fr3on/arabic-dialect-corpus

🇪🇬🇸🇦 Arabic Dialect Corpus (Egyptian & Saudi)

Dataset Description

This dataset contains 150K+ natural, informal Arabic text samples scraped from high-engagement YouTube discussions. It specifically targets Egyptian (EG) and Saudi (SA) dialects, filling a critical gap in resources for training LLMs on colloquial Arabic (Ammiya) rather than just Modern Standard Arabic (MSA).

Languages

• Primary Dialects:
  • Egyptian Arabic (EG) - Cairene and regional Egyptian variants
  • Saudi Arabic (SA) - Najdi and Hijazi Gulf variants
  • General Arabic (AR) - Mixed or pan-dialectal colloquial Arabic
• Script: Arabic script with colloquial spelling conventions
• Type: Informal, conversational text

Dataset Summary

Modern Arabic exists on a spectrum from formal Modern Standard Arabic (MSA) to highly localized dialects. While MSA dominates written content, colloquial dialects (Ammiya) dominate everyday communication, social media, and informal contexts. This dataset provides:

• Authentic dialect data: Real conversations from native speakers
• Regional coverage: Two major Arabic dialect groups (Egyptian and Gulf)
• Simple labeling: Clean 3-field schema (text, label, score)
• Quality filtering: Community-validated content via engagement metrics
• Training-ready format: JSONL optimized for streaming workflows

Dataset Structure

Data Format

Each entry contains:

{
  "text": "يا جدعان الفيديو ده تحفة بجد بس محتاج شوية تظبيط في الصوت",
  "label": "EG",
  "score": 45
}

Data Fields

Field	Type	Description
text	string	Cleaned Arabic comment text (colloquial dialect)
label	string	Dialect label: "EG" (Egyptian), "SA" (Saudi), or "AR" (General Arabic)
score	int64	Community engagement score (like count)

Dataset Statistics

Overview

• Total Entries: ~150,000+
• Source Platform: YouTube
• Content Type: User comments and discussions
• Dialect Coverage: Egyptian and Saudi Arabian variants
• Average Text Length: 15-80 words per entry
• Quality Range: Filtered for minimum engagement and coherence

Label Distribution

Label	Description	Percentage
EG	Egyptian Arabic (Cairene and regional variants)	~60%
SA	Saudi Arabic (Najdi, Hijazi variants)	~35%
AR	General colloquial Arabic (mixed or unidentified)	~5%

Content Distribution

The dataset draws from multiple video categories to ensure diverse vocabulary and contexts:

• Talk Shows & Podcasts: 35%
• Technology Reviews: 25%
• Entertainment & Comedy: 20%
• Social Commentary: 15%
• Other: 5%

Dialect Information

Label Classification

The label field indicates the dialect type:

• EG: Egyptian Arabic markers detected (e.g., إزيك, يعني, عايز, كده, بتاع)
• SA: Saudi/Gulf Arabic markers detected (e.g., وش, كيف, عندك, ياخي, حق)
• AR: Mixed or unclear dialectal markers, general colloquial Arabic

Note: Classification is automatic and based on dialectal keywords, video metadata, and linguistic patterns. Some entries may contain mixed dialects due to code-switching or regional overlap.

Egyptian Arabic (EG)

Egyptian Arabic is the most widely understood Arabic dialect due to Egypt's large population (~100M speakers) and cultural influence through media.

Characteristics:

• Simplified verb conjugations (no dual forms in verbs)
• Distinct pronunciation (ج as "g", ق as glottal stop)
• Unique vocabulary (e.g., إزيك for "how are you")
• Heavy use of particles like يعني, بقى, كده

Saudi Arabic (SA)

Includes Najdi (Central) and Hijazi (Western) variants spoken by ~30M people.

Characteristics:

• Preservation of classical pronunciation (ج as "j", ق as "q")
• Gulf-specific vocabulary and expressions
• Different question words (وش for "what")
• Distinct verb patterns and negation structures

Use Cases

✅ Recommended Use Cases

• Dialect Adaptation: Fine-tune base LLMs (Llama, Mistral, Qwen) for Egyptian/Saudi understanding
• Continued Pre-training: Augment model knowledge with colloquial Arabic
• Sentiment Analysis: Build classifiers for social monitoring in Egypt and KSA
• Dialect Identification: Train discriminators to distinguish regional variants (EG vs SA vs AR)
• Code-Switching Research: Study Arabic-English language mixing patterns
• Cultural NLP: Analyze slang, humor, and regional expressions
• Multi-Dialect Models: Train models that understand multiple Arabic varieties

⚠️ Limitations

• Platform Bias: YouTube demographics skew younger and more tech-savvy
• Topic Bias: Over-representation of entertainment and tech content
• Register: Primarily informal; limited formal or professional language
• Dialect Mixing: Contains code-switching (Arabic-English) and occasional MSA
• Size: Moderate scale (150K) - suitable for fine-tuning but not pre-training from scratch
• Temporal: Reflects 2023-2024 language usage and cultural references

Loading the Dataset

Using Hugging Face Datasets

from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset("fr3on/arabic-dialect-corpus")

# Access training data
print(f"Dataset size: {len(dataset['train'])} examples")
print(dataset['train'][0])

# Example output:
# {
#   'text': 'يا جدعان الفيديو ده تحفة بجد',
#   'label': 'EG',
#   'score': 45
# }

# Iterate through examples
for example in dataset['train']:
    print(example['text'])
    print(f"Dialect: {example['label']}")
    print(f"Quality score: {example['score']}")

Streaming Mode (for large-scale training)

from datasets import load_dataset

# Enable streaming for memory-efficient loading
dataset = load_dataset(
    "fr3on/arabic-dialect-corpus",
    split="train",
    streaming=True
)

# Process in batches
for batch in dataset.take(1000):
    # Your training code here
    pass

Filter by Dialect

# Load only Egyptian Arabic samples
dataset = load_dataset("fr3on/arabic-dialect-corpus")

egyptian_data = dataset['train'].filter(
    lambda x: x['label'] == 'EG'
)

print(f"Egyptian subset: {len(egyptian_data)} examples")

# Load only Saudi Arabic samples
saudi_data = dataset['train'].filter(
    lambda x: x['label'] == 'SA'
)

print(f"Saudi subset: {len(saudi_data)} examples")

# General Arabic only
general_data = dataset['train'].filter(
    lambda x: x['label'] == 'AR'
)

print(f"General Arabic subset: {len(general_data)} examples")

Filter by Quality Score

# Load only high-engagement content
dataset = load_dataset("fr3on/arabic-dialect-corpus")

high_quality = dataset['train'].filter(
    lambda x: x['score'] >= 50
)

print(f"High-quality subset: {len(high_quality)} examples")

Training Examples

Continued Language Model Pre-training

from datasets import load_dataset
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    TrainingArguments,
    Trainer,
    DataCollatorForLanguageModeling
)

# Load dataset
dataset = load_dataset("fr3on/arabic-dialect-corpus")

# Load base model (e.g., Llama 3)
model_name = "meta-llama/Llama-3-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Tokenize the data
def tokenize_function(examples):
    return tokenizer(
        examples['text'],
        truncation=True,
        max_length=512,
        padding=False
    )

tokenized_dataset = dataset.map(
    tokenize_function,
    batched=True,
    remove_columns=['text', 'label', 'score']
)

# Data collator for CLM
data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer,
    mlm=False  # CLM, not MLM
)

# Training arguments
training_args = TrainingArguments(
    output_dir="./arabic-dialect-clm",
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=4,
    save_steps=10_000,
    save_total_limit=2,
    learning_rate=2e-5,
    warmup_steps=500,
    logging_steps=100,
    fp16=True,
)

# Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=tokenized_dataset['train'],
)

# Train
trainer.train()

Using with Axolotl

Create a config file dialect-finetune.yml:

base_model: meta-llama/Llama-3-8B
model_type: LlamaForCausalLM

# Dataset configuration
datasets:
  - path: fr3on/arabic-dialect-corpus
    type: completion
    field: text

# Training parameters
sequence_len: 512
num_epochs: 3
micro_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 0.00002

# Output
output_dir: ./outputs/arabic-dialect

# Optimization
fp16: true
flash_attention: true

Then run:

axolotl train dialect-finetune.yml

Dialect-Aware Sentiment Analysis

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load dataset
dataset = load_dataset("fr3on/arabic-dialect-corpus")

# Add sentiment labels (you would need to label these)
# For demonstration, we'll filter by score as proxy
def add_sentiment_label(example):
    score = example['score']
    if score >= 100:
        example['label'] = 2  # Positive
    elif score >= 20:
        example['label'] = 1  # Neutral
    else:
        example['label'] = 0  # Negative
    return example

labeled_dataset = dataset['train'].map(add_sentiment_label)

# Train sentiment classifier
model = AutoModelForSequenceClassification.from_pretrained(
    "CAMeL-Lab/bert-base-arabic-camelbert-msa",
    num_labels=3
)

Country-Specific Model Training

from datasets import load_dataset

dataset = load_dataset("fr3on/arabic-dialect-corpus")

# Train separate models for each dialect region
dialects = ['EG', 'SA']

for dialect in dialects:
    # Filter by dialect label
    dialect_data = dataset['train'].filter(
        lambda x: x['label'] == dialect
    )
    
    dialect_name = {'EG': 'Egyptian', 'SA': 'Saudi'}.get(dialect)
    print(f"Training {dialect_name} model with {len(dialect_data)} examples")
    
    # Your training code here
    # model = train_model(dialect_data)
    # model.save_pretrained(f"./models/arabic-{dialect.lower()}")

# Or train a dialect classifier
def add_dialect_label(example):
    label_map = {'EG': 0, 'SA': 1, 'AR': 2}
    example['label_id'] = label_map[example['label']]
    return example

classifier_data = dataset['train'].map(add_dialect_label)
# Train dialect identification model

Comparative Dialect Analysis

from datasets import load_dataset
from collections import Counter

dataset = load_dataset("fr3on/arabic-dialect-corpus")

# Analyze vocabulary differences
def get_top_words(label, n=100):
    dialect_data = dataset['train'].filter(
        lambda x: x['label'] == label
    )
    
    all_words = []
    for example in dialect_data:
        words = example['text'].split()
        all_words.extend(words)
    
    return Counter(all_words).most_common(n)

# Compare Egyptian vs Saudi vocabulary
egypt_words = get_top_words('EG')
saudi_words = get_top_words('SA')

print("Top Egyptian words:", egypt_words[:10])
print("Top Saudi words:", saudi_words[:10])

Data Collection & Processing

Source

• Platform: YouTube public comments
• Selection Criteria: Videos with high engagement (>10K views)
• Categories: Talk shows, tech reviews, podcasts, entertainment
• Date Range: 2023-2024

Processing Pipeline

Our rigorous "Data Lab" pipeline ensures high quality:

1. Ingestion

   • API-based scraping of comment threads
   • Focus on high-traffic, organically popular videos
   • Collected ~300K raw comments

2. Normalization

   • Removed emojis, hashtags, and URLs
   • Stripped Tatweel/Kashida (مـــصـــر → مصر)
   • Collapsed repeated whitespace and newlines
   • Normalized Arabic punctuation

3. Filtering

   • Length filter: Removed comments with <3 words (spam/noise)
   • Language detection: Confirmed Arabic script majority
   • Deduplication: Hash-based removal of exact duplicates
   • Quality threshold: Minimum engagement score (like count ≥5)
   • Bot detection: Pattern-based removal of spam accounts
   • Dialect classification: Automatic labeling based on dialectal markers and video metadata

4. Quality Validation

   • Manual spot-checking of random samples (n=1000)
   • Automated profanity and toxic content filtering
   • Dialect verification for regional authenticity

5. Export

   • JSONL format for streaming compatibility
   • Metadata preservation for filtering/analysis

Data Quality Metrics

• ✅ Deduplication Rate: ~45% duplicates removed
• ✅ Bot Removal: ~12% spam accounts filtered
• ✅ Quality Score Range: 5-5000+ likes
• ✅ Manual Validation Accuracy: 94% dialect correctness
• ✅ Text Cleanliness: <1% non-Arabic characters

Considerations for Using the Data

Dialectal Arabic Characteristics

Colloquial Arabic differs fundamentally from MSA:

• Phonology: Different pronunciation rules (e.g., ج, ق sounds vary)
• Morphology: Simplified verb conjugations and case systems
• Lexicon: Region-specific vocabulary and loanwords
• Syntax: More flexible word order and dropped pronouns
• Orthography: Inconsistent spelling conventions

Recommended Training Approaches

1. Fine-tune multilingual Arabic models (e.g., AraGPT2, CAMeL-BERT) rather than training from scratch
2. Combine with MSA data to maintain formal language understanding
3. Use quality filtering to focus on high-engagement content
4. Consider domain adaptation if targeting specific use cases (e.g., tech, entertainment)
5. Augment with other dialect datasets for broader coverage

Code-Switching Handling

This dataset contains natural Arabic-English code-switching (e.g., "يعني basically كده"). If training a monolingual Arabic model, consider:

• Filtering or replacing English words
• Using bilingual tokenizers
• Training on code-switched data intentionally

Ethical Considerations

• Public Data: All content sourced from publicly accessible YouTube comments
• Privacy: No personal information (names, emails, addresses) included
• Anonymization: Author usernames removed during processing
• Bias Awareness: Dataset reflects online youth culture and may not represent all demographics
• Cultural Sensitivity: Content filtered for extreme hate speech but may contain strong opinions
• Intended Use: Research and model training only; not for surveillance or profiling

Citation

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

@dataset{arabic_dialect_corpus,
  title={Arabic Dialect Corpus (Egyptian & Saudi)},
  author={fr3on},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/fr3on/arabic-dialect-corpus},
  note={Natural colloquial Arabic from YouTube discussions}
}

Contributing

We welcome contributions to expand this corpus! You can help by:

Data Contributions

• Submit PRs with data from other Arabic dialects (Levantine, Iraqi, Moroccan)
• Share preprocessing scripts for other platforms (Twitter, forums)
• Provide domain-specific corpora (medical, legal, technical Arabic)

Quality Improvements

• Report mislabeled or low-quality examples
• Suggest improved filtering criteria
• Contribute manual dialect annotations

How to Contribute

1. Fork the repository or dataset
2. Process your data following the existing JSONL schema:

   {
     "text": "your_dialect_text",
     "label": "EG|SA|AR",
     "score": 0
   }
   

3. Document your data source and processing steps
4. Submit a pull request with clear description

Acknowledgments

• Community: YouTube creators and commenters for organic content
• Tools: Hugging Face Datasets, Python ecosystem
• Inspiration: CAMeL Lab, AraOpus, and other Arabic NLP initiatives

Version History

• v1.1.0 (2026-01-06): Expanded dataset

  • 350K+ entries

• v1.0.0 (2026-01-05): Initial release

  • 150K+ entries
  • Egyptian and Saudi dialects

License

This dataset is released under the MIT License. You are free to:

• ✅ Use for commercial and non-commercial purposes
• ✅ Modify and distribute
• ✅ Train models and publish results
• ✅ Sublicense

Attribution: Please cite this dataset in publications and model cards.

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Contact & Support

• Maintainer: fr3on
• Issues: Dataset Discussions
• Community: Join us in the dataset community tab for questions and feedback

Dataset Size: 150K+ examples | Format: JSONL | License: MIT | Labels: EG (Egyptian), SA (Saudi), AR (General)