---
language:
  - ary
  - ar
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: text-classification
base_model: SI2M-Lab/DarijaBERT
tags:
  - toxicity-detection
  - content-moderation
  - offensive-language
  - moroccan-darija
  - darija
  - low-resource-languages
  - bert
datasets:
  - OMCD_TypicaAI_Mix
metrics:
  - accuracy
  - f1
model-index:
  - name: DarijaToxicityDetector (binary)
    results:
      - task:
          type: text-classification
          name: Toxicity Detection (binary)
        dataset:
          name: OMCD_Typica.ai_Mix (test split)
          type: OMCD_Typica.ai_Mix
        metrics:
          - type: accuracy
            value: 0.8307
            name: Accuracy
          - type: f1
            value: 0.8308
            name: Weighted F1
widget:
  - text: "هاد الفيديو ما عجبنيش بزاف"
    example_title: "Clean: content criticism"
  - text: "مول هاد الفيديو باسل وما مربّيش"
    example_title: "Offensive: personal attack"
  - text: "هاد الإنفلونسر مكلّخ غير كيخربق"
    example_title: "Offensive: personal attack"

---

# DarijaToxicityDetector — Moroccan Darija Toxicity Detection (Binary)

**DarijaToxicityDetector** is a BERT-based binary text classifier that detects toxic / offensive content in **Moroccan Darija** (Moroccan Arabic dialect, written in Arabic script). It is fine-tuned from [SI2M-Lab/DarijaBERT](https://huggingface.co/SI2M-Lab/DarijaBERT) on the **OMCD_Typica.ai_Mix** dataset, a curated blend of the public OMCD dataset and Typica.ai's proprietary culturally grounded annotations.

The model is released by [Typica.ai](https://typica.ai) as part of its applied research on **culturally localized AI for underserved languages**, and is **open-sourced for educational and research purposes**.

> 📄 **Companion paper:** [A Comparative Benchmark of a Moroccan Darija Toxicity Detection Model (Typica.ai) and Major LLM-Based Moderation APIs (OpenAI, Mistral, Anthropic)](https://arxiv.org/abs/2505.04640) — the benchmark shows that this culturally adapted model outperforms general-purpose LLM moderation APIs on Moroccan Darija toxicity detection.

## Model Details

| | |
|---|---|
| **Developed by** | Hicham Assoudi — Typica.ai |
| **Model type** | BERT-based sequence classification (binary) |
| **Language** | Moroccan Darija (`ary`), Arabic script |
| **Base model** | [SI2M-Lab/DarijaBERT](https://huggingface.co/SI2M-Lab/DarijaBERT) |
| **License** | CC BY-NC 4.0 (non-commercial — education & research) |
| **Paper** | [arXiv:2505.04640](https://arxiv.org/abs/2505.04640) |
| **Contact** | assoudi@typica.ai |

### Labels

| id | label | meaning |
|----|-------|---------|
| 0 | `clean` | Non-toxic content |
| 1 | `offensive` | Toxic content (insults, hate, obscenity, culturally embedded aggression) |

## Intended Uses

**Direct intended uses:**
- Research on toxicity detection and content moderation for low-resource languages and Arabic dialects.
- Education: teaching NLP fine-tuning, evaluation, and culturally adapted model design.
- Benchmarking against general-purpose moderation systems (see companion paper).
- Prototyping moderation pipelines for Moroccan Darija user-generated content (comments, social media, forums).

**Out-of-scope uses:**
- ❌ Commercial deployment without a separate agreement with Typica.ai (license is non-commercial).
- ❌ Fully automated moderation decisions without human review — the model produces errors, especially on sarcasm.
- ❌ Text dominated by Latin script (Arabizi, French, English): such content was filtered out of training data.
- ❌ Other Arabic dialects or MSA — performance is not guaranteed outside Moroccan Darija.

## How to Use

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="TypicaAI/DarijaToxicityDetector",
)

texts = [
    "هاد الفيديو ما عجبنيش بزاف", # expected: clean
    "مول هاد الفيديو باسل وما مربّيش", # expected: offensive
    "هاد الإنفلونسر مكلّخ غير كيخربق" # expected: offensive
]
print(classifier(texts, truncation=True, max_length=512))
# [{'label': 'clean', 'score': ...}, {'label': 'offensive', 'score': ...}]
```

## Training Data: OMCD_Typica.ai_Mix

The model was trained on **OMCD_Typica.ai_Mix** (12,758 Moroccan Darija comments), built as follows:

- **Backbone:** [OMCD — Offensive Moroccan Comments Dataset](https://doi.org/10.1007/s10579-023-09663-2) (Essefar et al., 2023), a widely cited, well-annotated public resource for Darija toxicity research.
- **Enrichment:** Typica.ai proprietary annotations covering *culturally embedded* toxicity — indirect insults, sarcasm, euphemisms, and culturally specific aggression that general-purpose models frequently miss.
- **Preprocessing:** sentences with >50% Latin-script characters were removed (to exclude French/English/Arabizi-dominated code-switching); Latin characters, symbols, and punctuation were cleaned while preserving linguistic meaning.
- **Balancing:** random undersampling of the majority (clean) class to reach a **1:1 clean/offensive ratio**, preventing majority-class bias.

### Splits

| Split | Examples | Used for |
|-------|---------:|----------|
| Train | 9,568 | Fine-tuning |
| Validation | 2,552 | Best-checkpoint selection |
| Test | 638 | Final evaluation & paper benchmark |

Each example carries `sentence`, `label` (ClassLabel: `clean`/`offensive`), `idx`, and `origin` (source provenance) fields.

> The test split is publicly available for reproducibility in the [benchmark GitHub repository](https://github.com/assoudi-typica-ai/darija-toxicity-benchmark). The proprietary training annotations are not released.

## Training Procedure

- **Base checkpoint:** `SI2M-Lab/DarijaBERT`
- **Objective:** binary sequence classification (cross-entropy)
- **Tokenization:** DarijaBERT tokenizer, truncation at `max_length=512`, dynamic padding (`DataCollatorWithPadding`)

**Hyperparameters:**

| Hyperparameter | Value |
|---|---|
| Learning rate | 2e-5 |
| Train batch size | 16 |
| Eval batch size | 8 |
| Epochs | 10 |
| Weight decay | 0.01 |
| Eval/save strategy | per epoch, best model restored at end |

## Evaluation

### Held-out test set (638 examples, balanced)

| Metric | Score |
|---|---:|
| Accuracy | **0.8307** |
| Weighted F1 | **0.8308** |

### Benchmark vs. commercial moderation APIs

**Original benchmark** — from the [companion paper](https://arxiv.org/abs/2505.04640) (May 2025), on the OMCD_Typica.ai_Mix test split (n = 630):

| Model | Accuracy | Macro F1 | Toxic F1 | Not-Toxic F1 |
|---|---:|---:|---:|---:|
| **Typica.ai (this line of models)** | **0.830** | **0.830** | **0.834** | **0.827** |
| OpenAI (omni-moderation-latest) | 0.652 | 0.644 | 0.589 | 0.699 |
| Mistral (mistral-moderation-latest) | 0.649 | 0.641 | 0.588 | 0.694 |
| Anthropic Claude (claude-3-haiku-20240307) | 0.659 | 0.617 | 0.743 | 0.492 |

**Updated re-run** — July 2026, same gold test set (n = 630, balanced), same inputs to all APIs, using each provider's then-current moderation endpoint (weighted precision / recall / F1):

| Model | Precision | Recall | F1-score |
|---|---:|---:|---:|
| **Typica.ai (custom BERT-based model)** | **0.832** | **0.830** | **0.830** |
| Anthropic Claude (claude-haiku-4-5-20251001) | 0.695 | 0.657 | 0.646 |
| OpenAI (omni-moderation-latest) | 0.692 | 0.630 | 0.607 |
| Mistral (mistral-moderation-latest) | 0.633 | 0.592 | 0.571 |

Fourteen months after the original benchmark, the performance gap persists even against newer commercial models: the culturally adapted classifier still leads by ~18+ F1 points. General-purpose APIs continue to miss culturally nuanced toxicity (indirect insults, sarcasm, cultural idioms), while the specialized model maintains the best balance between catching toxic content and avoiding false positives.

## Limitations & Bias

- **Cross-dialectal noise:** source data may include some non-Moroccan Arabic dialect examples.
- **Annotation subjectivity:** toxicity is culturally shaped; annotator judgment introduces some variance.
- **Sarcasm false positives:** highly sarcastic but benign messages can be flagged as offensive.
- **Script coverage:** Arabic script only; Arabizi/Latin-script Darija is out of scope.
- **Temporal drift:** online toxic language evolves; periodic re-training is recommended.

## Ethical Considerations

This model deals with offensive content by design. It should support — not replace — human moderation. Misclassification can silence legitimate speech (false positives) or expose users to harm (false negatives). Deployers should implement human-in-the-loop review, appeal mechanisms, and threshold calibration appropriate to their community norms.

## Citation

If you use this model, please cite:

```bibtex
@article{assoudi2025comparative,
  title   = {A Comparative Benchmark of a Moroccan Darija Toxicity Detection Model (Typica.ai) and Major LLM-Based Moderation APIs (OpenAI, Mistral, Anthropic)},
  author  = {Assoudi, Hicham},
  journal = {arXiv preprint arXiv:2505.04640},
  year    = {2025},
  url     = {https://arxiv.org/abs/2505.04640}
}
```

## Contact

**Hicham Assoudi** — Founder & Applied AI Researcher, Typica.ai · PhD (AI/NLP)
*Typica.ai* — Independent applied research initiative
📧 assoudi@typica.ai · [Linkedin](https://www.linkedin.com/in/assoudi) . 🌐 [typica.ai](https://typica.ai) · 🤗 [TypicaAI on Hugging Face](https://huggingface.co/TypicaAI)