---
license: apache-2.0
language:
- ha
- yo
- ig
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- nigerian
- hausa
- yoruba
- igbo
- cross-lingual
base_model: olaverse/mist-encoder-base-ng
---

![mist models](https://cdn-uploads.huggingface.co/production/uploads/69949cbacd82af728f850c12/WzzrAklKUsaCCTTASicDf.png)


# naija-embed-base

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.