---
language: kab
tags:
- token-classification
- pos-tagging
- kabyle
- berber
- african-languages
license: cc-by-nc-4.0
model-index:
- name: Kabyle POS Tagger
  results:
  - task:
      type: token-classification
      name: Part-of-Speech Tagging
    dataset:
      type: masakhane-pos
      name: MasakhaPOS Kabyle
    metrics:
    - type: f1
      value: 0.875
    - type: precision
      value: 0.873
    - type: recall
      value: 0.878
---

# Kabyle POS Tagger

Part-of-Speech tagger for **Kabyle** (`kab`), a Berber language spoken in Algeria.

## Model Details

| Attribute | Value |
|-----------|-------|
| Base model | XLM-RoBERTa-base |
| Task | Token Classification (POS tagging) |
| Language | Kabyle (kab) |
| Training sentences | ~1,200 |
| Test F1 | **87.5%** |
| Test Precision | **87.3%** |
| Test Recall | **87.8%** |

## Dataset

Annotated following the **Universal Dependencies** POS tagset:
`ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X`

Source: Tatoeba parallel corpus, semi-manually annotated.

## Usage

```python
from transformers import pipeline

tagger = pipeline(
    "token-classification",
    model="boffire/kabyle-pos",
    aggregation_strategy="simple"
)

result = tagger("Aṭas n medden i yessen.")
```

## Limitations

This model has several known limitations that users should be aware of:

1. **Training data size**: The model was trained on only ~1,200 sentences. This is small compared to high-resource language POS taggers, which may affect generalization to unseen vocabulary and syntactic constructions.

2. **Pre-annotation noise**: Approximately 22% of tokens in the training data were pre-annotated by heuristic rules and not fully manually verified. This introduces some label noise, particularly for rare words and complex cliticized forms.

3. **Cliticized forms**: The model struggles with complex cliticized verb forms (e.g., `k-id-xeẓẓren`, `m-d-awiɣ`, `iyi-d-yewwi`) that were not well-represented in the training data. These are often tagged as `X` (unknown).

4. **Domain bias**: The training data comes from Tatoeba, which consists of short translated sentences. Performance may degrade on longer, more complex sentences from other domains (news, social media, literature).

5. **Proper nouns**: Capitalized words are heuristically tagged as `PROPN`, which may misclassify sentence-initial common nouns or adjectives.

6. **Ambiguous tags**: Some Kabyle words are genuinely ambiguous between tags (e.g., `d` as ADP vs. CCONJ, `yella` as AUX vs. VERB). The model may not consistently resolve these ambiguities.

7. **No diacritic normalization**: The model treats `e` and `ɛ`, `ḍ` and `d` as completely different characters. Misspelled or inconsistently diacritized text may perform poorly.

8. **Single annotator bias**: The annotations were created by a single annotator without inter-annotator agreement verification, which may introduce systematic tagging biases.

## Citation

```bibtex
@inproceedings{dione-etal-2023-masakhapos,
  title = {MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages},
  author = {Dione, Cheikh M. Bamba and Adelani, David Ifeoluwa and Nabende, Peter and Alabi, Jesujoba and others},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages = {10883--10900},
  year = {2023},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2023.acl-long.609},
  doi = {10.18653/v1/2023.acl-long.609}
}
```

Side part of the **Masakhane** initiative for African NLP.