# Korean sentence embedding

A sentence embedding model fine-tuned on the Korean triplet dataset based on the Alibaba-NLP/gte-multilingual-base model for semantic similarity calculation

A Korean sentence embedding model fine-tuned based on multilingual-e5-large, supporting 1024-dimensional vector space mapping, suitable for tasks such as semantic similarity calculation

A Korean sentence embedding model fine-tuned from intfloat/multilingual-e5-small, supporting 384-dimensional vector representation, suitable for tasks like semantic similarity calculation

A Korean-optimized sentence embedding model based on BAAI/bge-m3, supporting 1024-dimensional vector representation, suitable for tasks like semantic similarity calculation

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.

KoSimCSE-BERT-multitask is a high-performance Korean sentence embedding model based on BERT architecture and optimized with multi-task learning strategy, specifically designed for generating high-quality Korean sentence embeddings.

This is a sentence-transformers model based on KLUE BERT-base, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

This is a Korean sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

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.

This is a Korean sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

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

This is a Korean sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

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.

This is a Korean sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.