youssefreda9/HAYAA

Hayā (حياء) — Arabic Toxic Content Classifier 🛡️

Model Description

Hayā is a fine-tuned UBC-NLP/MARBERTv2 model for binary Arabic toxicity classification (Safe / Toxic). It is designed to detect offensive language, hate speech, cyberbullying, profanity, and other forms of toxic content across all major Arabic dialects.

Hayā is the core classifier in a larger defense-in-depth content moderation system that includes rule-based layers for explicit profanity and a Chrome extension that blurs toxic content in real time.

Key Highlights

• 🎯 96.52% accuracy on a held-out test set of ~96K sentences
• 🌍 Every major Arabic dialect: Egyptian, Levantine, Gulf, Maghrebi, Iraqi, Sudanese, MSA
• 📊 Trained on ~770K examples from 44 curated public Arabic datasets
• ⚡ Optimized for real-time inference via serverless API (Modal + FastAPI)

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Performance

Evaluated on a held-out test set of ~96,000 sentences (stratified, zero data leakage):

Metric	Score
Accuracy	96.52%
F1 (Toxic)	91.01%
F1 (Safe)	97.28%

Note on the numbers: Manual error analysis showed the model frequently outperformed the original human annotations — many counted "errors" were actually mislabels in the source datasets. Real-world performance on correctly-labeled data is therefore higher than the raw scores suggest.

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Intended Use

Primary Use Cases

• ✅ Classifying Arabic text as Safe or Toxic
• ✅ Content moderation for Arabic social media, forums, and comment sections
• ✅ Child safety and parental control applications
• ✅ Research on Arabic hate speech and toxicity detection

Out-of-Scope Use

• ❌ Generating toxic or hateful content
• ❌ Surveillance or profiling of individuals
• ❌ Final automated decisions without human review
• ❌ Non-Arabic text classification

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Usage

Quick Start with Transformers Pipeline

from transformers import pipeline

classifier = pipeline("text-classification", model="youssefreda9/HAYAA", top_k=None)

results = classifier("أنت إنسان رائع ومحترم")
print(results)
# [[{'label': 'Safe', 'score': 0.99}, {'label': 'Toxic', 'score': 0.01}]]

results = classifier("يا حمار أنت")
print(results)
# [[{'label': 'Toxic', 'score': 0.98}, {'label': 'Safe', 'score': 0.02}]]

Direct Model Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("youssefreda9/HAYAA")
model = AutoModelForSequenceClassification.from_pretrained("youssefreda9/HAYAA")

text = "كلامك جميل جدا"
inputs = tokenizer(text, return_tensors="pt", max_length=128, truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=-1)
    pred = torch.argmax(probs, dim=-1).item()

labels = {0: "Safe", 1: "Toxic"}
print(f"Prediction: {labels[pred]} ({probs[0][pred]:.2%})")

Batch Inference

from transformers import pipeline

classifier = pipeline("text-classification", model="youssefreda9/HAYAA")

texts = [
    "صباح الخير يا أصدقاء",
    "أنت واطي ومحترمش حد",
    "الجو جميل النهارده",
]

results = classifier(texts)
for text, result in zip(texts, results):
    print(f"{result['label']} ({result['score']:.2%}): {text}")

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Training Details

Base Model

• Model: UBC-NLP/MARBERTv2
• Architecture: BERT-base (Arabic-specific pre-training)
• Task: Binary sequence classification (Safe / Toxic)

Training Data

• Dataset: youssefreda9/HAYAA
• Training examples: ~770K
• Validation examples: ~96K
• Sources: 44 curated public Arabic hate-speech and abuse datasets
• Dialects: All major Arabic dialects + MSA

Hyperparameters

Parameter	Value
Max sequence length	128
Epochs	5 (with early stopping, patience=2)
Batch size	16 (effective: 32 with gradient accumulation)
Learning rate	2e-5
Warmup ratio	0.1
Weight decay	0.01
FP16	✅
Loss	Weighted cross-entropy (class imbalance)
Best model metric	F1 (weighted)
Optimizer	AdamW
Seed	42

Class Weighting

The training set is imbalanced (more Safe than Toxic). A weighted cross-entropy loss was used, with the Toxic class weight dynamically computed as safe_count / toxic_count to ensure the model doesn't under-predict toxicity.

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What It Detects

The Toxic label covers:

Category	Examples
Profanity	Explicit swear words across all dialects
Hate speech	Incitement against groups based on religion, ethnicity, nationality
Cyberbullying	Personal insults, harassment, directed attacks
Racism	Racial slurs and discriminatory language
Sexism	Misogynistic and sexually explicit language
Religious hate	Blasphemy, sectarian attacks
Obfuscated toxicity	Intentional typos, spaced letters, homoglyphs

Dialects

Dialect	Coverage
Egyptian	✅
Levantine (Syrian, Lebanese, Jordanian, Palestinian)	✅
Gulf (Saudi, Emirati, Kuwaiti, Bahraini, Omani, Qatari)	✅
Maghrebi (Moroccan, Algerian, Tunisian, Libyan)	✅
Iraqi	✅
Sudanese	✅
Modern Standard Arabic (MSA)	✅

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Part of a Larger System

This model is one layer in the Hayā defense-in-depth pipeline:

Layer	Function
L0 — Sanitize	Unicode normalization, homoglyph folding, emoji analysis
L1 — Dictionary	Context-aware instant matching (100% precision)
L1.5 — De-obfuscation	Resolves masked/spaced evasion attempts
L2 — This Model	Deep-learning classification for implicit toxicity

The full pipeline ships as a Chrome Extension (Manifest V3) with PIN-protected parental controls. See the GitHub repository for the complete system.

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Limitations & Biases

• Platform bias: Training data is predominantly from Twitter; performance may vary on other platforms (forums, messaging apps, gaming chat).
• Annotation noise: Despite extensive cleaning, some label noise from the 44 original source datasets may persist.
• Dialect imbalance: Egyptian and MSA are better represented than Sudanese or Yemeni dialects.
• Max length: Sequences longer than 128 tokens are truncated, which may affect classification of very long texts.
• Evolving language: Arabic internet slang and evasion tactics evolve constantly; periodic retraining is recommended.
• Context limitations: The model classifies individual texts in isolation; conversational context is not considered.

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Ethical Considerations

• This model is designed to protect people — especially children — from online abuse.
• It should be used as a tool to assist human moderators, not as a sole decision-maker.
• False positives (Safe text flagged as Toxic) can suppress legitimate speech; false negatives (Toxic text missed) can leave users exposed to harm.
• The model was trained on publicly available datasets with appropriate licenses.

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Citation

@misc{hayaa2026,
  title={Hayā: Fine-tuned MARBERTv2 for Multi-Dialect Arabic Toxicity Classification},
  author={Youssef Reda},
  year={2026},
  url={https://huggingface.co/youssefreda9/HAYAA},
}

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License

This model is released under the Apache 2.0 License.

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Links

• 🤗 Dataset: youssefreda9/HAYAA
• 💻 GitHub: youssefreda10/HAYAA
• 🧠 Base Model: UBC-NLP/MARBERTv2