README.md

---
dataset_info:
  features:
  - name: topic
    dtype: string
  - name: subtopic
    dtype: string
  - name: Arabizi
    dtype: string
  - name: English
    dtype: string
  - name: Darija
    dtype: string
  - name: annotator_dialect
    dtype: string
  splits:
  - name: test
    num_bytes: 132360
    num_examples: 850
  - name: train
    num_bytes: 126518
    num_examples: 850
  download_size: 140184
  dataset_size: 258878
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
  - split: train
    path: data/train-*
license: mit
task_categories:
- translation
size_categories:
- n<1K
language:
- ary
- en
---

# TerjamaBench: A Culturally Specific Dataset for Evaluating Translation Models for Moroccan Darija

Moroccan Darija, the widely spoken dialect of Arabic in Morocco, is rich in cultural expressions, regional variations, and multilingual influences. 
Despite its prevalence, there is a lack of robust, culturally relevant datasets for evaluating models on Moroccan Darija, particularly for translation tasks.
To address this gap, we introduce TerjamaBench, a dataset specifically designed to evaluate the performance of translation models on Moroccan Darija
across various cultural and linguistic contexts. The benchmark includes entries in Darija written in both the Latin alphabet (Arabizi) and Arabic script, 
along with their corresponding English translations.

## Dataset Curation
This dataset has been manually annotated and reviewed by AtlasIA community, and is intended to support research and development in AI technologies that understand and process Moroccan Darija.

## Dataset Composition

The benchmark contains 850 entries, carefully curated and categorized to cover a diverse range of linguistic and cultural scenarios. 

**Note:** Some *Darija* samples are written in diverse *Arabizi* forms or might be mapped to different *English* translations. 
Hence, beware of duplicates if using only *Darija* values.

Below is the breakdown of categories (topics):

| Topic             	| Description                                                          	| Number of samples
|---------------------------|--------------------------------------------------------------------------|-------|
| **Common Phrases**     	| Everyday expressions like greetings and common sayings.              	| 136 |
| **Named Entities**     	| Sentences with proper nouns, place names, cities, etc.               	| 53 |
| **Numeric and Date Expressions** | Sentences containing numbers, dates, or time expressions.         	| 62 |
| **Educational**        	| Sentences from domains like medical, legal, or scientific contexts.  	| 73 |
| **Mixed Language Content** | Sentences combining Darija with MSA, French, or English.             	| 50 |
| **Idioms**             	| Proverbs and sayings unique to Moroccan culture.                     	| 51 |
| **Humor**              	| Jokes, puns, or humorous expressions.                               	| 50 |
| **Religion**           	| Sentences containing religious terms or expressions.                	| 66 |
| **Single Words**       	| Isolated words to test basic translation capabilities.               	| 163 |
| **Long Sentences**     	| Sentences designed to test coherence in lengthy translations.        	| 50 |
| **Incorrect Spellings**	| Sentences with slight spelling errors to evaluate model robustness. 	| 50 |
| **Dialectal Variations**   | Sentences from different Moroccan regions (northern, eastern, southern). | 46 |


<img src="./atlasia_topics_dist.png" alt="Topics Distribution" width="750"/>


## Key Features

- **Cultural and Linguistic Authenticity**: Reflects real-world Moroccan usage and incorporates a mix of everyday expressions, technical terms, and culturally rich content such as proverbs and humor.
- **Manually Curated**: All entries are carefully written, translated, and reviewed by native Moroccan speakers to ensure accuracy and relevance.
- **Extensive Scope**: The dataset captures linguistic diversity with standard Darija as well as regional dialects, and addresses practical challenges like mixed language use and spelling variations.

## Usage

This benchmark can be used to:

- Evaluate machine translation systems.
- Test multilingual language models for Moroccan Darija understanding.
- Develop culturally sensitive AI systems.

To use the dataset, load it from Hugging Face Hub and refer to the accompanying examples for integration into your workflows.

## Learn more
Read our article to learn more about the dataset and the evaluation of SOTA models on the benchmark.
Find it [here](https://huggingface.co/blog/imomayiz/terjama-bench).

## Citation

If you use this dataset in your research or applications, please cite:

```bibtex
@dataset{TerjamaBench,
  title={TerjamaBench: A Culturally Specific Dataset for Evaluating Translation Models for Moroccan Darija},
  author={AtlasIA},
  year={2024},
  url={https://huggingface.co/datasets/atlasia/TerjamaBench/}
}
```

## Acknowledgments
We thank the members of AtlasIA for their dedication and efforts in creating this benchmark. 

Special recognition goes to the contributors:
Aissam Outchakoucht, Chaymae Rami, Mahmoud Bidry, Zaid Chiech, 
Imane Momayiz, Abdelaziz Bounhar, Abir Arsalane, Abdeljalil ElMajjodi, Aymane ElFirdoussi, 
Nouamane Tazi, Salah-Eddine Iguiliz, Hamza Essamaali, Ihssane Nedjaoui, Yousef Khoubrane, Khaoula Alaoui, 
Salah-Eddine Alabouch, Adnan Anouzla, Bilal El Hammouchi, Anas Amchaar,
Taha Boukhari, Mustapha Ajeghrir, Ikhlas Elhamly, Fouad Aurag, Omar Choukrani.