# 384-dimensional Vector

This is a sentence-transformers-based model, specifically optimized for Australian legal texts, capable of mapping sentences and paragraphs into a 384-dimensional vector space, suitable for semantic search and clustering tasks in the legal domain.

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 vector space for sentence similarity computation and semantic search tasks.

This is a model based on sentence-transformers that can map sentences and paragraphs to a 384-dimensional 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.

The unsupervised version of E5-small, generating text embeddings through weakly supervised contrastive pre-training, suitable for tasks like text similarity calculation

This is a sentence embedding model based on the MiniLM architecture, capable of mapping text to a 384-dimensional vector space, suitable for tasks such as semantic search and sentence similarity calculation.

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

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 sentence similarity calculation and semantic search.

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 clustering or semantic search.

This is a sentence similarity model based on sentence-transformers, which maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.

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

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