# 1024-dimensional vector space

This is a sentence transformer model fine-tuned on xlm-roberta-large, which can map text to a 1024-dimensional vector space and support multilingual semantic understanding tasks.

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

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.

This is a Polish-language sentence embedding model capable of mapping sentences and paragraphs into a 1024-dimensional vector space, suitable for semantic search and clustering tasks.

A German text embedding model based on the sentence-transformers framework, capable of mapping text to a 1024-dimensional vector space, specifically designed to enhance few-shot text classification performance in German.