# Medical Text Embedding

MedCPT is a model capable of generating biomedical text embeddings, particularly suitable for semantic search (dense retrieval) tasks.

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 clustering or semantic search.

A PubMedBERT-based sentence transformer model for generating 768-dimensional vector representations of sentences and paragraphs, suitable for semantic search and clustering tasks.

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.

A sentence-transformer model based on PubMedBert for generating 768-dimensional vector representations of sentences and paragraphs, suitable for clustering and semantic search tasks.

A sentence-transformers model fine-tuned on the MS-MARCO dataset based on PubMedBERT, suitable for semantic similarity calculation and information retrieval tasks in the medical/health text domain

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, clustering, and semantic search.