# 1024-dimensional Embedding

This is a model based on sentence-transformers that can map sentences and paragraphs into a 1024-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 1024-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 1024-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 converting text into 1024-dimensional vector representations, suitable for tasks such as sentence similarity calculation and semantic search.

This is a Portuguese legal domain-specific model based on the BERTimbau large version, optimized for semantic textual similarity tasks.