README.md

---
language:
  - ar
license: apache-2.0
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
  - hate-speech-detection
tags:
  - arabic
  - toxicity
  - hate-speech
  - cyberbullying
  - content-moderation
  - offensive-language
  - profanity
  - multi-dialect
  - arabic-nlp
pretty_name: "Hayā — Arabic Toxic Content Dataset"
size_categories:
  - 100K<n<1M
source_datasets:
  - original
  - L-HSAB
  - T-HSAB
  - OSACT4
  - OSACT5
  - MPOLD
  - Let-Mi
  - ArMIS
  - ArMI
  - ADHAR
  - LREC
dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype: int64
    - name: source
      dtype: string
    - name: dialect
      dtype: string
  splits:
    - name: train
      num_examples: 798071
    - name: validation
      num_examples: 99759
    - name: test
      num_examples: 99759
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.parquet
      - split: validation
        path: val.parquet
      - split: test
        path: test.parquet
---

# Hayā (حياء) — Arabic Toxic Content Dataset 🛡️

[![Model on HF](https://img.shields.io/badge/🤗_Model-Hayā-blue)](https://huggingface.co/youssefreda9/HAYAA)
[![GitHub](https://img.shields.io/badge/GitHub-HAYAA-black?logo=github)](https://github.com/youssefreda10/HAYAA)

## Dataset Description

**Hayā** is a large-scale, curated Arabic binary toxicity dataset built for training and evaluating Arabic content-moderation models. It was assembled by reviewing, collecting, standardizing, and merging **51 public Arabic hate-speech and abuse datasets** into a single clean corpus of **nearly 1 million examples**.

This is the dataset used to train the [Hayā model](https://huggingface.co/youssefreda9/HAYAA), a fine-tuned **UBC-NLP/MARBERTv2** classifier that achieved **97.84% accuracy** on the held-out test set.

### Key Features

- **~997K examples** of Arabic text (comments, posts, tweets) labeled as **Safe (0)** or **Toxic (1)**
- **Every major Arabic dialect**: Egyptian, Levantine, Gulf, Maghrebi, Iraqi, Sudanese, and MSA
- **Multi-platform**: Twitter, Facebook, YouTube, news comments, and more
- **Deduplicated**: Zero duplicate texts across the entire corpus
- **Conflict-resolved**: Overlapping texts from different sources resolved via majority vote
- **Leak-free splits**: Stratified 80/10/10 split with verified zero overlap between train, validation, and test

---

## Dataset Summary

| Property | Value |
|----------|-------|
| **Language** | Arabic (all major dialects) |
| **Task** | Binary text classification |
| **Labels** | `0` = Safe, `1` = Toxic |
| **Total Size** | 997,589 examples |
| **Train** | 798,071 (80%) |
| **Validation** | 99,759 (10%) |
| **Test** | 99,759 (10%) |
| **Sources** | 51 public datasets merged & standardized |
| **Format** | Parquet |

---

## What "Toxic" Covers

The **Toxic** label encompasses:

- 🤬 **Profanity & offensive language**
- 🔥 **Hate speech**
- 😡 **Insults, harassment & cyberbullying**
- 🚫 **Racism & religious hate**
- ⚠️ **Sexism & sexually explicit language**
- 🔤 **Morphologically complex and context-dependent expressions**
- 🕵️ **Intentional typos / obfuscation attempts**

---

## Dialects Covered

| Dialect Group | Varieties |
|---------------|-----------|
| **Egyptian** | Egyptian Arabic |
| **Levantine** | Syrian, Lebanese, Jordanian, Palestinian |
| **Gulf** | Saudi, Emirati, Kuwaiti, Bahraini, Omani, Qatari |
| **Maghrebi** | Moroccan, Algerian, Tunisian, Libyan |
| **Other** | Iraqi, Sudanese, Yemeni |
| **MSA** | Modern Standard Arabic |

---

## Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `text` | `string` | The Arabic text (comment, tweet, or post) |
| `label` | `int` | `0` = Safe, `1` = Toxic |
| `source` | `string` | Origin dataset(s), pipe-separated if from multiple sources |
| `dialect` | `string` | Dialect(s), pipe-separated if from multiple sources |

---

## Data Splits

| Split | Examples | Purpose |
|-------|----------|---------|
| `train` | ~798K | Model training |
| `validation` | ~100K | Hyperparameter tuning & early stopping |
| `test` | ~100K | Final held-out evaluation |

All splits are **stratified by label** and verified for **zero text overlap** (no data leakage).

---

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("youssefreda9/HAYAA")

# Access splits
train = dataset["train"]
val = dataset["validation"]
test = dataset["test"]

# Example
print(train[0])
# {'text': '...', 'label': 0, 'source': 'OSACT4', 'dialect': 'MSA'}
```

---

## Curation Process

### 1. Collection
Reviewed and collected **100+ public Arabic hate-speech and abuse datasets** from published research, shared tasks, and public repositories.

### 2. Standardization
Every source used a different labeling scheme (3-class, 5-class, multi-label, etc.). All were standardized into a single **binary** scheme:
- **Safe (0)**: Non-toxic, normal text
- **Toxic (1)**: Offensive, hateful, abusive, or harmful

### 3. Cleaning & Quality Control
- **Removed problematic datasets**: Sources with noisy labels, wrong task definitions, or exact duplicates of other sources were excluded
- **Fixed known mislabels**: Manual keyword-based correction of known systematic errors (e.g., SaudiCodeMixing safe-labeled profanity)
- **Filtered noise**: Removed long, machine-translated safe texts that didn't match real Arabic usage patterns

### 4. Conflict Resolution
Texts appearing in multiple sources with conflicting labels were resolved via **majority vote**.

### 5. Deduplication
Full text-level deduplication with dialect and source metadata merged (pipe-separated) for multi-source texts.

### 6. Splitting
**Stratified 80/10/10 split** (train/val/test) with seed=42, verified for zero leakage across all split pairs.

---

## Source Datasets

This corpus merges **51 standardized public Arabic datasets**, including:

| Dataset | Dialect | Size | Platform |
|---------|---------|------|----------|
| L-HSAB | Levantine | ~5.8K | Twitter |
| T-HSAB | Tunisian | ~6K | Facebook/YouTube |
| OSACT4 | MSA + mixed | ~10K | Twitter |
| OSACT5 | MSA + mixed | ~12.7K | Twitter |
| MPOLD | Multi-dialect | ~4K | Multi-platform |
| Let-Mi | Levantine | ~6.6K | Twitter |
| ArMI | MSA, Egyptian, Gulf, Levantine | ~9.8K | Twitter |
| ADHAR | Multi-dialect | ~4.3K | Twitter |
| LREC | MSA + dialects | ~15.9K | Twitter |
| *... and 42 more* | | | |

See the full [Dataset Registry](https://github.com/youssefreda10/HAYAA/tree/main/data) for detailed per-source documentation.

---

## Model Trained on This Dataset

The [Hayā model](https://huggingface.co/youssefreda9/HAYAA) — fine-tuned **UBC-NLP/MARBERTv2** — achieves:

| Metric | Score |
|--------|-------|
| Accuracy | **97.84%** |
| F1 (Toxic class) | **94.12%** |
| F1 (Safe class) | 98.45% |

> **Note:** Manual error analysis showed the model frequently *outperformed the original human annotations* — many counted "errors" were actually mislabels in the source data.

---

## Intended Use

- ✅ Training and evaluating Arabic toxicity / hate-speech classifiers
- ✅ Benchmarking Arabic NLP models on content moderation
- ✅ Research on multi-dialect Arabic text classification
- ✅ Building content-moderation pipelines for Arabic platforms

## Out-of-Scope Use

- ❌ Generating toxic or hateful content
- ❌ Surveillance or profiling of individuals
- ❌ Deployed decisions without human review

---

## Limitations & Biases

- **Platform bias**: The majority of the data comes from Twitter; other platforms (forums, messaging apps) are underrepresented.
- **Annotation noise**: Despite extensive cleaning, some label noise from original sources may remain.
- **Dialect imbalance**: Some dialects (e.g., Egyptian, MSA) are better represented than others (e.g., Sudanese, Yemeni).
- **Temporal bias**: Most data reflects Arabic social media discourse from 2018–2024; slang and evasion tactics evolve constantly.

---

## Citation

If you use this dataset, please cite:

```bibtex
@misc{hayaa2026,
  title={Hayā: A Large-Scale Multi-Dialect Arabic Toxicity Dataset},
  author={Youssef Reda},
  year={2026},
  url={https://huggingface.co/datasets/youssefreda9/HAYAA},
}
```

---

## License

This dataset is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).

---

## Contact

- **GitHub**: [youssefreda10/HAYAA](https://github.com/youssefreda10/HAYAA)
- **Model**: [youssefreda9/HAYAA](https://huggingface.co/youssefreda9/HAYAA)