Model Catalog
Ready-to-use configurations for
1-click deployments!
all-MiniLM-L6-v2
This model maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
all-mpnet-base-v2
This model maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
mxbai-embed-large-v1
This model produces 1024-dimensional embeddings that perform extremely well on a variety of tasks. See the repository for the prefix required for queries.
multilingual-e5-large-instruct
This multilingual model is continually trained on a mixture of multilingual datasets covering 100 languages. See the repository for the prefix required for queries and passages.
paraphrase-multilingual-MiniLM-L12-v2
This multilingual model maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
ember-v1
Model trained on an extensive corpus of text pairs belonging to domains such as finance, science, medicine, law, and various others.
bge-base-en-v1.5
BGE models from BAAI are optimized for retrieval and search. See the repository for the prefix required for queries.
bge-large-en-v1.5
BGE models from BAAI are optimized for retrieval and search. See the repository for the prefix required for queries.