---
language:
  - ary
  - ar
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: token-classification
base_model: SI2M-Lab/DarijaBERT
tags:
  - part-of-speech
  - pos-tagging
  - token-classification
  - moroccan-darija
  - darija
  - low-resource-languages
  - bert
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: DarijaPOSTagger v0.1 (preliminary)
    results:
      - task:
          type: token-classification
          name: Part-of-Speech Tagging
        dataset:
          name: Darija POS corpus (validation split, dev+test merged, in-corpus)
          type: darija-pos-corpus
        metrics:
          - type: f1
            value: 1.0
            name: Seqeval F1 (in-corpus validation — see caveats)
widget:
  - text: "شربت أتاي مع صحابي"
    example_title: "Simple sentence"
  - text: "غادي نمشي للسوق غدا"
    example_title: "Future construction"
  - text: "ما عند ي ش الفلوس"
    example_title: "Negation, clitic-segmented (ما … ش)"
---

# 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](#preliminary-validation-july-2026) and [Known Limitations](#known-limitations--bias) 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](https://huggingface.co/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](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**.

## Model Details

| | |
|---|---|
| **Developed by** | Hicham Assoudi — Typica.ai |
| **Model type** | BERT-based token classification (POS tagging) |
| **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) |
| **Version** | v0.1 (preliminary) |
| **Released** | July 2026 |
| **Contact** | assoudi@typica.ai <!-- TODO: verify preferred contact email --> |

### Tagset (15 tags)

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

| Tag | Category | Corpus-attested examples |
|-----|----------|--------------------------|
| `NOUN` | Noun | مغربي، ديبلوم |
| `V` | Verb | خدام |
| `ADJ` | Adjective | — |
| `ADV` | Adverb | عند |
| `PRON` | Pronoun | لي، هوم |
| `DET` | Determiner | ال |
| `PREP` | Preposition | ديال |
| `CONJ` | Conjunction | — |
| `PART` | Particle | — |
| `PROG_PART` | Progressive particle | — |
| `FUT_PART` | Future particle | — |
| `NEG_PART` | Negation particle | ما، ش |
| `NSUFF` | Noun suffix | ات |
| `CASE` | (per corpus scheme) | — |
| `O` | Other (punctuation, numbers, URLs, mentions, emoticons, foreign tokens — collapsed) | 0.7 |

> 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

```python
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.

<!-- TODO: add source corpus name, provenance, and citation -->

**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

| Split | Sentences | Used for |
|-------|----------:|----------|
| Train | 1,225 | Fine-tuning |
| Dev | 175 | — |
| Test | 350 | — |
| Validation (dev + test merged) | 525 | Per-epoch evaluation (monitoring) |

## 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:**

| Hyperparameter | Value |
|---|---|
| Learning rate | 2e-5 |
| Epochs | 10 |
| Weight decay | 0.01 |
| Eval/save strategy | per epoch |
| Data collator | `DataCollatorForTokenClassification` (dynamic padding) |

## 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:

| Word | Predicted | Confidence |
|---|---|---:|
| كيلعب | V | 1.000 |
| نمشي | V | 1.000 |
| شربت | V | 1.000 |
| غدا | ADV | 0.989 |
| الفلوس | NOUN | 0.998 |
| للسوق | NOUN | 0.999 |

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

| Input (raw, fused) | Predicted | Observation |
|---|---|---|
| الولد | O (0.512) | Near coin-flip confidence; fused article form unseen as a single token |
| عنديش | ADV (0.582) | Low confidence on the fused form. Note: the corpus tags عند as `ADV`, so the predicted class follows the corpus convention |
| فالزنقة | merged into a NOUN span | The fused preposition ف is not separately tagged (also affected by pipeline span aggregation) |

**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:

| Word | Predicted | Confidence |
|---|---|---:|
| غادي | PART | 0.988 |
| مع | CONJ | 0.996 |

**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:

```bibtex
@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](https://huggingface.co/SI2M-Lab/DarijaBERT) — see its model card for the authors' preferred citation.

## Related Models by Typica.ai

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