Datasets:

DANGDOCAO
/

GeneratingQuestions

@@ -1,293 +0,0 @@
----
-license: mit
-language:
-- vi
-tags:
-- vietai, vietnamese, nlp, question-answering, question-generation, low-resource
-pretty_name: HVU_QA - Vietnamese Question Generation Dataset
-size_categories:
-- 10K<n<100K
----
-# 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 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
-@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.*