ Verify installation:
+
+* Windows (PowerShell)
+
+```powershell
+python -c "import torch, transformers, datasets, sklearn, sentencepiece, safetensors, accelerate, tensorboard, evaluate, sacrebleu, rouge_score, nltk; print('✅ All dependencies installed correctly!')"
+```
+
+* Linux/macOS
+
+```bash
+python3 -c "import torch, transformers, datasets, sklearn, sentencepiece, safetensors, accelerate, tensorboard, evaluate, sacrebleu, rouge_score, nltk; print('✅ All dependencies installed correctly!')"
+```
+
+---
+
+## Usage
+
+* Train and evaluate a question generation model.
+* Develop Vietnamese NLP tools.
+* Conduct linguistic research.
+
+### Training (Fine-tuning)
+
+When you run `fine_tune_qg.py`, the script will:
+
+1. Load the dataset from **`30ktrain.json`**
+2. Fine-tune the `VietAI/vit5-base` model
+3. Save the trained model into a new folder named **`t5-viet-qg-finetuned/`**
+
+Run:
+
+```bash
+python fine_tune_qg.py
+```
+
+### Generating 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?
+
+---
+
+## ⚙️ Generation Settings
+
+In `generate_question.py`, you can adjust:
+
+* `top_k`, `top_p`, `temperature`, `no_repeat_ngram_size`, `repetition_penalty`
+
+---
+
+## 🤝 Contribution
+
+We welcome contributions:
+
+* Open issues
+* Submit pull requests
+* Suggest improvements or add datasets
+
+---
+
+## 📄 Citation
+
+If you use this repository or datasets in research, please cite:
+
+**Ha Nguyen-Tien, Phuc Le-Hong, Dang Do-Cao, Cuong Nguyen-Hung, Chung Mai-Van. 2025. A Method to Build QA Corpora for Low-Resource Languages. Proceedings of KSE 2025. ACM TALLIP.**
+
+### 📚 BibTeX
+
+```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 KSE 2025},
+  year={2025}
+}
+```
+
+---
+
+## 📬 Contact
+
+* **Ha Nguyen-Tien** (Corresponding author)  
+  📧 [nguyentienha@hvu.edu.vn](mailto:nguyentienha@hvu.edu.vn)
+
+* **Phuc Le-Hong**  
+  📧 [Lehongphuc20021408@gmail.com](mailto:Lehongphuc20021408@gmail.com)
+
+* **Dang Do-Cao**  
+  📧 [docaodang532001@gmail.com](mailto:docaodang532001@gmail.com)
+
+📍 Faculty of Engineering and Technology, Hung Vuong University, Phu Tho, Vietnam  
+🌐 [https://hvu.edu.vn](https://hvu.edu.vn)
+
+---
+
+*This repository is part of our ongoing effort to support Vietnamese NLP and make language technology more accessible for low-resource and underrepresented languages.*