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

• 🎯 97.84% accuracy on a held-out test set of ~100K sentences
• 🌍 Every major Arabic dialect: Egyptian, Levantine, Gulf, Maghrebi, Iraqi, Sudanese, MSA
• 📊 Trained on nearly 1 million examples (997K) from 51 curated public Arabic datasets
• ⚡ Optimized for real-time inference via serverless API (Modal + FastAPI)
• 🧠 V2 Update: Enhanced with continued fine-tuning on 34K hard edge-cases to conquer implicit hate and subtle toxicity.

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Performance

Evaluated on a held-out test set of 99,759 sentences (stratified, zero data leakage):

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: ~798K
• Validation examples: ~100K
• Sources: 51 curated public Arabic hate-speech and abuse datasets
• Dialects: All major Arabic dialects + MSA

Hyperparameters

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:

Dialects

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Part of a Larger System

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

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 51 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