
lucataco/nomic-embed-text-v1
Convert text into vector embeddings for semantic search, retrieval, clustering, and classification. Accept a newline-sep...
Found 29 models (showing 1-20)
Convert text into vector embeddings for semantic search, retrieval, clustering, and classification. Accept a newline-sep...
Convert text into 1024-dimensional embeddings for semantic search and retrieval. Takes a text prompt and returns a dense...
Rerank passages for a text query. Takes a query and one or more candidate documents (text) and outputs a relevance score...
Convert text into embeddings for semantic search and retrieval. Takes a text string and returns a 768-dimensional embedd...
Score relevance between text pairs for reranking search results and retrieval-augmented generation. Takes a JSON list of...
Generate text query embeddings for dense retrieval and semantic search. Accepts an array of short query strings and retu...
Score relevance between pairs of texts to rerank search results, passages, or candidate answers. Accepts a list of text...
Re-rank candidate documents by relevance to an English text query. Accepts a query string and a list of documents; retur...
Score the relevance between query and passage text pairs for retrieval reranking. Accept a JSON string of one or more [q...
Convert text into 128β768-dimensional embeddings for semantic search, retrieval, and clustering. Accepts text input (up...
Rerank text pairs by semantic relevance and output a numeric score for each pair. Accepts an array of [query, document]...
Convert text into embeddings for semantic search and retrieval. Accepts a document string and optionally a query and a o...
Compute semantic relevance between a query and a passage and generate multilingual text embeddings. Takes a text query a...
Rank sentences by semantic similarity to a query sentence. Accept a list of text sentences (first item as the query) and...
Convert English text into dense vector embeddings for semantic search, long-document retrieval, RAG, reranking, and reco...
Convert multilingual text into 1024-dimensional embeddings for semantic search, passage retrieval, clustering, and RAG....
Convert English text into dense vector embeddings for semantic search, information retrieval, semantic similarity, and r...
Convert English text to 1024-dimensional embeddings for semantic search, retrieval-augmented generation (RAG), similarit...
Embed English text into dense vectors for semantic search, retrieval, reranking, recommendation, and RAG. Process long d...
Compute multilingual text embeddings and a queryβpassage relevance score for semantic search, retrieval, and reranking....