# 768-dimensional vector embedding

SimCSE model based on IndoBERT for generating semantic embedding vectors of Indonesian sentences

This is a multilingual sentence transformer model supporting English, Russian, Ukrainian, and Polish, primarily used for sentence similarity computation and feature extraction tasks.

This is a sentence-transformers based model that maps Vietnamese address sentences and paragraphs into a 768-dimensional dense vector space, primarily used for address standardization tasks.

This is a Korean sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.