# Dense vector retrieval

The GTE multilingual base model is a dense sentence transformer that supports sentence similarity calculation and text embedding tasks in multiple languages.

A long-context bi-encoder based on MosaicML's pre-trained BERT with 1024 sequence length, for sentence and paragraph embeddings

A long-context bi-encoder based on MosaicML's pre-trained BERT with 1024 sequence length, designed for generating 768-dimensional dense vector representations of sentences and paragraphs

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

A multilingual sentence embedding model optimized for Bengali, mapping text to a 768-dimensional vector space

This is a sentence-transformers based model that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like sentence similarity computation and semantic search.