TypicaAI
/

DarijaToxicityDetector

@@ -1,199 +1,213 @@
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 ---
+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
+From the [companion paper](https://arxiv.org/abs/2505.04640), on the same OMCD_Typica.ai_Mix test split:
+| 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 |
+The culturally adapted model achieves the best balance between catching toxic content and avoiding false positives — the general-purpose APIs miss much culturally nuanced toxicity (indirect insults, sarcasm, cultural idioms), while prompt-driven LLM moderation tends to over-flag benign content.
+## 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}
+}
+```
+Please also cite the OMCD dataset the training data builds upon:
+```bibtex
+@article{essefar2023omcd,
+  title   = {OMCD: Offensive Moroccan Comments Dataset},
+  author  = {Essefar, Kabil and Ait Baha, Hassan and El Mahdaouy, Abdelkader and El Mekki, Abdellah and Berrada, Ismail},
+  journal = {Language Resources and Evaluation},
+  volume  = {57},
+  number  = {4},
+  pages   = {1745--1765},
+  year    = {2023},
+  doi     = {10.1007/s10579-023-09663-2}
+}
+```
+## 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)