# High-Dimensional Embedding

This is a sentence encoder model based on sentence-transformers, capable of mapping sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as semantic search and clustering.

This is a sentence similarity model based on sentence-transformers, which maps sentences and paragraphs into a 768-dimensional vector space, suitable for clustering and semantic search tasks.

Sentence embedding model based on MPNet architecture, mapping text to a 768-dimensional vector space, suitable for semantic search and text similarity tasks