Delete HVU_QA

HVU_QA/30ktrain.json

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-oid sha256:a1989e727e0bb58732b6cd569c87fe2e474816d69064d0769f34cd307544d2fa
-size 11786416

HVU_QA/README.md

@@ -1,290 +0,0 @@
-# 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**.
-
-### Data folders
-
-* `DataTotalQCAtriples30k/` → **30,000 training samples** (`30ktrain.json`)
-* `Datatest1k/` → **1,000 samples** for manual & automatic evaluation (`testorgin1k.json`)
-* `Datatrain29k/` → **29,000 preprocessed samples** (`29kcorpustag.json`)
-
-> All data files are UTF-8 encoded and ready for use in NLP pipelines.
-
----
-
-## 📁 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
-
-```
-.
-├── t5-viet-qg-finetuned/
-├── fine_tune_qg.py
-├── generate_question.py
-├── 30ktrain.json
-└── README.md
-```
-
----
-
-## 🛠️ 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 `GenerationQuestions.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:
-Cà phê sữa đá là đồ uống nổi tiếng ở Việt Nam.
-
-Number of questions: 5
-```
-
-✅ Output:
-
-1. Loại cà phê nào nổi tiếng 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?
-
----
-
-## ⚙️ 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.*

HVU_QA/fine_tune_qg.py

@@ -1,102 +0,0 @@
-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,  # vẫn truyền để bạn có thể eval thủ công sau
-        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

@@ -1,154 +0,0 @@
-# generate_question.py
-import json
-from difflib import SequenceMatcher
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-from transformers.utils import logging as hf_logging
-
-# ===== Giảm ồn cảnh báo của transformers =====
-hf_logging.set_verbosity_error()
-
-# ===== Cấu hình đường dẫn =====
-MODEL_DIR = "t5-viet-qg-finetuned"  # thư mục model đã fine-tune
-DATA_PATH = "30ktrain.json"        # file JSON dữ liệu gốc
-
-# ===== Load model & tokenizer 1 lần =====
-tokenizer = T5Tokenizer.from_pretrained(MODEL_DIR)
-model = T5ForConditionalGeneration.from_pretrained(MODEL_DIR)
-
-def find_best_match_from_context(user_context, squad_data):
-    """
-    Tìm bản ghi gần nhất dựa trên article.title (giữ đúng logic code gốc).
-    Trả về tuple (context_title, answer_text, question_text) hoặc None.
-    """
-    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):
-    """Loại câu gần trùng dựa trên tỉ lệ giống nhau."""
-    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):
-    # Load dữ liệu JSON
-    with open(DATA_PATH, "r", encoding="utf-8") as f:
-        squad_data = json.load(f)
-
-    # Tìm bản ghi gần giống nhất (KHÔNG in ra màn hình)
-    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  # chỉ cần answer gốc để bảo toàn tính toàn vẹn
-
-    # Chuẩn bị input cho mô hình (đặt max_length để tránh cảnh báo truncate)
-    input_text = f"answer: {answer} context: {user_context}"
-    inputs = tokenizer(
-        input_text,
-        return_tensors="pt",
-        truncation=True,
-        max_length=max_input_len
-    )
-
-    # Sinh nhiều câu hỏi theo lô để tiết kiệm RAM
-    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,   # chiều dài phần sinh
-            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:  # lọc câu quá ngắn
-                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
-
-    # Cắt đúng số lượng yêu cầu
-    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__":
-    # Nhập đoạn văn
-    user_context = input("\nNhập đoạn văn bản:\n ").strip()
-
-    # Nhập số lượng câu hỏi cần sinh (tùy biến)
-    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
-
-    # Ràng buộc an toàn
-    if total_questions < 1:
-        total_questions = 1
-    if total_questions > 200:
-        print("⚠️ Giới hạn tối đa 200 câu. Sẽ sinh 200 câu.")
-        total_questions = 200
-
-    # batch_size tự động theo quy mô
-    batch_size = 10 if total_questions >= 30 else min(10, total_questions)
-
-    # Thông báo phân tích
-    print("\n🔍 Đang 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
-    )

HVU_QA/t5-viet-qg-finetuned/added_tokens.json

@@ -1,98 +0,0 @@
-{
-  "<extra_id_0>": 36095,
-  "<extra_id_10>": 36085,
-  "<extra_id_11>": 36084,
-  "<extra_id_12>": 36083,
-  "<extra_id_13>": 36082,
-  "<extra_id_14>": 36081,
-  "<extra_id_15>": 36080,
-  "<extra_id_16>": 36079,
-  "<extra_id_17>": 36078,
-  "<extra_id_18>": 36077,
-  "<extra_id_19>": 36076,
-  "<extra_id_1>": 36094,
-  "<extra_id_20>": 36075,
-  "<extra_id_21>": 36074,
-  "<extra_id_22>": 36073,
-  "<extra_id_23>": 36072,
-  "<extra_id_24>": 36071,
-  "<extra_id_25>": 36070,
-  "<extra_id_26>": 36069,
-  "<extra_id_27>": 36068,
-  "<extra_id_28>": 36067,
-  "<extra_id_29>": 36066,
-  "<extra_id_2>": 36093,
-  "<extra_id_30>": 36065,
-  "<extra_id_31>": 36064,
-  "<extra_id_32>": 36063,
-  "<extra_id_33>": 36062,
-  "<extra_id_34>": 36061,
-  "<extra_id_35>": 36060,
-  "<extra_id_36>": 36059,
-  "<extra_id_37>": 36058,
-  "<extra_id_38>": 36057,
-  "<extra_id_39>": 36056,
-  "<extra_id_3>": 36092,
-  "<extra_id_40>": 36055,
-  "<extra_id_41>": 36054,
-  "<extra_id_42>": 36053,
-  "<extra_id_43>": 36052,
-  "<extra_id_44>": 36051,
-  "<extra_id_45>": 36050,
-  "<extra_id_46>": 36049,
-  "<extra_id_47>": 36048,
-  "<extra_id_48>": 36047,
-  "<extra_id_49>": 36046,
-  "<extra_id_4>": 36091,
-  "<extra_id_50>": 36045,
-  "<extra_id_51>": 36044,
-  "<extra_id_52>": 36043,
-  "<extra_id_53>": 36042,
-  "<extra_id_54>": 36041,
-  "<extra_id_55>": 36040,
-  "<extra_id_56>": 36039,
-  "<extra_id_57>": 36038,
-  "<extra_id_58>": 36037,
-  "<extra_id_59>": 36036,
-  "<extra_id_5>": 36090,
-  "<extra_id_60>": 36035,
-  "<extra_id_61>": 36034,
-  "<extra_id_62>": 36033,
-  "<extra_id_63>": 36032,
-  "<extra_id_64>": 36031,
-  "<extra_id_65>": 36030,
-  "<extra_id_66>": 36029,
-  "<extra_id_67>": 36028,
-  "<extra_id_68>": 36027,
-  "<extra_id_69>": 36026,
-  "<extra_id_6>": 36089,
-  "<extra_id_70>": 36025,
-  "<extra_id_71>": 36024,
-  "<extra_id_72>": 36023,
-  "<extra_id_73>": 36022,
-  "<extra_id_74>": 36021,
-  "<extra_id_75>": 36020,
-  "<extra_id_76>": 36019,
-  "<extra_id_77>": 36018,
-  "<extra_id_78>": 36017,
-  "<extra_id_79>": 36016,
-  "<extra_id_7>": 36088,
-  "<extra_id_80>": 36015,
-  "<extra_id_81>": 36014,
-  "<extra_id_82>": 36013,
-  "<extra_id_83>": 36012,
-  "<extra_id_84>": 36011,
-  "<extra_id_85>": 36010,
-  "<extra_id_86>": 36009,
-  "<extra_id_87>": 36008,
-  "<extra_id_88>": 36007,
-  "<extra_id_89>": 36006,
-  "<extra_id_8>": 36087,
-  "<extra_id_90>": 36005,
-  "<extra_id_91>": 36004,
-  "<extra_id_92>": 36003,
-  "<extra_id_93>": 36002,
-  "<extra_id_94>": 36001,
-  "<extra_id_95>": 36000,
-  "<extra_id_9>": 36086
-}

HVU_QA/t5-viet-qg-finetuned/checkpoint-72/added_tokens.json

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-{
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HVU_QA/t5-viet-qg-finetuned/checkpoint-72/config.json

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-{
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HVU_QA/t5-viet-qg-finetuned/checkpoint-72/generation_config.json

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-{
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HVU_QA/t5-viet-qg-finetuned/checkpoint-72/special_tokens_map.json

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-{
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HVU_QA/t5-viet-qg-finetuned/checkpoint-72/spiece.model

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-size 820370

HVU_QA/t5-viet-qg-finetuned/checkpoint-72/tokenizer_config.json

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