README.md

---
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

<table>
  <tr>
<th>Name</th>
<th>Explanation</th>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/arbml/cidar>CIDAR</a></t> 
<td>10,000 instructions and responses in Arabic</td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/arbml/cidar-eval-100><b>CIDAR-EVAL-100</b></a></t> 
<td>100 instructions to evaluate LLMs on cultural relevance</td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/arbml/cidar-mcq-100>CIDAR-MCQ-100</a></t> 
<td>100 Multiple choice questions and answers to evaluate LLMs on cultural relevance </td>
</tr>
</table>

## 📋 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}
}
```