# Dense Vector Encoding

This is a sentence embedding model based on sentence-transformers, capable of mapping text to a 768-dimensional vector space, suitable for semantic search and text similarity calculation.

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 encoder model based on sentence-transformers, capable of converting text into 768-dimensional vector representations, suitable for tasks such as semantic search and sentence similarity calculation.

This is a sentence transformer model that maps text to a dense vector space, suitable for semantic search and clustering tasks.

This is an ONNX-based sentence transformer model that maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.

A sentence embedding model based on sentence-transformers that maps text to a 768-dimensional vector space

This is a sentence transformer model based on DistilBERT, specifically designed for feature extraction and sentence similarity calculation.

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

A sentence embedding model trained on the MS MARCO dataset, using a word2vec-initialized 256k vocabulary and DistilBERT architecture, suitable for semantic search and sentence similarity tasks