README.md

---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- text-classification
- translation
- token-classification
task_ids:
- language-modeling
- sentiment-classification
- multi-class-classification
- machine-translation
pretty_name: "ArSyra Complete — Multi-Dialect Arabic Dataset"
tags:
- arabic
- dialect
- nlp
- multi-dialect
- crowdsourced
- arabic-nlp
- dialectal-arabic
- spoken-arabic
- msa
- levantine
- egyptian
- gulf
- maghrebi
- iraqi
- arabic-corpus
- language-diversity
- fine-tuning
- llm-training
- low-resource
- middle-east
- north-africa
- parallel-corpus
- native-speaker
- jais
- allam
- arabic-bert
configs:
- config_name: default
  data_files:
  - split: train
    path: data/all.jsonl
dataset_info:
  features:
  - name: text
    dtype: string
  - name: category
    dtype: string
  - name: country
    dtype: string
  - name: dialect_group
    dtype: string
  - name: quality_score
    dtype: int32
  - name: msa_text
    dtype: string
  - name: context
    dtype: string
  - name: speaker_hash
    dtype: string
  splits:
  - name: train
    num_examples: 30358
---

# 🏆 ArSyra Complete — Multi-Dialect Arabic Dataset

> The most comprehensive crowdsourced Arabic dialect dataset available.

---

## Table of Contents

- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
  - [Category Breakdown](#category-breakdown)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact](#social-impact)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Use Cases](#use-cases)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

---

## Dataset Description

- **Homepage:** [https://arsyra.com](https://arsyra.com)
- **Repository:** [https://huggingface.co/datasets/ArSyra/arsyra-complete](https://huggingface.co/datasets/ArSyra/arsyra-complete)
- **Point of Contact:** support@arsyra.com
- **Full Dataset:** 30,358 examples
- **Preview Sample:** 50 examples (included)
- **Access:** Preview included · [Purchase full dataset](https://arsyra.com/datasets.html)

### 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** | 30,358 |
| **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** | 78.6/100 |
| **License** | CC-BY-NC-SA-4.0 |
| **Last Updated** | 2026-02-21 |

### How ArSyra Compares to Existing Arabic Datasets

| Dataset | Records | Dialects | Countries | Categories | Crowdsourced | MSA↔Dialect Pairs |
|---------|---------|----------|-----------|------------|--------------|-------------------|
| **ArSyra (arsyra-complete)** | **30,358** | **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**](https://huggingface.co/datasets/ArSyra/arsyra-chatbot) — Purpose-built training data for Arabic conversational AI systems.
- 🌐 [**ArSyra Translation — Arabic Dialect–MSA Parallel Corpus**](https://huggingface.co/datasets/ArSyra/arsyra-translation) — Parallel corpus bridging Modern Standard Arabic and regional dialects.
- 🇪🇬 [**ArSyra Egyptian Arabic (Masri) Dataset**](https://huggingface.co/datasets/ArSyra/arsyra-egyptian) — The most widely understood Arabic dialect — now as structured NLP data.
- 🇸🇾 [**ArSyra Levantine Arabic (Shami) Dataset**](https://huggingface.co/datasets/ArSyra/arsyra-levantine) — Authentic Shami dialect data from Syria, Lebanon, Jordan, and Palestine.
- 🇸🇦 [**ArSyra Gulf Arabic (Khaliji) Dataset**](https://huggingface.co/datasets/ArSyra/arsyra-gulf) — Gulf Arabic data from the Arabian Peninsula's rapidly growing digital population.
- 🇲🇦 [**ArSyra Maghreb Arabic (Darija) Dataset**](https://huggingface.co/datasets/ArSyra/arsyra-maghrebi) — Addressing the critical underrepresentation of North African Arabic in NLP.

Browse all datasets: [huggingface.co/ArSyra](https://huggingface.co/ArSyra) | [arsyra.com/datasets.html](https://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.

---

## Dataset Structure

### Data Instances

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

```json
{
  "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 | 30,358 |

*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 | 4,073 | 13.4% |
| conversation_pairs | 2,686 | 8.8% |
| vocabulary | 2,404 | 7.9% |
| slang | 2,392 | 7.9% |
| taboo | 1,827 | 6.0% |
| instruction_following | 1,720 | 5.7% |
| freeform | 1,720 | 5.7% |
| proverbs | 1,469 | 4.8% |
| formality_transfer | 1,439 | 4.7% |
| instructions | 1,400 | 4.6% |
| greetings | 1,335 | 4.4% |
| medical_dialect | 1,211 | 4.0% |
| price | 1,126 | 3.7% |
| tech_dialect | 1,070 | 3.5% |
| paraphrase | 996 | 3.3% |
| food_culture | 914 | 3.0% |
| code_switching | 873 | 2.9% |
| sentiment | 719 | 2.4% |
| named_entities_local | 684 | 2.3% |
| control | 300 | 1.0% |

---

## 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](https://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

---

## 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

- 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 30,358 total.
> Purchase the full dataset instantly at [arsyra.com/datasets.html](https://arsyra.com/datasets.html)

### Pricing

| | |
|---|---|
| **Preview (this repo)** | 50 sample records — free to download and evaluate |
| **Full Dataset** | 30,358 records — instant download after purchase |
| **Academic License** | From $29 — for research and non-commercial use |
| **Commercial License** | From $99 — for products, SaaS, and enterprise use |

### 🛒 [Buy Now →](https://arsyra.com/datasets.html)

**What you get with the full dataset:**

- All 30,358 quality-filtered records
- Per-category JSONL splits for easy loading
- Instant download as ZIP after payment
- Regular updates as our community grows
- Priority support for integration questions

**Questions?** Email [support@arsyra.com](mailto:support@arsyra.com)

---

### Quick Start

```python
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 (30,358 records), visit: https://arsyra.com/datasets.html
```

### 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 | Pricing |
|---------|----------|---------|
| CC-BY-NC-SA-4.0 | Academic research, non-commercial use | From $29 |
| Commercial License | Enterprise, products, SaaS applications | From $99 |

[Purchase a license →](https://arsyra.com/datasets.html) or email [support@arsyra.com](mailto:support@arsyra.com) for custom licensing.

### Citation Information

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

```bibtex
@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 30,358 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](https://arsyra.com).

---

*Dataset card generated by the ArSyra Publish Pipeline. Last updated: 2026-02-21.*