# 384-dimensional Embedding

This is a sentence similarity model based on sentence-transformers, which maps text to a 384-dimensional vector space 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.

This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a sentence embedding model based on sentence-transformers, capable of converting text into 384-dimensional vector representations.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

A patent contradiction sentence identification model based on all-MiniLM-L6-v2, capable of mapping sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks like clustering or semantic search.

This is an ONNX-converted model based on sentence-transformers/all-MiniLM-L6-v2, capable of mapping sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks like clustering or semantic search.

This is a sentence similarity calculation model based on sentence-transformers, capable of mapping text to a 384-dimensional vector space