Sentence Similarity
sentence-transformers
Safetensors
Twi
Akan
English
xlm-roberta
feature-extraction
twi
akan
cross-lingual-retrieval
retrieval
rag
knowledge-distillation
text-embeddings-inference
Instructions to use DexelLabs/MargEmbed-Akan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DexelLabs/MargEmbed-Akan with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DexelLabs/MargEmbed-Akan") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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- Optimized for Twi questions over English passages; for English-only search, the base model or proprietary models are equal or better.
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- The AfriQA evaluation corpus has 5,357 passages; scores on a full Wikipedia-scale index will be lower.
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## License
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The training data includes NLLB-200 translations (CC BY-NC 4.0), so this model is released for **research and non-commercial use**.
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## Citation
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Paper forthcoming. Please cite this model page until then.
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- Optimized for Twi questions over English passages; for English-only search, the base model or proprietary models are equal or better.
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- The AfriQA evaluation corpus has 5,357 passages; scores on a full Wikipedia-scale index will be lower.
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