Datasets:

DANGDOCAO
/

GeneratingQuestions

@@ -1,239 +0,0 @@
-# HVU_QA_NLP
-**HVU_QA_NLP** 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 QA dataset.
-2. Generate multiple, diverse questions given a user-provided text passage (context).
----
-## 📁 Dataset Format
-Your dataset must follow the **SQuAD v2.0** JSON structure:
-```json
-{
-  "version": "v2.0",
-  "data": [
-    {
-      "title": "Đồ uống nào của Việt Nam từng lọt top ngon nhất thế giới?",
-      "paragraphs": [
-        {
-          "qas": [
-            {
-              "id": "q1_1",
-              "question": "Đồ uống nào của Việt Nam từng lọt top ngon nhất thế giới?",
-              "answers": [
-                {
-                  "text": "Theo bản đánh giá tháng 2/2023 của Taste Atlas...",
-                  "answer_start": "Theo bản đánh giá tháng 2/2023 của Taste Atlas..."
-                }
-              ],
-              "is_impossible": false
-            }
-          ]
-        }
-      ]
-    }
-  ]
-}
-```
-**Required fields:**
-- `title` → used as context
-- `question` → target question
-- `answers[0].text` → seed answer for training
-- `is_impossible` → filter for valid QAs
-**File name:** `30ktrain.json` (UTF-8)
----
-## 📁 Datasets
-This repository provides datasets for **training** and **evaluating** Vietnamese question generation models.
-### 🔹 `DataTotalQCAtriples30k/`
-- **`30ktrain.json`** → 30,000 QCA triples for training.
-### 🔹 `Datatest1k/`
-- **`testorgin1k.json`** → 1,000 examples for manual & automatic evaluation.
-### 🔹 `Datatrain29k/`
-- **`29kcorpustag.json`** → 29,000 preprocessed QCA triples.
-> All files are UTF-8 encoded and ready for direct use in NLP pipelines.
----
-## 📊 Evaluation Results
-We performed **manual evaluation on 500 samples** and **automatic evaluation on 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     |
----
-## 🔧 Installation
-Create a virtual environment and install dependencies:
-### Windows (PowerShell)
-```powershell
-python -m venv .venv
-.venv\Scripts\Activate.ps1
-python -m pip install --upgrade pip
-pip install torch transformers datasets scikit-learn
-```
-### Linux / macOS
-```bash
-python3 -m venv .venv
-source .venv/bin/activate
-python -m pip install --upgrade pip
-pip install torch transformers datasets scikit-learn
-```
----
-## 🚀 Training (Fine-tuning)
-Make sure `30ktrain.json` is in the same folder as `fine_tune_qg.py`.
-Run:
-```bash
-python fine_tune_qg.py
-```
-**What happens:**
-- Loads `30ktrain.json`
-- Splits into 80% train / 20% validation
-- Tokenizes using `T5Tokenizer`
-- Fine-tunes `VietAI/vit5-base` for 3 epochs
-- Saves model + tokenizer to `t5-viet-qg-finetuned/`
-**Key training parameters** (edit in `fine_tune_qg.py` if needed):
-- `per_device_train_batch_size`: 1
-- `learning_rate`: 2e-4
-- `num_train_epochs`: 3
-- `max_input_length`: 512
-- `max_target_length`: 64
----
-## 💡 Generating Questions
-Once training is done, use `generate_question.py` to generate new questions.
-Ensure:
-- `MODEL_DIR` → `t5-viet-qg-finetuned/`
-- `DATA_PATH` → `30ktrain.json`
-Run:
-```bash
-python generate_question.py
-```
-Steps:
-1. Enter a context passage (Vietnamese)
-2. Enter number of questions (default 20, max 200)
-3. Script will:
-   - Find best match in dataset by title similarity
-   - Use matched answer + your context
-   - Generate multiple unique questions with top-k & top-p sampling
-4. Output lists generated questions.
----
-## ⚙️ Generation Settings
-In `generate_question.py`, tweak:
-- `top_k` (default 60)
-- `top_p` (default 0.95)
-- `temperature` (default 0.9)
-- `no_repeat_ngram_size` (default 3)
-- `repetition_penalty` (default 1.12)
----
-## 📂 Project Structure
-```
-.
-├── fine_tune_qg.py
-├── generate_question.py
-├── 30ktrain.json
-├── t5-viet-qg-finetuned/
-├── README.md
-└── LICENSE
-```
----
-## 🔍 Example Usage
-**Training**
-```bash
-python fine_tune_qg.py
-```
-**Generating**
-```bash
-python generate_question.py
-```
-Example:
-```
-Nhập đoạn văn bản:
- Cà phê sữa đá là đồ uống nổi tiếng ở Việt Nam.
-Nhập vào số lượng câu hỏi bạn cần: 5
-✅ Các câu hỏi mới được sinh ra:
-1. Loại cà phê nào nổi ti���ng ở Việt Nam?
-2. Tại sao cà phê sữa đá được yêu thích?
-3. Cà phê sữa đá gồm những nguyên liệu gì?
-4. Nguồn gốc của cà phê sữa đá là từ đâu?
-5. Cà phê sữa đá Việt Nam được pha chế như thế nào?
-```
----
-## 🤝 Contribution
-You’re welcome to:
-- Open issues
-- Submit pull requests
-- Suggest new datasets
----
-## 📄 License
-Licensed under the MIT License – see the `LICENSE` file for details.
----
-## 📬 Contact
-- **Ha Nguyen-Tien** (Corresponding author)
-  Email: nguyentienha@hvu.edu.vn
-- **Phuc Le-Hong**
-  Email: Lehongphuc20021408@gmail.com
-- **DANG DO CAO**
-  Email: docaodang532001@gmail.com
----
-*This repository is part of an effort to advance Vietnamese NLP by making question generation more accessible for researchers and developers.*