DarijaPOSTagger (v0.1) — Part-of-Speech Tagging for Moroccan Darija

🚧 Preliminary release (v0.1) — for education & research. This model was trained on a small corpus (1,225 sentences) and is released early, with its limitations openly documented, as a teaching artifact and a baseline for the Darija NLP community. Read the Preliminary Validation and Known Limitations sections before using it. Not intended for production use.

DarijaPOSTagger is a BERT-based token classification model that assigns part-of-speech (POS) tags to Moroccan Darija text (Moroccan Arabic dialect, Arabic script). It is fine-tuned from SI2M-Lab/DarijaBERT on a POS-annotated Darija corpus using a 15-tag scheme that includes Darija-relevant particle categories (PROG_PART, FUT_PART, NEG_PART) alongside standard classes. Notably, the corpus is morpheme-segmented: clitics such as the article ال and the negation ش are annotated as separate tokens (e.g., ما NEG_PART + عند ADV + هوم PRON + ش NEG_PART).

The model is released by Typica.ai as part of its applied research on culturally localized AI for underserved languages, and is open-sourced for educational and research purposes.

Model Details

Tagset (15 tags)

Corpus-attested examples are shown where available; the annotation scheme of the source corpus is authoritative.

Note: some conventions may differ from other Arabic tagsets — for instance, this corpus annotates عند as ADV. Consult the source corpus's annotation guidelines for authoritative definitions.

Intended Uses

Direct intended uses:

• Research on morphosyntactic analysis of Moroccan Darija and Arabic dialects.
• Education: teaching token classification, subword/label alignment, and fine-tuning for low-resource languages.
• Baseline / preprocessing component in experimental Darija NLP pipelines.

Out-of-scope uses:

• ❌ Commercial deployment without a separate agreement with Typica.ai (license is non-commercial).
• ❌ Latin-script Darija (Arabizi), French, or English text: non-Arabic characters were cleaned from training data.
• ❌ Modern Standard Arabic or other Arabic dialects — the training data is Darija-specific.
• ❌ Production use: this is a preliminary v0.1 release.

How to Use

from transformers import pipeline

pos_tagger = pipeline(
    "token-classification",
    model="TypicaAI/DarijaPOSTagger",
    aggregation_strategy="first",
    ignore_labels=[],   # important: show 'O' predictions too (POS ≠ NER — every word needs a tag)
)

sentences = [
    "غادي نمشي للسوق غدا",     # I will go to the market tomorrow
    "شربت أتاي مع صحابي",      # I drank tea with my friends
    "ما عند ي ش الفلوس",       # I don't have money (clitic-segmented, matching the training format)
]

for sentence in sentences:
    print(f"\n{sentence}")
    for token in pos_tagger(sentence):
        print(f"  {token['word']:<15} → {token['entity_group']:<10} ({token['score']:.3f})")

ℹ️ Input format matters. The training corpus is morpheme-segmented (clitics like ال and ش appear as separate tokens), while raw Darija text fuses them into surface words (e.g., عنديش، فالزنقة). Fused surface forms are out-of-distribution for this model; expect lower confidence and less reliable tags on them (see Preliminary Validation). Also note that aggregation_strategy may merge adjacent same-label words into one span — for strict one-tag-per-word output, tokenize with is_split_into_words=True and read per-word predictions directly.

Training Data

The model was trained on a POS-annotated Moroccan Darija corpus in CoNLL-style format (one token tag pair per line, sentences separated by EOS markers), read from local files.

Preprocessing (as implemented in the training notebook):

• Tokens were cleaned with a character filter keeping Arabic script, digits, and basic punctuation.
• The original tag scheme was simplified to 15 tags: formatting and non-linguistic tags (WB, TB, EMOT, HASH, FOREIGN, MENTION, PUNC, URL, NUM) were mapped to a single O class (any tag not in the final tagset defaults to O).
• Subword/label alignment: each word's label is propagated to all of its WordPiece subwords; special tokens are masked at -100.

⚠️ Known v0.1 training artifact: the label-alignment code inherits a B- → I- conversion step from NER-style code (if label % 2 == 1: label += 1), which is not meaningful for POS tags. For words split into multiple subwords, this shifts odd-id labels on continuation subwords to the next label id (e.g., DET → FUT_PART, PRON → V) during training. The impact has not been quantified; it is scheduled to be removed in v0.2.

Splits

Training Procedure

• Base checkpoint: SI2M-Lab/DarijaBERT with a freshly initialized token classification head (15 labels)
• Objective: token-level cross-entropy; seqeval-based evaluation during training
• Hardware: single T4 GPU (Google Colab)
• Checkpoint released: final model after 10 epochs (no best-checkpoint selection was configured)

Hyperparameters:

Evaluation

Quantitative (in-corpus)

On the validation split (525 sentences, dev + test merged), training-log seqeval metrics reach 1.00 (precision, recall, F1, and token accuracy) from epoch 6 onward, including the final epoch 10 checkpoint that is released here.

⚠️ Interpret with care. The validation set is small, merges the original dev and test splits, and comes from the same corpus as the training data — it is not an independent held-out test set. A perfect in-corpus score on a small, homogeneous, morpheme-segmented corpus does not translate to real-world accuracy on raw Darija text. The qualitative validation below gives a more realistic picture.

Preliminary Validation (July 2026)

Manual spot-checks on raw (unsegmented) everyday Darija sentences — a harder condition than the segmented training format — showed the following:

Reliable on canonical surface forms — open-class words are tagged consistently, typically at ≥ 0.99 confidence:

Degraded on fused clitic forms (out-of-distribution relative to the segmented training data):

Closed-class predictions to verify against corpus conventions — these are confident predictions whose correctness depends on the source corpus's annotation guidelines, which have not yet been checked for these words:

Practical takeaways: low confidence (≈ 0.5–0.6) is a useful signal that an input form is out-of-distribution (typically fused clitics); clitic-segmented input matching the training format is more reliable; and confident predictions on function words should be interpreted relative to the corpus's own annotation scheme rather than assumed tagging conventions from other resources.

Planned for v0.2: removal of the label-alignment artifact, an independent held-out evaluation on raw text, verification of closed-class conventions against the source annotation guidelines, and either a paired clitic segmenter or retraining on de-segmented surface-word data.

Known Limitations & Bias

• Preliminary release: v0.1 is an educational baseline, not a production tagger. Observations above are documented, not yet fixed.
• Segmentation mismatch: the training data is morpheme-segmented; raw text with fused clitics is out-of-distribution and degrades reliability.
• Training artifact: the subword label-alignment shift described in Training Data affects multi-subword words with odd label ids; impact unquantified.
• Small training corpus: 1,225 training sentences; limited coverage of rare constructions, regional sub-dialects, and domain vocabulary.
• In-corpus evaluation only: no independent held-out test yet; the quantitative metrics overestimate real-world performance.
• Script coverage: Arabic script only; Arabizi/Latin-script Darija is out of scope.
• Tag granularity: numbers, punctuation, URLs, mentions, and foreign words are collapsed into O.

Citation

No dedicated paper accompanies this model yet. If you use it, please cite the model directly:

@misc{assoudi2026darijapostagger,
  title        = {DarijaPOSTagger v0.1: Part-of-Speech Tagging for Moroccan Darija (Preliminary Release)},
  author       = {Assoudi, Hicham},
  year         = {2026},
  month        = {July},
  publisher    = {Hugging Face},
  organization = {Typica.ai},
  url          = {https://huggingface.co/TypicaAI/DarijaPOSTagger}
}

Please also cite the base model, SI2M-Lab/DarijaBERT — see its model card for the authors' preferred citation.

Related Models by Typica.ai

See the TypicaAI organization page for the full Darija NLP suite, including MAGBERT-NER (named entity recognition) and the Darija toxicity detection model (arXiv:2505.04640).

Contact

Hicham Assoudi — Founder & Applied AI Researcher, Typica.ai · PhD (AI/NLP) Typica.ai — Independent applied research initiative 📧 assoudi@typica.ai · Linkedin . 🌐 typica.ai · 🤗 TypicaAI on Hugging Face