Datasets:

DANGDOCAO
/

GeneratingQuestions

@@ -1,88 +1,59 @@
-# 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/
@@ -91,9 +62,7 @@ A fine-tuned model trained on **HVU_QA (VietAI/vit5-base)** achieved:
 ├── 39k_train.json
 └── README.md
 ```
-> All data files are UTF-8 encoded and ready for use in NLP pipelines.
----
 ## 🛠️ Requirements
@@ -101,185 +70,84 @@ A fine-tuned model trained on **HVU_QA (VietAI/vit5-base)** achieved:
 * 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 **`3939k_train.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.*

 ---
+license: mit
+task_categories:
+- question-answering
+- text-generation
+- table-question-answering
+- sentence-similarity
+- feature-extraction
+language:
+- vi
+tags:
+- question-generation
+- nlp
+- faq
+- low-resource
+- code
+pretty_name: HVU_QA
+size_categories:
+- 10K<n<100K
 ---
+# 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:** 3939,000 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/
 ├── 39k_train.json
 └── README.md
 ```
+## 📁 Vietnamese Question Generation Tool
 ## 🛠️ Requirements
 * 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}
 }
+```