ArSyra/arsyra-complete

🏆 ArSyra Complete — Multi-Dialect Arabic Dataset

The most comprehensive crowdsourced Arabic dialect dataset available.

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Dataset Summary

A comprehensive, crowdsourced Arabic dialect dataset covering multiple linguistic categories across 20+ Arab countries. ArSyra Complete captures the full spectrum of spoken Arabic — from everyday conversation and cultural proverbs to code-switching patterns, sentiment expressions, and formality registers.

Each entry is contributed by verified native speakers through a gamified collection platform, ensuring authentic dialectal representation. Designed for researchers and engineers building Arabic-aware NLP systems, this dataset bridges the critical gap between Modern Standard Arabic (MSA) resources and the rich diversity of regional dialects actually spoken by 400+ million people.

Statistic	Value
Total Records	32,039
Linguistic Categories	20
Countries Represented	16 (Tunisia, Syria, EU, Egypt, Saudi Arabia, Morocco, Algeria, Iraq, Jordan, Lebanon, UAE, Sudan, Yemen, Libya, Kuwait, Palestine)
Dialect Groups	8 (Maghrebi, Levantine, Diaspora, Egyptian, Gulf, Iraqi, Sudanese, Other)
Average Quality Score	79.0/100
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-complete)	32,039	8	16	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 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.
• 🇲🇦 ArSyra Maghreb Arabic (Darija) Dataset — Addressing the critical underrepresentation of North African Arabic in NLP.

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.
• Machine Translation — Build translation systems between MSA and regional Arabic dialects.
• Token Classification — Named entity recognition and sequence labeling in dialectal Arabic.

Languages

Primary Language: Arabic (ar)

This dataset contains text in Modern Standard Arabic (MSA) and the following regional dialect groups: Maghrebi, Levantine, Diaspora, Egyptian, Gulf, Iraqi, Sudanese, Other. Country-level dialect codes: ar-TN, ar-SY, ar-EU, ar-EG, ar-SA, ar-MA, ar-DZ, ar-IQ, ar-JO, ar-LB, ar-AE, ar-SD, ar-YE, ar-LY, ar-KW, ar-PS.

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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	32,039

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	4,301	13.4%
conversation_pairs	2,748	8.6%
vocabulary	2,590	8.1%
slang	2,532	7.9%
taboo	1,958	6.1%
instruction_following	1,758	5.5%
freeform	1,741	5.4%
proverbs	1,557	4.9%
formality_transfer	1,464	4.6%
instructions	1,441	4.5%
greetings	1,426	4.5%
medical_dialect	1,352	4.2%
price	1,201	3.7%
tech_dialect	1,159	3.6%
paraphrase	1,068	3.3%
food_culture	993	3.1%
code_switching	942	2.9%
sentiment	755	2.4%
named_entities_local	753	2.4%
control	300	0.9%

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Dataset Creation

Curation Rationale

ArSyra Complete exists because the Arabic-speaking world's 400+ million people communicate primarily in regional dialects, yet the vast majority of Arabic NLP resources focus exclusively on Modern Standard Arabic (MSA). This dataset was created to provide the research and engineering community with authentic, high-quality dialectal Arabic data that reflects how people actually speak, write, and express themselves across the Arab world.

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 16 countries across the Arab world (Tunisia, Syria, EU, Egypt, Saudi Arabia, Morocco, Algeria, Iraq, Jordan, Lebanon, UAE, Sudan, Yemen, Libya, Kuwait, Palestine), 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

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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

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Additional Information

Use Cases

• Fine-tuning Arabic language models (GPT, BERT, LLaMA) on authentic dialectal data
• Building dialect-aware machine translation systems
• Training Arabic sentiment analysis and opinion mining models
• Developing culturally-aware Arabic chatbots and virtual assistants
• Dialectal Arabic speech recognition post-processing
• Academic research in Arabic sociolinguistics and computational linguistics

Get the Full Dataset

This repository contains a preview sample of 50 records out of 32,039 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	32,039 records — contact us for access
Suggested Price	$499
Contact	support@arsyra.com
Website	arsyra.com

What you get with the full dataset:

• All 32,039 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.

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Quick Start

from datasets import load_dataset

# Load the preview sample
dataset = load_dataset("ArSyra/arsyra-complete")
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 (32,039 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_complete_2026,
  title     = {ArSyra Complete — Multi-Dialect Arabic Dataset},
  author    = {{ArSyra Team}},
  year      = {2026},
  url       = {https://huggingface.co/datasets/ArSyra/arsyra-complete},
  publisher = {HuggingFace},
  license   = {CC-BY-NC-SA-4.0},
  note      = {Crowdsourced Arabic dialect dataset with 32,039 records from 16 countries}
}

Contributions

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

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