
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...
Rerank a list of candidate documents for a text query. Accepts a text query and a list of documents, and returns the top...
Score the relevance between query and passage text pairs for retrieval reranking. Accept a JSON string of one or more [q...
Embed text into 128β768 dimensional vectors for semantic search, retrieval-augmented generation (RAG), and similarity. A...
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...
Score semantic similarity between sentences. Input a list of sentences and return one numeric similarity score per sente...
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....