SEA-LION-Embedding: Open and Reproducible Text Embeddings for Southeast Asia

Abstract:Text embeddings are fundamental to many downstream applications, making robustness important for real-world NLP. However, most recent state-of-the-art embedding models are not reproducible because they rely on closed or undisclosed training data, and they remain insufficiently robust for Southeast Asian languages. We present SEA-LION-Embedding, a fully open and reproducible text-embedding pipeline for Southeast Asian languages trained only on publicly available data, and use it to study three core factors of robust embedding design: data composition, training objective, and base encoder initialization. SEA-LION-Embedding achieves state-of-the-art results on SEA-BED while enabling systematic and reproducible analysis of robust text embeddings for the region.

Computation and Language (cs.CL)

arXiv:2606.03027 [cs.CL]

(or arXiv:2606.03027v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.03027 Focus to learn more arXiv-issued DOI via DataCite

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