metadata

dataset_info:
  features:
    - name: Question
      dtype: string
    - name: A
      dtype: string
    - name: B
      dtype: string
    - name: C
      dtype: string
    - name: D
      dtype: string
    - name: answer
      dtype: string
  splits:
    - name: train
      num_bytes: 18899
      num_examples: 100
  download_size: 13287
  dataset_size: 18899
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: apache-2.0
task_categories:
  - multiple-choice
language:
  - ar
pretty_name: 'CIDAR-MCQ-100 '
size_categories:
  - n<1K

Dataset Card for "CIDAR-MCQ-100"

CIDAR-MCQ-100

CIDAR-MCQ-100 contains 100 multiple-choice questions and answers about the Arabic culture.

📚 Datasets Summary

Name	Explanation
CIDAR	10,000 instructions and responses in Arabic
CIDAR-EVAL-100	100 instructions to evaluate LLMs on cultural relevance
CIDAR-MCQ-100	100 Multiple choice questions and answers to evaluate LLMs on cultural relevance

Category	CIDAR-EVAL-100	CIDAR-MCQ-100
Food&Drinks	14	8
Names	14	8
Animals	2	4
Language	10	20
Jokes&Puzzles	3	7
Religion	5	10
Business	6	7
Cloths	4	5
Science	3	4
Sports&Games	4	2
Tradition	4	10
Weather	4	2
Geography	7	8
General	4	3
Fonts	5	2
Literature	10	2
Plants	3	0
Total	100	100

📋 Dataset Structure

Question(str): Question about the Arabic culture.
A(str): First choice.
B(str): Second choice.
C(str): Third choice.
D(str): Fourth choice.
answer(str): The correct choice from A,B,C, and D.

📁 Loading The Dataset

You can download the dataset directly from HuggingFace or use the following code:

from datasets import load_dataset
cidar = load_dataset('arbml/CIDAR-MCQ-100')

📄 Sample From The Dataset:

Question: حدد حيوان مشهور في المنطقة

A: الجمل B: اللاما C: الكانغرو D: الدب القطبي answer: A

🔑 License

The dataset is licensed under Apache-2.0. Apache-2.0.

Citation

@misc{alyafeai2024cidar,
      title={{CIDAR: Culturally Relevant Instruction Dataset For Arabic}}, 
      author={Zaid Alyafeai and Khalid Almubarak and Ahmed Ashraf and Deema Alnuhait and Saied Alshahrani and Gubran A. Q. Abdulrahman and Gamil Ahmed and Qais Gawah and Zead Saleh and Mustafa Ghaleb and Yousef Ali and Maged S. Al-Shaibani},
      year={2024},
      eprint={2402.03177},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}