This dataset contains quality-scored Arabic dialect data collected from verified native speakers. This is a preview sample (50 records). The full dataset is available for purchase at arsyra.com/datasets.
By requesting access you agree to: - Use the data only for research or evaluation purposes - Not redistribute the data - Contact support@arsyra.com for commercial licensing

YAML Metadata Warning: The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

🤖 ArSyra Chatbot — Conversational Arabic Training Data

Purpose-built training data for Arabic conversational AI systems.

Dataset Summary

A curated subset of conversational Arabic data designed for fine-tuning chatbots, virtual assistants, and dialogue systems. Focuses on dialogue-oriented categories: natural conversation pairs, greeting and farewell patterns, instruction-following examples, freeform open-ended responses, and formality transfer between casual and formal registers.

All data is sourced from native speakers, producing naturally flowing dialectal Arabic. Quality-filtered to ensure coherent, contextually appropriate responses that reflect authentic Arabic communication patterns across multiple dialect groups.

Statistic	Value
Total Records	29,015
Linguistic Categories	5
Countries Represented	16 (Tunisia, Syria, EU, Egypt, Saudi Arabia, Morocco, Iraq, Sudan, Algeria, Jordan, Lebanon, UAE, Yemen, Libya, Kuwait, Palestine)
Dialect Groups	7 (Maghrebi, Levantine, Egyptian, Gulf, Iraqi, Sudanese, Other)
Average Quality Score	94.4/100
License	CC-BY-NC-SA-4.0
Last Updated	2026-02-23

How ArSyra Compares to Existing Arabic Datasets

Dataset	Records	Dialects	Countries	Categories	Verified	MSA↔Dialect Pairs
ArSyra (arsyra-chatbot)	29,015	7	16	5	✅	✅
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 multi-dialect Arabic dataset available.
🌐 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.
Conversational AI — Develop Arabic chatbots and dialogue systems with natural dialectal responses.

Languages

Primary Language: Arabic (ar)

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

{
  "question_code": "CP-0035",
  "category": "conversation_pairs",
  "subcategory": "customer_service",
  "question_text": "كيف تحجز موعد عند الدكتور بلهجتك؟",
  "answer_text": "عسلامة نحب نعمل موعد",
  "response_time_ms": 80430,
  "quality_score": 100,
  "country": "TN",
  "answered_at": "2026-02-17T20:58:49.768Z",
  "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	29,015

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
conversation_pairs	22,541	77.7%
freeform	1,838	6.3%
instruction_following	1,744	6.0%
formality_transfer	1,619	5.6%
greetings	1,273	4.4%

Dataset Creation

Curation Rationale

Most Arabic chatbot training data relies on MSA or translated English conversations, resulting in robotic, unnatural dialogue. ArSyra Chatbot captures how real Arabic speakers converse — with natural dialect mixing, culturally appropriate greetings, and authentic expression patterns. This enables building conversational AI that feels genuinely Arabic rather than a translated English bot.

Source Data

Initial Data Collection and Normalization

Data was collected through the ArSyra platform (arsyra.com), a multi-dialect Arabic data collection system where verified native Arabic speakers respond to structured linguistic prompts about their dialect. The platform:

Verifies speakers through phone number verification (region-specific) and language verification questions
Presents structured prompts across multiple linguistic categories: dialect translations, conversation pairs, proverbs, slang, code-switching, sentiment expressions, instruction following, formality registers, and more
Quality-scores all data through multi-layer validation to ensure linguistic accuracy and dialect authenticity
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, Iraq, Sudan, Algeria, Jordan, Lebanon, UAE, 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:

Platform access bias — Contributors need internet access and a smartphone, potentially underrepresenting older, rural, or lower-income speakers
Country representation — Some countries may be overrepresented depending on recruitment channels
Urban bias — Online populations tend to be more urban, potentially underrepresenting rural dialect variants
Literacy bias — Written responses may differ from purely spoken dialect, as speakers may unconsciously shift toward MSA
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 conversational AI models for Arabic-speaking users
Training Arabic chatbots that handle multiple dialect groups
Building instruction-following systems in dialectal Arabic
Developing formality-aware response generation (formal/informal switching)

Get the Full Dataset

This repository contains a preview sample of 50 records out of 29,015 total. Purchase the full dataset instantly at arsyra.com/datasets.html

Pricing


Preview (this repo)	50 sample records — free to download and evaluate
Full Dataset	29,015 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 →

What you get with the full dataset:

All 29,015 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

Quick Start

from datasets import load_dataset

# Load the preview sample
dataset = load_dataset("ArSyra/arsyra-chatbot")
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 (29,015 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 → or email support@arsyra.com for custom licensing.

Citation Information

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

@dataset{arsyra_arsyra_chatbot_2026,
  title     = {ArSyra Chatbot — Conversational Arabic Training Data},
  author    = {{ArSyra Team}},
  year      = {2026},
  url       = {https://huggingface.co/datasets/ArSyra/arsyra-chatbot},
  publisher = {HuggingFace},
  license   = {CC-BY-NC-SA-4.0},
  note      = {Multi-dialect Arabic dataset with 29,015 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.

Dataset card generated by the ArSyra Publish Pipeline. Last updated: 2026-02-23.

Downloads last month: 27

50 examples (included)

Access:

Purchase full dataset