--- dataset_info: features: - name: Source dtype: string - name: Sentence dtype: string - name: Topic dtype: string splits: - name: train num_bytes: 10696 num_examples: 100 download_size: 6725 dataset_size: 10696 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - text-generation language: - ar pretty_name: CIDAR-EVAL-100 size_categories: - n<1K --- # Dataset Card for "CIDAR-EVAL-100" # CIDAR-EVAL-100 CIDAR-EVAL-100 contains **100** instructions about Arabic culture. The dataset can be used to evaluate an LLM for culturally relevant answers. ## 📚 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 - `Source(str)`: Source of the instruction. - `Sentence(str)`: Sentence of the instruction. - `Topic(str)`: Topic covered by the instruction. ## 📁 Loading The Dataset You can download the dataset directly from HuggingFace or use the following code: ```python from datasets import load_dataset cidar = load_dataset('arbml/CIDAR-EVAL-100') ``` ## 📄 Sample From The Dataset: **Source**: Manual **Sentence**: أخبرني عن أشهر أربعة حيوانات في المنطقة **Topic**: Animals ## 🔑 License The dataset is licensed under **Apache-2.0**. [Apache-2.0](https://www.apache.org/licenses/LICENSE-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} } ```