# 768-Dimensional Dense Vector

This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for semantic similarity and feature extraction tasks.

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

This is a 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 sentence similarity calculation and semantic search.

This is a sentence transformer model based on XLM-Roberta-base, specifically designed for semantic text similarity tasks in Greek and English.