# 768-dimensional Dense Vector

This is the Investopedia embedding model developed by the FinLang team for financial applications. It is fine-tuned based on BAAI/bge-base-en-v1.5 and maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for semantic search and other tasks in the financial domain.

This is a sentence similarity calculation model based on sentence-transformers, capable of mapping text to a 768-dimensional vector space

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, semantic search, and clustering.

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

A sentence transformer model optimized for phrases, mapping phrases into a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

Spanish sentence embedding model based on BERTIN RoBERTa, optimized for natural language inference tasks