Cross-lingual sentence embeddings for Nigerian languages (Hausa, Yoruba, Igbo). Contrastively fine-tuned from olaverse/mist-encoder-base-ng on general-domain synthetic parallel pairs: clean English sentences (FineWeb, ODC-By) machine-translated into ha/yo/ig with the MIT-licensed HelpMumHQ/AI-translator-eng-to-9ja, forming English↔Nigerian and Nigerian↔Nigerian pairs that share an English source. Mean pooling, cosine similarity.

python

from sentence_transformers import SentenceTransformer
m = SentenceTransformer("olaverse/naija-embed-base")
emb = m.encode(["sentence one", "sentence two"])

Best for

Cross-lingual retrieval (e.g. Hausa query → Yoruba document), within-language semantic search, clustering, RAG, and deduplication over Nigerian-language text.

Evaluation

Within-language usefulness — frozen embeddings + logistic regression on MasakhaNEWS topics (test accuracy / macro-F1):

Lang	Acc	Macro-F1
Hausa	0.818	0.803
Yoruba	0.798	0.796
Igbo	0.808	0.772

Cross-lingual retrieval — acc@1 on FLORES+ (real human-translated dev, n=997, no shared source). This is the trustworthy cross-lingual benchmark:

Pair	acc@1
Hausa → Yoruba	0.670
Igbo → Yoruba	0.581

Limitations

Synthetic training data. Pairs are machine-translated and carry MT noise; cross-lingual alignment is genuine (see FLORES) but below what a large model trained on real parallel data would reach. Igbo alignment is slightly looser than Hausa, reflecting translator quality.
No Nigerian Pidgin (pcm). The translator only outputs ha/yo/ig, so Pidgin was not part of cross-lingual training.

License & provenance

Apache-2.0 weights. Training data derived from ODC-By English (FineWeb) via an MIT-licensed translation model.