Datasets:

DANGDOCAO
/

GeneratingQuestions

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HVU_QA/README.md DELETED Viewed

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-# 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 (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.*

HVU_QA/fine_tune_qg.py DELETED Viewed

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-import json
-from datasets import Dataset
-from sklearn.model_selection import train_test_split
-from transformers import (
-    T5Tokenizer,
-    T5ForConditionalGeneration,
-    TrainingArguments,
-    Trainer
-)
-def load_squad_data(file_path):
-    with open(file_path, "r", encoding="utf-8") as f:
-        squad_data = json.load(f)
-    data = []
-    for article in squad_data["data"]:
-        context = article.get("title", "")
-        for paragraph in article["paragraphs"]:
-            for qa in paragraph["qas"]:
-                if not qa.get("is_impossible", False) and qa.get("answers"):
-                    answer = qa["answers"][0]["text"]
-                    question = qa["question"]
-                    input_text = f"answer: {answer} context: {context}"
-                    data.append({"input": input_text, "target": question})
-    return data
-def preprocess_function(example, tokenizer, max_input_length=512, max_target_length=64):
-    model_inputs = tokenizer(
-        example["input"],
-        max_length=max_input_length,
-        padding="max_length",
-        truncation=True,
-    )
-    labels = tokenizer(
-        text_target=example["target"],
-        max_length=max_target_length,
-        padding="max_length",
-        truncation=True,
-    )
-    model_inputs["labels"] = labels["input_ids"]
-    return model_inputs
-def main():
-    data_path = "30ktrain.json"
-    output_dir = "t5-viet-qg-finetuned"
-    logs_dir = "logs"
-    model_name = "VietAI/vit5-base"
-    print("Tải mô hình và tokenizer...")
-    tokenizer = T5Tokenizer.from_pretrained(model_name)
-    model = T5ForConditionalGeneration.from_pretrained(model_name)
-    print("Đọc và chia dữ liệu...")
-    raw_data = load_squad_data(data_path)
-    train_data, val_data = train_test_split(raw_data, test_size=0.2, random_state=42)
-    train_dataset = Dataset.from_list(train_data)
-    val_dataset = Dataset.from_list(val_data)
-    tokenized_train = train_dataset.map(
-        lambda x: preprocess_function(x, tokenizer),
-        batched=True,
-        remove_columns=["input", "target"]
-    )
-    tokenized_val = val_dataset.map(
-        lambda x: preprocess_function(x, tokenizer),
-        batched=True,
-        remove_columns=["input", "target"]
-    )
-    print("Cấu hình huấn luyện...")
-    training_args = TrainingArguments(
-        output_dir=output_dir,
-        overwrite_output_dir=True,
-        per_device_train_batch_size=1,
-        gradient_accumulation_steps=1,
-        num_train_epochs=3,
-        learning_rate=2e-4,
-        weight_decay=0.01,
-        warmup_steps=0,
-        logging_dir=logs_dir,
-        logging_steps=10,
-        fp16=False
-    )
-    print("Huấn luyện mô hình...")
-    trainer = Trainer(
-        model=model,
-        args=training_args,
-        train_dataset=tokenized_train,
-        eval_dataset=tokenized_val,
-        tokenizer=tokenizer,
-    )
-    trainer.train()
-    print("Lưu mô hình...")
-    model.save_pretrained(output_dir)
-    tokenizer.save_pretrained(output_dir)
-    print("Huấn luyện hoàn tất!")
-if __name__ == "__main__":
-    main()

HVU_QA/generate_question.py DELETED Viewed

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-import json
-from difflib import SequenceMatcher
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-from transformers.utils import logging as hf_logging
-hf_logging.set_verbosity_error()
-MODEL_DIR = "t5-viet-qg-finetuned"
-DATA_PATH = "30ktrain.json"
-tokenizer = T5Tokenizer.from_pretrained(MODEL_DIR)
-model = T5ForConditionalGeneration.from_pretrained(MODEL_DIR)
-def find_best_match_from_context(user_context, squad_data):
-    best_score, best_entry = 0.0, None
-    ui = user_context.lower()
-    for article in squad_data.get("data", []):
-        context_title = article.get("title", "")
-        score_title = SequenceMatcher(None, ui, context_title.lower()).ratio()
-        for paragraph in article.get("paragraphs", []):
-            for qa in paragraph.get("qas", []):
-                answers = qa.get("answers", [])
-                if not answers:
-                    continue
-                answer_text = answers[0].get("text", "").strip()
-                question_text = qa.get("question", "").strip()
-                score = score_title
-                if score > best_score:
-                    best_score = score
-                    best_entry = (context_title, answer_text, question_text)
-    return best_entry
-def _near_duplicate(q, seen, thr=0.90):
-    for s in seen:
-        if SequenceMatcher(None, q, s).ratio() >= thr:
-            return True
-    return False
-def generate_questions(user_context,
-                       total_questions=20,
-                       batch_size=10,
-                       top_k=60,
-                       top_p=0.95,
-                       temperature=0.9,
-                       max_input_len=512,
-                       max_new_tokens=64):
-    with open(DATA_PATH, "r", encoding="utf-8") as f:
-        squad_data = json.load(f)
-    best_entry = find_best_match_from_context(user_context, squad_data)
-    if best_entry is None:
-        print("Không tìm thấy dữ liệu phù hợp trong file JSON.")
-        return
-    _, answer, _ = best_entry
-    input_text = f"answer: {answer} context: {user_context}"
-    inputs = tokenizer(
-        input_text,
-        return_tensors="pt",
-        truncation=True,
-        max_length=max_input_len
-    )
-    unique_questions = []
-    remaining = total_questions
-    while remaining > 0:
-        n = min(batch_size, remaining)
-        outputs = model.generate(
-            **inputs,
-            do_sample=True,
-            top_k=top_k,
-            top_p=top_p,
-            temperature=temperature,
-            max_new_tokens=max_new_tokens,
-            num_return_sequences=n,
-            no_repeat_ngram_size=3,
-            repetition_penalty=1.12
-        )
-        for out in outputs:
-            q = tokenizer.decode(out, skip_special_tokens=True).strip()
-            if len(q) < 5:
-                continue
-            if not _near_duplicate(q, unique_questions, thr=0.90):
-                unique_questions.append(q)
-        remaining = total_questions - len(unique_questions)
-        if remaining <= 0:
-            break
-    unique_questions = unique_questions[:total_questions]
-    print("Các câu hỏi mới được sinh ra:")
-    for i, q in enumerate(unique_questions, 1):
-        print(f"{i}. {q}")
-if __name__ == "__main__":
-    user_context = input("\nNhập đoạn văn bản:\n ").strip()
-    raw_n = input("\nNhập vào số lượng câu hỏi bạn cần:").strip()
-    if raw_n == "":
-        total_questions = 20
-    else:
-        try:
-            total_questions = int(raw_n)
-        except ValueError:
-            print("Giá trị không hợp lệ. Dùng mặc định 20.")
-            total_questions = 20
-    if total_questions < 1:
-        total_questions = 1
-    if total_questions > 200:
-        total_questions = 200
-    batch_size = 20 if total_questions >= 30 else min(20, total_questions)
-    print("\nĐang phân tích dữ liệu...\n")
-    generate_questions(
-        user_context=user_context,
-        total_questions=total_questions,
-        batch_size=batch_size,
-        top_k=60,
-        top_p=0.95,
-        temperature=0.9,
-        max_input_len=512,
-        max_new_tokens=64
-    )