lucataco/nomic-embed-text-v1
Convert text into vector embeddings for semantic search, retrieval-augmented generation, clustering, and classification....
Found 31 models (showing 1-20)
Convert text into vector embeddings for semantic search, retrieval-augmented generation, clustering, and classification....
Embed text into 1024-dimensional vectors for semantic search, dense retrieval, and RAG pipelines. Takes a text prompt an...
Rerank query–document pairs for information retrieval and RAG. Accepts text inputs as one or many [query, passage] pairs...
Convert text into dense vector embeddings for semantic search, information retrieval, semantic textual similarity, reran...
Re-rank query–candidate text pairs for search and RAG by returning a relevance score per pair. Takes a JSON-encoded list...
Generate query embeddings from short English text for passage retrieval and semantic search. Accepts an array of query s...
Score relevance between text pairs for re-ranking search results and RAG pipelines. Takes a JSON-formatted list of (quer...
Rerank documents for a text query, returning the top-k most relevant texts with relevance scores. Accepts a text query a...
Score relevance between queries and passages for retrieval re-ranking. Accept a JSON string containing one or many [quer...
Convert text into 128–768-dimensional embeddings for semantic search, retrieval-augmented generation (RAG), classificati...
Re-rank text passages by scoring the relevance of text pairs. Accepts a JSON array of [query, document] string pairs and...
Generate instruction-tuned text embeddings for documents and queries, with an optional relevance score for query–documen...
Generate multilingual text embeddings and a relevance score from a query and a passage. Takes a text query and a text pa...
Compute semantic similarity scores for input sentences. Takes a list of sentences, encodes them into sentence embeddings...
Convert English text into embeddings for semantic search and long‑document retrieval. Accepts English text and returns d...
Convert multilingual text into dense vector embeddings for retrieval and semantic search. Accept one or more texts as in...
Convert English text into vector embeddings for semantic search, information retrieval, and reranking. Accepts a single...
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 query–passage relevance score for semantic search, retrieval, and reranking....