Update README.md

README.md

@@ -15,7 +15,8 @@ size_categories:
 ---
 # 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.
+**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.
 
 ---
 
@@ -27,19 +28,21 @@ size_categories:
 - **Domains covered:** Social services, labor law, administrative processes, and other public service topics.  
 
 Each data sample contains:
-- `question`: Generated or extracted question
-- `context`: Supporting text passage from which the answer is derived
-- `answer`: Answer span within the context
+
+- `question`: Generated or extracted question  
+- `context`: Supporting text passage from which the answer is derived  
+- `answer`: Answer span within the context  
 
 ---
 
 ## ⚙️ Dataset Creation
 
 **Pipeline:**
-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
+
+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.  
 
 **Licensing:** All data are collected from public-domain Vietnamese government/service portals and released under CC BY 4.0.
 
@@ -49,12 +52,12 @@ Each data sample contains:
 
 A fine-tuned `VietAI/vit5-base` model trained on HVU_QA achieved:
 
-| Metric               | Score        |
-|----------------------|--------------|
-| BLEU                  | 90.61         |
-| Semantic similarity   | 97.0% (cos ≥ 0.8) |
-| Human grammar score   | 4.58 / 5      |
-| Human usefulness      | 4.29 / 5      |
+| Metric                 | Score           |
+|-------------------------|----------------|
+| BLEU                    | 90.61           |
+| Semantic similarity     | 97.0% (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.
 
@@ -69,6 +72,7 @@ These results confirm that HVU_QA is a high-quality resource for developing robu
   "answer": "Iced milk coffee"
 }
 ```
+
 ---
 
 ## ⚡ How to Use
@@ -79,7 +83,7 @@ These results confirm that HVU_QA is a high-quality resource for developing robu
 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)*
+*(Install PyTorch separately from [pytorch.org](https://pytorch.org) if not installed yet.)*
 
 ---
 
@@ -87,6 +91,7 @@ pip install datasets transformers sentencepiece safetensors accelerate evaluate
 
 ```python
 from datasets import load_dataset
+
 ds = load_dataset("DANGDOCAO/GeneratingQuestions", split="train")
 print(ds[0])
 ```
@@ -95,28 +100,30 @@ print(ds[0])
 
 ## 🚀 Example Usage
 
-### 🔹 Fine-tuning
+### Fine-tuning
 
 ```bash
 python fine_tune_qg.py
 ```
 
 This will:
-1. Load the dataset from `30ktrain.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))*
+1. Load the dataset from `30ktrain.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
+### Generating Questions
 
 ```bash
 python generate_question.py
 ```
 
 **Example:**
+
 ```
 Input passage:
 Iced milk coffee is a famous drink in Vietnam.
@@ -124,14 +131,16 @@ 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?
+5. 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`
+**You can adjust** in `generate_question.py`:
+
+- `top_k`, `top_p`, `temperature`, `no_repeat_ngram_size`, `repetition_penalty`
 
 ---
 
@@ -146,4 +155,4 @@ If you use **HVU_QA** in your research, please cite:
   booktitle={Proceedings of the International Conference on Knowledge and Systems Engineering (KSE)},
   year={2025}
 }
-```
+```