andreasjansson/llama-2-13b-embeddings
Convert text prompts into dense vector embeddings. Accept a single string containing up to 100 prompts separated by a cu...
Found 42 models (showing 21-40)
Convert text prompts into dense vector embeddings. Accept a single string containing up to 100 prompts separated by a cu...
Convert text into 768-dimensional embeddings for semantic search, retrieval, clustering, and similarity matching. Accept...
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...
Convert multilingual text into dense vector embeddings for semantic search, cross-lingual similarity, and retrieval. Acc...
Convert text prompts into vector embeddings for semantic search, clustering, classification, and retrieval. Accept a bat...
Generate vector embeddings from one or more text prompts for semantic search, retrieval, clustering, and RAG. Accepts mu...
Compute multilingual text embeddings and a query–passage relevance score for semantic search, retrieval, and reranking....
Generate English monolingual embeddings from text inputs, supporting sequence lengths up to 8192. This model, based on a...
Convert text into dense vector embeddings for semantic search, similarity, clustering, classification, and retrieval-aug...
Generate text embeddings for semantic search, retrieval, clustering, and reranking. Accepts batches of texts and returns...
Generate sentence embeddings from one or multiple text inputs. Return dense vector representations for semantic search,...
Embed images and text into a shared CLIP vector space for similarity search, cross-modal retrieval, and zero-shot classi...
Convert text in many languages into vector embeddings for cross-lingual semantic search and retrieval. Accept a list of...
Generate multilingual text embeddings and a query–passage relevance score from text input. Accepts a query and a passage...
Generate dense text embeddings from a JSONL dataset for semantic search, retrieval, and RAG workflows. Accept a JSONL fi...
Generate multilingual text similarity scores for retrieval by encoding two newline-separated lists of texts and returnin...