# Multilingual Sentence Embedding

GTE-Multilingual-Base is a multilingual sentence embedding model that supports sentence similarity calculation for multiple languages.

This is a multilingual sentence embedding model capable of mapping sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks such as clustering and semantic search.

NaSE is a news domain-specialized multilingual sentence encoder, based on LaBSE with domain-specific training, supporting sentence embedding and similarity calculation for 100+ languages.

This is a multilingual sentence embedding model based on sentence-transformers, capable of mapping text to a 1024-dimensional vector space, suitable for semantic search and clustering tasks.

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

This is a sentence transformer model based on XLM-RoBERTa, which maps text to a 768-dimensional vector space, suitable for tasks such as semantic search and sentence similarity calculation.