Initial commit: Multi-TW Chinese language learning dataset

.gitattributes

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README.md

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+# Multi-TW: Traditional Chinese Language Learning Dataset
+
+## Dataset Description
+
+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.
+
+## Dataset Structure
+
+The dataset contains 900 samples in the validation split, suitable for benchmarking purposes.
+
+### Data Fields
+
+- `id`: Unique identifier for each question
+- `instruction`: Task instructions in Chinese
+- `question`: The question text in Chinese  
+- `option1`: Multiple choice option A
+- `option2`: Multiple choice option B
+- `option3`: Multiple choice option C
+- `option4`: Multiple choice option D (may be empty)
+- `answer`: Correct answer (A, B, C, or D)
+- `image`: PIL Image object (for visual questions)
+- `audio`: Audio data with sampling rate (for audio questions)
+
+### Data Composition
+
+- **Total samples**: 900
+- **Samples with images**: 450  
+- **Samples with audio**: 450
+- **Answer distribution**: A: 249, B: 261, C: 263, D: 127
+- **Question types**: L (Listening): 660, R (Reading): 240
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("ntuai/multi-tw")
+validation_data = dataset["validation"]
+
+# Access a sample
+sample = validation_data[0]
+print(f"Question: {sample['question']}")
+print(f"Options: {sample['option1']}, {sample['option2']}, {sample['option3']}")
+print(f"Answer: {sample['answer']}")
+
+# Check if sample has image or audio
+if sample['image'] is not None:
+    # Process image
+    image = sample['image']
+    
+if sample['audio'] is not None:
+    # Process audio
+    audio_array = sample['audio']['array']
+    sampling_rate = sample['audio']['sampling_rate']
+```
+
+## Dataset Statistics
+
+The dataset covers various aspects of Chinese language learning:
+
+- **Visual comprehension**: Questions requiring image understanding
+- **Audio comprehension**: Questions requiring audio understanding  
+- **Multiple choice format**: 3-4 options per question
+- **Balanced distribution**: Relatively even distribution across answer choices
+
+## License
+
+MIT License
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{multi_tw_2024,
+  title={Multi-TW: Chinese Language Learning Dataset},
+  author={NTUAI Club},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/ntuai/multi-tw}
+}
+```

dataset_dict.json

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+{"splits": ["validation"]}

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validation/dataset_info.json

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+}

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