README.md

metadata

language:
  - en
  - kab
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - kabyle
  - taqbaylit
  - tamazight
  - berber
  - embeddings
  - cross-lingual
  - african-languages
  - nlp
datasets:
  - Imsidag-community/nllb_en_kab
  - Imsidag-community/english-kabyle-parallel
  - Imsidag-community/libretranslate-suggestions
  - ayymen/Weblate-Translations
pipeline_tag: sentence-similarity

Kabyle Sentence Transformer (MPNet)

A sentence embedding model specifically fine-tuned for Kabyle (Taqbaylit) - English cross-lingual semantic similarity.

Model Details

Attribute	Value
Base model	sentence-transformers/paraphrase-multilingual-mpnet-base-v2
Fine-tuning data	~2.5M unique EN–KAB parallel sentences
Embedding dimension	768
Training framework	SentenceTransformers
Training time	~1h 16min (1 epoch, 15,593 steps)
Final loss	0.043 (started at 0.278)

Training Data

Source	Pairs	Description
NLLB (cleaned)	~2.35M	Diverse domain parallel corpus
Tatoeba + CS	~202K	Community translations + software localization
Weblate	~9K	FLOSS UI strings
LibreTranslate	~449	User-reviewed translations

Performance

Compared to the base paraphrase-multilingual-mpnet-base-v2 (untrained):

Metric	Base	This Model	Gain
Avg. cosine similarity (EN<->KAB)	0.278	0.857	+58 points

Usage

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("boffire/kabyle-sentence-transformer-mpnet")

# Embed English and Kabyle
sentences = ["Hello!", "Azul!"]
embeddings = model.encode(sentences)

# Cross-lingual similarity
from sklearn.metrics.pairwise import cosine_similarity
sim = cosine_similarity([embeddings[0]], [embeddings[1]])
print(sim)

Limitations

• Trained primarily on parallel data; monolingual Kabyle similarity not explicitly optimized
• Best for EN<->KAB cross-lingual tasks; Kabyle<->Kabyle may work but is untested
• Religious text overrepresented in NLLB portion; may underperform on highly technical/modern domains
• Evaluator used constant labels (all 1.0) due to all pairs being positive; correlation metrics were undefined

Future Work

• Train v2 with Davlan/afro-xlmr-large backbone for African-specific pretraining
• Add monolingual Kabyle data for better Kabyle<->Kabyle similarity
• Fix evaluator to use AvgCosineEvaluator instead of correlation-based metrics
• Evaluate against LASER on a proper benchmark

Citation

If you use this model, please cite:

@misc{kabyle-st-mpnet,
  title={Kabyle Sentence Transformer},
  author={boffire},
  year={2026},
  howpublished={\url{https://huggingface.co/boffire/kabyle-sentence-transformer-mpnet}}
}

Acknowledgments

• Imsidag-community for the cleaned parallel corpora
• Tatoeba contributors for community translations
• Meta AI for LASER and NLLB datasets
• boffire community for Kabyle NLP tooling