---
license: apache-2.0
---

# 📚 Traditional Chinese Examination Dataset 🤗

This project is an collection of exam-related files (PDFs and MP3s) that can be used to train document/audio understanding models, evaluate datasets for indexing question-answer pairs, or OCR preprocessing.

---

## 🗂️ Project Structure

```
project/
├── data/                     # Original exam files (PDFs, MP3s)
├── convert.py                # Script to generate metadata
├── main.py                   # Script to load dataset with datasets
├── metadata.jsonl            # Output metadata file
└── README.md                 # Project documentation
```

---

## 🛠️ Requirements

- Python 3.7+
- Install required packages:

```bash
pip install datasets
```

---

## 🔁 Generate Metadata

Run `convert.py` to extract metadata from filenames and generate a `metadata.jsonl` file.

```bash
python convert.py
```

This will read all files in the `data/` directory and output a line-delimited JSON file (`metadata.jsonl`) describing each file with fields like:

```json
{
  "id": "01-1131-2-一公民-題目",
  "serial": "01",
  "grade": "一",
  "subject": "公民",
  "variant": null,
  "type": "題目",
  "path": "data/01-1131-2-一公民-題目.pdf",
  "format": "pdf"
}
```

---

## 📥 Metadata

Run `main.py` to load the dataset using the Hugging Face `datasets` library.

```bash
python main.py
```

This will load and print a few samples from your dataset.

---

## 🔍 Dataset Fields

- `id`: Unique identifier from the filename
- `serial`: Serial code from the filename
- `grade`: 一 (1st year), 二 (2nd year), 三 (3rd year)
- `subject`: e.g., 公民, 英文, 數學
- `variant`: Optional (e.g., 體, 音) from parentheses
- `type`: 題目 (questions), 答案 (answers), 手寫卷 (handwritten)
- `path`: File path
- `format`: pdf or mp3

---

## 🧠 Tips

- You can use this dataset for training document/audio understanding models, indexing question-answer pairs, or OCR preprocessing.
- Extend `convert.py` to extract text from PDFs or audio features if needed.
- Easily upload the dataset to the Hugging Face Hub via `datasets.Dataset.push_to_hub`.

---

## 📬 Future Work

- [ ] PDF text extraction
- [ ] Audio transcription
- [ ] Tagging with more metadata (exam year, term, difficulty, etc.)
- [ ] Train-test splitting

---

## 📝 License

Apache 2.0 License

---

## 🤝 Contributions Welcome!

Feel free to fork, improve, and PR!

## 🙏 Special Thanks

This project was made possible with the help of **ChatGPT**, an AI assistant by [OpenAI](https://openai.com/), which provided:

- Ez filename parsing and metadata extraction strategy
- Dataset integration guidance
- Project structuring and documentation
- 🧠 Motivation to keep going when the files got messy

Thanks, AI buddy! 🤖💡