| ---
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| language:
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| - ar
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| library_name: transformers
|
| pipeline_tag: fill-mask
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| base_model:
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| - UBC-NLP/MARBERT
|
| tags:
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| - arabic
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| - maghrebi-arabic
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| - darija
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| - arabizi
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| - code-switching
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| - algeria
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| - morocco
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| - tunisia
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| - libya
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| - bert
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| - pytorch
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| - masked-language-modeling
|
| ---
|
|
|
| <img src="https://i.imgur.com/cwgkvqe.png"
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| alt="MagharibiBERT" width="35%" align="right"/>
|
|
|
| # MagharibiBERT
|
|
|
| **MagharibiBERT** is a regionally adapted Transformer language model
|
| for **Maghrebi Arabic**.
|
|
|
| It is initialized from `UBC-NLP/MARBERT` and further adapted using
|
| Masked Language Modeling (MLM) on a large multi-source corpus of
|
| Maghrebi text.
|
|
|
| The model targets the linguistic and orthographic diversity of
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| North African online communication, including Arabic-script dialect,
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| Arabizi, spelling variation, and Arabic-French/English code-switching.
|
|
|
| MagharibiBERT is developed by **NumidiaNLP**.
|
|
|
| ---
|
|
|
| ## Model architecture
|
|
|
| | Property | Value |
|
| |---|---:|
|
| | Architecture | BERT |
|
| | Parent model | `UBC-NLP/MARBERT` |
|
| | Objective | Masked Language Modeling |
|
| | Parameters | 162,942,880 |
|
| | Transformer layers | 12 |
|
| | Hidden size | 768 |
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| | Attention heads | 12 |
|
| | Vocabulary size | 100,000 |
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| | Maximum position embeddings | 512 |
|
|
|
| ---
|
|
|
| ## Regional continued pretraining
|
|
|
| MagharibiBERT underwent additional regional continued pretraining on
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| a curated mixture of **17 corpora** covering Maghrebi Arabic and
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| North African social-media language.
|
|
|
| The training mixture includes material associated with Algeria, Tunisia,
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| Morocco, Libya, and cross-regional Maghrebi sources, with explicit
|
| coverage of Arabic script, Arabizi, and code-mixed text.
|
|
|
| ### Training configuration
|
|
|
| - MLM sequence length: 128
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| - Optimizer steps: 120,000
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| - Effective batch size: 48
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| - Learning rate: 2e-5
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| - Warmup steps: 6,000
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| - Weight decay: 0.01
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| - Precision: BF16
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| - Random seed: 42
|
|
|
| ---
|
|
|
| ## How to use
|
|
|
| ### Fill-mask pipeline
|
|
|
| ```python
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| from transformers import pipeline
|
|
|
| pipe = pipeline(
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| "fill-mask",
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| model="NumidiaNLP/MagharibiBERT"
|
| )
|
|
|
| results = pipe("واش راك [MASK]", top_k=5)
|
|
|
| for result in results:
|
| print(result["token_str"], result["score"])
|
| ```
|
|
|
| ### Example output
|
|
|
| Output produced by the released MagharibiBERT checkpoint:
|
|
|
| ```text
|
| {"token": "دير", "score": 0.277943}
|
| {"token": "تهدر", "score": 0.071477}
|
| {"token": "تدير", "score": 0.062739}
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| {"token": "تقول", "score": 0.047855}
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| {"token": "تحكي", "score": 0.038360}
|
| ```
|
|
|
| > Scores depend on the complete masked context. The example above is
|
| > provided only to demonstrate model usage rather than as an evaluation result.
|
|
|
| ### Load the model directly
|
|
|
| ```python
|
| from transformers import AutoTokenizer, AutoModelForMaskedLM
|
|
|
| model_name = "NumidiaNLP/MagharibiBERT"
|
|
|
| tokenizer = AutoTokenizer.from_pretrained(model_name)
|
| model = AutoModelForMaskedLM.from_pretrained(model_name)
|
| ```
|
|
|
| ---
|
|
|
| ## Intended use
|
|
|
| MagharibiBERT is intended primarily for research involving Maghrebi Arabic,
|
| including:
|
|
|
| - hate and offensive language detection,
|
| - sentiment analysis,
|
| - dialect identification,
|
| - Arabizi processing,
|
| - code-switched text processing,
|
| - social-media text classification,
|
| - representation learning for Maghrebi Arabic.
|
|
|
| ---
|
|
|
| ## Intrinsic evaluation
|
|
|
| On the fixed held-out MLM test set used during development, the released
|
| checkpoint obtained:
|
|
|
| | Metric | MagharibiBERT |
|
| |---|---:|
|
| | MLM loss | 2.8099 |
|
| | Perplexity | 16.6080 |
|
| | Masked-token accuracy | 52.42% |
|
|
|
| Downstream evaluation against MARBERT and other Arabic language models
|
| is being conducted separately.
|
|
|
| ---
|
|
|
| ## Limitations
|
|
|
| Maghrebi Arabic is highly heterogeneous across countries, regions,
|
| writing systems, platforms, and communities. Performance may therefore
|
| vary across domains and linguistic varieties.
|
|
|
| The model should not be interpreted as representing all North African
|
| languages or all Maghrebi speakers equally.
|
|
|
| ---
|
|
|
| ## Authors
|
|
|
| - **Ahmed Zoubir Messalti**
|
| - **Sadik Bessou**
|
|
|
| **Institution:** University Ferhat Abbas Sétif 1, Algeria
|
| **Corresponding author:** ahmedzoubir.messalti@univ-setif.dz
|
|
|
| ---
|
|
|
| ## Citation
|
|
|
| If you use MagharibiBERT in your research, please cite the accompanying paper:
|
|
|
| > Ahmed Zoubir Messalti and Sadik Bessou.
|
| > **MagharibiBERT: A Regionally Adapted Language Model for Maghrebi Arabic Social Media.**
|
| > *International Journal of Computers and Their Applications*, 2026.
|
| > **Accepted for publication. Planned issue: Volume 33, No. 4, December 2026.**
|
|
|
| Final issue assignment, page numbers, and the article link will be updated upon publication.
|
|
|
| ```bibtex
|
| @article{messalti2026magharibibert,
|
| author = {Messalti, Ahmed Zoubir and Bessou, Sadik},
|
| title = {{MagharibiBERT}: A Regionally Adapted Language Model
|
| for Maghrebi Arabic Social Media},
|
| journal = {International Journal of Computers and Their Applications},
|
| year = {2026},
|
| note = {Accepted for publication.
|
| Planned issue: volume 33, number 4, December 2026}
|
| }
|
| ```
|
|
|
| MagharibiBERT is developed within the MARBERT model lineage.
|
| Users should also cite the original MARBERT work:
|
|
|
| > Muhammad Abdul-Mageed, AbdelRahim Elmadany, and El Moatez Billah Nagoudi. (2021).
|
| > **ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic.**
|
| > *ACL-IJCNLP 2021*, pp. 7088–7105.
|
| > https://doi.org/10.18653/v1/2021.acl-long.551
|
|
|
| ---
|
|
|
| ## Organization
|
|
|
| **NumidiaNLP — Language Intelligence for North Africa**
|
|
|
|
|
