# 768-dimensional embedding

M2-BERT-128 is an 80-million-parameter retrieval model checkpoint proposed in the paper 'Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT'

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.

This is a sentence-transformers based model that maps sentences and paragraphs to a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-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 that can convert text into a 768-dimensional vector representation, suitable for tasks such as semantic similarity and text clustering.

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

This is a sentence transformer model based on the ALBERT-small architecture, capable of mapping sentences and paragraphs into a 768-dimensional vector space, suitable for tasks such as sentence similarity calculation and semantic search.