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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: instruction |
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dtype: string |
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- name: question |
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dtype: string |
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- name: option1 |
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dtype: string |
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- name: option2 |
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dtype: string |
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- name: option3 |
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dtype: string |
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- name: option4 |
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dtype: string |
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- name: answer |
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dtype: string |
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- name: image |
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dtype: image |
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- name: audio |
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dtype: audio |
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splits: |
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- name: validation |
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num_bytes: 873288128.0 |
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num_examples: 900 |
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download_size: 819328629 |
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dataset_size: 873288128.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: validation |
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path: data/validation-* |
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--- |
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# Multi-TW: Traditional Chinese Language Learning Dataset |
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## Dataset Description |
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Multi-TW is a Traditional Chinese language learning and assessment dataset containing 900 multiple-choice questions with multimedia content. This dataset is designed for evaluating multi-modal language models on Traditional Chinese comprehension tasks. |
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## Dataset Structure |
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The dataset contains 900 samples in the validation split, suitable for benchmarking purposes. |
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### Data Fields |
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- `id`: Unique identifier for each question |
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- `instruction`: Task instructions in Chinese |
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- `question`: The question text in Chinese |
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- `option1`: Multiple choice option A |
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- `option2`: Multiple choice option B |
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- `option3`: Multiple choice option C |
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- `option4`: Multiple choice option D (may be empty) |
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- `answer`: Correct answer (A, B, C, or D) |
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- `image`: PIL Image object (for visual questions) |
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- `audio`: Audio data with sampling rate (for audio questions) |
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### Data Composition |
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- **Total samples**: 900 |
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- **Samples with images**: 450 |
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- **Samples with audio**: 450 |
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- **Answer distribution**: A: 249, B: 261, C: 263, D: 127 |
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- **Question types**: L (Listening): 660, R (Reading): 240 |
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## Usage |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("ntuai/multi-tw") |
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validation_data = dataset["validation"] |
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# Access a sample |
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sample = validation_data[0] |
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print(f"Question: {sample['question']}") |
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print(f"Options: {sample['option1']}, {sample['option2']}, {sample['option3']}") |
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print(f"Answer: {sample['answer']}") |
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# Check if sample has image or audio |
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if sample['image'] is not None: |
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# Process image |
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image = sample['image'] |
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if sample['audio'] is not None: |
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# Process audio |
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audio_array = sample['audio']['array'] |
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sampling_rate = sample['audio']['sampling_rate'] |
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``` |
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## Dataset Statistics |
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The dataset covers various aspects of Chinese language learning: |
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- **Visual comprehension**: Questions requiring image understanding |
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- **Audio comprehension**: Questions requiring audio understanding |
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- **Multiple choice format**: 3-4 options per question |
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- **Balanced distribution**: Relatively even distribution across answer choices |
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## License |
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本研究使用華測會官網之公開模擬試題，試題著作權為華測會所有，僅供個人學習使用，不得作為營利用途 |
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## Citation |
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If you use this dataset in your research, please cite: |
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```bibtex |
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@dataset{multi_tw_2025, |
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title={Multi-TW: Benchmarking Multimodal Models on Traditional Chinese Question Answering in Taiwan}, |
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author={Jui-Ming Yao, Bing-Cheng Xie, Sheng-Wei Peng, Hao-Yuan Chen, He-Rong Zheng, Bing-Jia Tan, Peter Shaojui Wang, and Shun-Feng Su}, |
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year={2025}, |
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publisher={Hugging Face}, |
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url={https://huggingface.co/datasets/ntuai/multi-tw} |
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} |
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``` |
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