# SimCSE optimization

A sentence-transformers model based on XLM-R, trained using the SimCSE method, which maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

A sentence transformer model based on m-Distil-BERT, trained using the SimCSE method, capable of mapping text to 768-dimensional vectors, suitable for semantic search and clustering tasks

A Thai sentence embedding model based on mBERT, trained using the SimCSE method on Thai Wikipedia data, capable of mapping text to 768-dimensional vectors