Datasets:

DANGDOCAO
/

GeneratingQuestions

@@ -1,38 +1,13 @@
----
-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
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: 40k_train.json
----
 # HVU_QA
-**HVU_QA** is an open-source Vietnamese Question-Context-Answer (QCA) corpus, accompanied by supporting tools, created to facilitate the development of FAQ-style question generation and question answering systems, particularly for low-resource language settings. The dataset was developed by a research team at Hung Vuong University, Phu Tho, Vietnam, led by Dr. Ha Nguyen, Deputy Head of the Department of Engineering Technology. HVU_QA was constructed using a fully automated data-building pipeline that combines web crawling from reliable sources, semantic tag-based extraction, and AI-assisted filtering, helping ensure high factual accuracy, consistent structure, and practical usability for real-world applications.
 ## 📋 Dataset Description
 - **Language:** Vietnamese
 - **Format:** SQuAD-style JSON
-- **Total samples:** 40,000 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
@@ -41,131 +16,56 @@ configs:
 ## ⚙️ 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 `VietAI/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.
-## 📁 Project Structure
-```text
-HVU_QA/
-├── backend/
-│   ├── __init__.py               # Flask app factory
-│   └── app.py                    # Backend API and frontend serving
-├── frontend/
-│   ├── index.html                # Web interface
-│   ├── app.js                    # Frontend logic
-│   └── style.css                 # Frontend styles
-├── t5-viet-qg-finetuned/         # Fine-tuned model folder
-├── 40k_train.json                # Training dataset in SQuAD-style JSON
-├── fine_tune_qg.py               # Model fine-tuning script
-├── generate_question.py          # CLI question generation script
-├── HVU_QA_tool.py                # Standalone launcher and model downloader
-├── main.py                       # Start the local web application
-├── requirements.txt              # Python dependencies
-├── HVU_QA_end_to_end_guide.ipynb # End-to-end usage notebook
 └── README.md
 ```
 ## 📁 Vietnamese Question Generation Tool
-## 🛠️ Installation and Setup
-This repository supports two common workflows:
-1. **Full project mode:** use the complete source code, web interface, backend API, training script, and local model folder.
-2. **Standalone launcher mode:** use only `HVU_QA_tool.py` to automatically prepare a lightweight runtime, install missing dependencies, download the model, and run the app.
-### Requirements
-* Python 3.10+
-* `pip`
-* Optional: NVIDIA GPU with CUDA for faster inference or training
-### Option A. Install and run the full project
-Create and activate a virtual environment:
-```bash
-python -m venv venv
-```
-```bash
-# Windows
-venv\Scripts\activate
-# macOS / Linux
-source venv/bin/activate
-```
-Install dependencies:
-```bash
-python -m pip install --upgrade pip
-python -m pip install -r requirements.txt
-```
-`requirements.txt` already includes the main runtime packages such as Flask, Torch, Transformers, Datasets, Accelerate, SentencePiece, Safetensors, NumPy, and Hugging Face Hub.
-If you need a CUDA-specific PyTorch build, install the matching version from [pytorch.org](https://pytorch.org) first, then install the remaining dependencies from `requirements.txt`.
-Prepare the local model:
-```bash
-python HVU_QA_tool.py --skip-run
-```
-If `t5-viet-qg-finetuned/` already exists and contains a valid exported model, you can skip this step.
-Run the web app:
 ```bash
-python main.py
 ```
-Then open `http://127.0.0.1:5000` in your browser.
-### Option B. Use the standalone launcher only
-If you only have `HVU_QA_tool.py` in an empty folder, run:
-```bash
-python HVU_QA_tool.py
-```
-The launcher will:
-1. Detect whether the full project already exists.
-2. Create a standalone runtime if needed.
-3. Create a dedicated virtual environment when necessary.
-4. Install missing runtime dependencies.
-5. Download the model from Hugging Face.
-6. Start the local web app automatically.
-### Optional: Load only the 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.
@@ -175,50 +75,48 @@ print(ds[0])
 ### 🔹 Fine-tuning
 ```bash
-python fine_tune_qg.py --train_file 40k_train.json --output_dir t5-viet-qg-finetuned
 ```
 This will:
-1. Load the dataset from `40k_train.json`.
-2. Fine-tune `VietAI/vit5-base`.
-3. Save the trained model into `t5-viet-qg-finetuned/`.
-4. Export the best or final model to a folder such as `best-model/` or `final-model/`.
-### 🔹 Generating Questions
 ```bash
-python generate_question.py --text "Cà phê sữa đá là một loại đồ uống nổi tiếng ở Việt Nam." --num_questions 5
-**Example output:**
-```text
-1. Loại cà phê nào nổi tiếng ở Việt Nam?
-   (Which type of coffee is famous in Vietnam?)
-2. Cà phê sữa đá là loại đồ uống nổi tiếng ở đâu?
-   (Where is iced milk coffee a famous drink?)
-3. Vì sao cà phê sữa đá được nhiều người biết đến?
-   (Why is iced milk coffee well known?)
-4. Đồ uống nào là đặc trưng nổi tiếng ở Việt Nam?
-   (Which beverage is a famous specialty in Vietnam?)
-5. Cà phê sữa đá thường được nhắc đến như thế nào?
-   (How is iced milk coffee commonly described?)
 ```
-**Common CLI options in `generate_question.py`:**
-- `--model_dir`
-- `--task_prefix`
-- `--num_questions`
-- `--device auto|cpu|cuda`
-- `--cpu_threads`
-- `--gpu_dtype auto|float16|bfloat16|float32`
-- `--max_source_length`
-- `--max_new_tokens`
-- `--output_format text|json`
 ## 📌 Citation
 If you use **HVU_QA** in your research, please cite:
 ```bibtex
@@ -232,47 +130,4 @@ If you use **HVU_QA** in your research, please cite:
   address   = {Nha Trang, Vietnam},
   note      = {To appear}
 }
-```
-## ❤️ Support / Funding
-If you find **HVU_QA** useful, please consider supporting our work.
-Your contributions help us maintain the dataset, improve quality, and release new versions (cleaning, expansion, benchmarks, and tools).
-### 🇻🇳 Donate via VietQR (scan to support)
-This **VietQR / NAPAS 247** code can be scanned by Vietnamese banking apps and some international payment apps that support QR bank transfers.
-<img src="QRtk.jpg" alt="VietQR Support" width="320"/>
-**Bank:** VietinBank (Vietnam)
-**Account name:** NGUYEN TIEN HA
-**Account number:** 103004492490
-**Branch:** VietinBank CN PHU THO - HOI SO
-### 🌍 International Support (Quick card payment)
-If you are outside Vietnam, you can support this project via **Buy Me a Coffee**
-(no PayPal account needed; pay directly with a credit/debit card):
-- BuyMeACoffee: https://buymeacoffee.com/hanguyen0408
-### 🌍 International Support (PayPal)
-If you prefer PayPal, you can also support us here:
-- PayPal.me: https://paypal.me/HaNguyen0408
-### ✨ Other ways to support
-- Star this repository / dataset on Hugging Face
-- Cite our paper if you use it in your research
-- Open issues / pull requests to improve the dataset and tools
-## 📬 Contact / Maintainers
-For questions, feedback, collaborations, or issue reports related to HVU_QA, please contact:
-Dr. Ha Nguyen (Project Lead)
-Hung Vuong University, Phu Tho, Vietnam
-Email: nguyentienha@hvu.edu.vn

 # 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
 ## ⚙️ 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.
 ### 🔹 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
   address   = {Nha Trang, Vietnam},
   note      = {To appear}
 }
+```