ArSyra/arsyra-maghrebi

🇲🇦 ArSyra Maghreb Arabic (Darija) Dataset

Addressing the critical underrepresentation of North African Arabic in NLP.

---

---

Dataset Summary

Maghrebi Arabic (Darija) data from Morocco, Algeria, Tunisia, Libya, and Mauritania. Often considered the most linguistically distinct Arabic dialect group due to Amazigh and French substrate influence, Maghrebi Arabic is critically underrepresented in existing NLP resources.

This dataset addresses that gap with authentic dialectal data spanning all linguistic categories, contributed by verified native speakers from across North Africa. Essential for any Arabic NLP system aiming to serve the 100+ million Maghrebi Arabic speakers.

Statistic	Value
Total Records	7,223
Linguistic Categories	20
Countries Represented	4 (Tunisia, Morocco, Algeria, Libya)
Dialect Groups	1 (Maghrebi)
Average Quality Score	76.5/100
License	CC-BY-NC-SA-4.0
Last Updated	2026-02-20

How ArSyra Compares to Existing Arabic Datasets

Dataset	Records	Dialects	Countries	Categories	Crowdsourced	MSA↔Dialect Pairs
ArSyra (arsyra-maghrebi)	7,223	1	4	20	✅	✅
NADI (shared task)	~20K	4	21	1	❌ (Twitter)	❌
MADAR	~12K	6	25	1	✅ (paid)	✅
AOC (Arabic Online Commentary)	~100K	—	—	3	❌ (scraped)	❌
DART (Dialect Arabic)	~25K	5	—	1	❌ (Twitter)	❌
ArSentD-LEV	~4K	1	4	1	❌ (Twitter)	❌

ArSyra's advantages: Authentic native-speaker data (not scraped), multi-category structure, parallel MSA↔dialect text, quality scored, and continuously growing.

Related ArSyra Datasets

Explore our other specialized Arabic dialect datasets:

• 🏆 ArSyra Complete — Multi-Dialect Arabic Dataset — The most comprehensive crowdsourced Arabic dialect dataset available.
• 🤖 ArSyra Chatbot — Conversational Arabic Training Data — Purpose-built training data for Arabic conversational AI systems.
• 🌐 ArSyra Translation — Arabic Dialect–MSA Parallel Corpus — Parallel corpus bridging Modern Standard Arabic and regional dialects.
• 🇪🇬 ArSyra Egyptian Arabic (Masri) Dataset — The most widely understood Arabic dialect — now as structured NLP data.
• 🇸🇾 ArSyra Levantine Arabic (Shami) Dataset — Authentic Shami dialect data from Syria, Lebanon, Jordan, and Palestine.
• 🇸🇦 ArSyra Gulf Arabic (Khaliji) Dataset — Gulf Arabic data from the Arabian Peninsula's rapidly growing digital population.

Browse all datasets: huggingface.co/ArSyra | arsyra.com/datasets.html

Supported Tasks

• Text Generation — Fine-tune language models to generate authentic dialectal Arabic text.
• Text Classification — Train classifiers for dialect identification, sentiment analysis, and content categorization.

Languages

Primary Language: Arabic (ar)

This dataset contains text in Modern Standard Arabic (MSA) and the following regional dialect groups: Maghrebi. Country-level dialect codes: ar-TN, ar-MA, ar-DZ, ar-LY.

---

Dataset Structure

Data Instances

Each record represents a single response from a verified native Arabic speaker to a structured linguistic prompt:

{
  "question_code": "V-0100",
  "category": "vocabulary",
  "subcategory": "food",
  "question_text": "نعناع",
  "answer_text": "نعناع",
  "response_time_ms": 25062,
  "quality_score": 83,
  "country": "TN",
  "answered_at": "2026-02-17T20:57:29.235Z",
  "quality_grade": "B",
  "speaker_hash": "anon-d2ViLTE3"
}

Data Fields

Field	Type	Description
text	string	The Arabic text content — may be in dialect, MSA, or a mix
category	string	Linguistic category (e.g., dialect, proverbs, sentiment, conversation_pairs)
country	string	ISO 3166-1 alpha-2 country code of the speaker (e.g., EG, SA, MA)
dialect_group	string	Broad dialect group: egyptian, levantine, gulf, maghrebi, iraqi, or sudanese
quality_score	int	Human-assigned quality rating from 0 to 100
msa_text	string	Modern Standard Arabic equivalent (where available)
context	string	Additional context about the prompt or response
speaker_hash	string	Anonymized speaker identifier

Data Splits

Split	Examples
train	7,223

Note: A single train split is provided. We recommend creating your own train/validation/test splits based on your use case. For dialect-fair evaluation, stratify by country or dialect_group.

Category Breakdown

Category	Records	% of Total
dialect	888	12.3%
conversation_pairs	657	9.1%
slang	555	7.7%
vocabulary	541	7.5%
taboo	437	6.1%
freeform	426	5.9%
instruction_following	424	5.9%
proverbs	350	4.8%
instructions	340	4.7%
formality_transfer	330	4.6%
medical_dialect	320	4.4%
greetings	310	4.3%
tech_dialect	280	3.9%
price	262	3.6%
paraphrase	240	3.3%
food_culture	240	3.3%
code_switching	208	2.9%
named_entities_local	168	2.3%
sentiment	167	2.3%
control	80	1.1%

---

Dataset Creation

Curation Rationale

Maghrebi Arabic is the most underrepresented major dialect group in Arabic NLP, partly because its significant French and Amazigh influences make it difficult for models trained on Eastern Arabic. This dataset provides the authentic North African data that existing resources lack.

Source Data

Initial Data Collection and Normalization

Data was collected through the ArSyra platform (arsyra.com), a gamified crowdsourcing system where verified native Arabic speakers answer structured linguistic prompts about their dialect. The platform:

1. Verifies speakers through phone number verification (region-specific) and language verification questions
2. Presents structured prompts across multiple linguistic categories: dialect translations, conversation pairs, proverbs, slang, code-switching, sentiment expressions, instruction following, formality registers, and more
3. Gamifies collection through points, leaderboards, and incentive systems to maintain engagement and data quality
4. Automatically enriches responses with metadata: country, dialect group, category, and quality indicators

Who are the source language producers?

Native Arabic speakers from 4 countries across the Arab world (Tunisia, Morocco, Algeria, Libya), participating voluntarily through the ArSyra platform. Speakers represent diverse demographics including age groups, education levels, and urban/rural backgrounds.

Annotations

Annotation Process

Each response receives:

• Automatic quality scoring based on response length, character set validation, and consistency checks
• Category labeling derived from the prompt type
• Dialect group classification based on the speaker's registered country
• Cross-speaker validation where multiple speakers from the same region answer the same prompts

Who are the annotators?

The primary "annotators" are the native speakers themselves, who provide dialectal data along with structured metadata. Quality scoring is automated. No external annotators are used for labeling.

Personal and Sensitive Information

• All speaker identifiers are anonymized — original user IDs are replaced with non-reversible hashed identifiers
• No personally identifiable information (names, locations, phone numbers) is included
• Taboo and sensitive content (where present) is clearly labeled by category
• Speakers provided informed consent during registration for their anonymized data to be used for research

---

Considerations for Using the Data

Social Impact

This dataset contributes to Arabic NLP equity by providing training data for the dialects actually spoken by 400+ million people. Most existing Arabic NLP resources focus exclusively on Modern Standard Arabic, which is no one's native language. By bridging this gap, ArSyra helps ensure that Arabic-speaking populations benefit equally from advances in language technology.

Discussion of Biases

Known biases to consider:

1. Platform access bias — Contributors need internet access and a smartphone, potentially underrepresenting older, rural, or lower-income speakers
2. Country representation — Some countries may be overrepresented depending on recruitment channels
3. Urban bias — Online populations tend to be more urban, potentially underrepresenting rural dialect variants
4. Literacy bias — Written responses may differ from purely spoken dialect, as speakers may unconsciously shift toward MSA
5. Self-selection bias — Voluntary participants may not represent the full demographic spectrum

Other Known Limitations

• Written approximations — Dialectal Arabic has limited standardized orthography; spelling varies across speakers
• Prompt influence — Structured prompts may elicit more formal responses than spontaneous speech
• Quality variation — Despite quality scoring, some responses may be lower quality
• Temporal snapshot — Language evolves; slang and expressions may become dated over time

---

Additional Information

Use Cases

• Building NLP tools for North African Arabic speakers
• Darija-MSA translation systems
• Social media analysis for Maghrebi content
• Research on Arabic-French code-switching in North Africa

Get the Full Dataset

This repository contains a preview sample of 50 records out of 7,223 total. The full dataset is available on request for research and commercial use.

How to Access

Preview (this repo)	50 sample records — free to download and evaluate
Full Dataset	7,223 records — contact us for access
Suggested Price	$149
Contact	support@arsyra.com
Website	arsyra.com

What you get with the full dataset:

• All 7,223 quality-filtered records
• Per-category JSONL splits for easy loading
• Regular updates as our community grows
• Priority support for integration questions
• Custom filtering by country, dialect, or category on request

To request access, email support@arsyra.com with:

1. Your name / organization
2. Intended use case (research / commercial / education)
3. Which product(s) you are interested in

We typically respond within 24 hours.

---

Quick Start

from datasets import load_dataset

# Load the preview sample
dataset = load_dataset("ArSyra/arsyra-maghrebi")
print(f"Preview: {len(dataset['train'])} sample records")

# Browse examples
for example in dataset["train"].select(range(5)):
    print(f"{example['country']} ({example['dialect_group']}): {example['text'][:80]}...")

# For the full dataset (7,223 records), contact: support@arsyra.com

Licensing Information

The preview sample included in this repository is released under CC-BY-NC-SA-4.0.

The full dataset is available under flexible licensing terms:

License	Use Case
CC-BY-NC-SA-4.0	Academic research, non-commercial use
Commercial License	Enterprise, products, SaaS applications

Contact support@arsyra.com for commercial licensing inquiries.

Citation Information

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

@dataset{arsyra_arsyra_maghrebi_2026,
  title     = {ArSyra Maghreb Arabic (Darija) Dataset},
  author    = {{ArSyra Team}},
  year      = {2026},
  url       = {https://huggingface.co/datasets/ArSyra/arsyra-maghrebi},
  publisher = {HuggingFace},
  license   = {CC-BY-NC-SA-4.0},
  note      = {Crowdsourced Arabic dialect dataset with 7,223 records from 4 countries}
}

Contributions

Thanks to the Arabic-speaking community who contributed their dialectal knowledge through the ArSyra platform. To contribute, visit arsyra.com.

---

Dataset card generated by the ArSyra Publish Pipeline. Last updated: 2026-02-20.