| --- |
| 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. |
