ArSyra/arsyra-levantine

🇸🇾 ArSyra Levantine Arabic (Shami) Dataset

Authentic Shami dialect data from Syria, Lebanon, Jordan, and Palestine.

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Dataset Summary

A curated collection of Levantine Arabic (Shami) data from Syria, Lebanon, Jordan, and Palestine. Levantine Arabic is spoken by over 30 million people and shares distinctive phonological and lexical features that set it apart from other dialect groups.

This dataset captures the natural variation within the Levantine dialect continuum — from the softer Lebanese register to the broader Syrian and Jordanian variants. All 17 linguistic categories are covered, making it suitable for training dialect-specific NLP models, speech recognition systems, and regional content understanding tools.

Statistic	Value
Total Records	5,715
Linguistic Categories	16
Countries Represented	1 (Syria)
Dialect Groups	1 (Levantine)
Average Quality Score	96.5/100
License	CC-BY-NC-SA-4.0
Last Updated	2026-02-19

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: Levantine. Country-level dialect codes: ar-SY.

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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": "SL-0105",
  "category": "slang",
  "subcategory": "street",
  "question_text": "ما هي الكلمة العامية لـ\"حرامي\" عندكم؟",
  "answer_text": "حرامي",
  "response_time_ms": 7852,
  "quality_score": 100,
  "country": "SY",
  "answered_at": "2026-02-18T14:27:26.517Z",
  "quality_grade": "A",
  "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	5,715

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	1,038	18.2%
slang	637	11.1%
vocabulary	542	9.5%
taboo	481	8.4%
conversation_pairs	396	6.9%
proverbs	342	6.0%
freeform	299	5.2%
instruction_following	297	5.2%
greetings	282	4.9%
formality_transfer	280	4.9%
instructions	250	4.4%
price	222	3.9%
sentiment	199	3.5%
code_switching	174	3.0%
paraphrase	174	3.0%
named_entities_local	102	1.8%

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Dataset Creation

Curation Rationale

Levantine Arabic spans a dialect continuum across four countries with significant internal variation. This dataset captures that diversity, enabling models to handle both the shared Levantine core and country-specific variations.

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 17 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 1 countries across the Arab world (Syria), 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

• Training Levantine Arabic language models
• Regional content analysis for Syria, Lebanon, Jordan
• Levantine dialect chatbot development
• Cross-dialect transfer learning experiments

Get the Full Dataset

This repository contains a preview sample of 50 records out of 5,715 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	5,715 records — contact us for access
Suggested Price	$149
Contact	support@arsyra.com
Website	arsyra.com

What you get with the full dataset:

• All 5,715 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-levantine")
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 (5,715 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_levantine_2026,
  title     = {ArSyra Levantine Arabic (Shami) Dataset},
  author    = {{ArSyra Team}},
  year      = {2026},
  url       = {https://huggingface.co/datasets/ArSyra/arsyra-levantine},
  publisher = {HuggingFace},
  license   = {CC-BY-NC-SA-4.0},
  note      = {Crowdsourced Arabic dialect dataset with 5,715 records from 1 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: 2026-02-19.