# Semantic Search

The OpenSearch Semantic Highlighter is a text classifier designed to evaluate the relevance of document sentences to queries.

A cross-encoder model trained on the msmarco dataset using the sentence-transformers library, designed for text re-ranking and semantic search

This is a cross-encoder model fine-tuned from ModernBERT-base, designed for calculating scores of text pairs, suitable for text reordering and semantic search tasks.

This is a cross-encoder model fine-tuned from ModernBERT-base, designed for calculating scores of text pairs, suitable for text re-ranking and semantic search tasks.

This is a cross-encoder model fine-tuned on MiniLM-L12-H384-uncased for text re-ranking and semantic search tasks.

ko-reranker is a text ranking model fine-tuned with Korean data based on the BAAI/bge-reranker-large model.

This is a Korean-optimized sentence embedding model based on the multilingual-e5-base model, supporting multilingual text similarity computation and feature extraction.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 256-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

This is a sentence similarity model based on sentence-transformers, which maps text to a 384-dimensional vector space for semantic search and clustering tasks.

This is a sentence similarity calculation model based on sentence-transformers, capable of mapping text to a 768-dimensional vector space

This is a model based on sentence-transformers that can map sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a multilingual sentence transformer model that maps text to a dense vector space, supporting semantic search and clustering tasks

This is a model based on sentence-transformers that maps sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks like clustering or semantic search.

This is a sentence encoder model based on sentence-transformers, capable of mapping sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as semantic search and clustering.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional vector space for sentence similarity computation and semantic search tasks.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 256-dimensional vector space for tasks such as sentence similarity calculation and semantic search.

This is a sentence transformer model that maps sentences and paragraphs into a dense vector space, suitable for tasks such as clustering and semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs to a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 1536-dimensional dense vector space.

This is a sentence encoder model based on sentence-transformers, capable of mapping sentences and paragraphs into a 256-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

This is a sentence embedding model based on sentence-transformers, capable of converting text into 1024-dimensional vector representations, suitable for tasks such as semantic search and text similarity calculation.

This is a sentence embedding model based on sentence-transformers that maps text to a 1536-dimensional vector space, suitable for semantic search and clustering tasks.

This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 1024-dimensional dense vector space for tasks such as sentence similarity calculation and semantic search.

This is a sentence embedding model based on sentence-transformers, capable of mapping text to a 768-dimensional vector space, suitable for tasks such as semantic search and text clustering.

This is a sentence embedding model based on sentence-transformers, which can map text to a 1024-dimensional vector space and is suitable for semantic search and text similarity calculation.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering and semantic search.

This is a model based on sentence-transformers that can map sentences and paragraphs to a 384-dimensional vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a sentence encoder model based on sentence-transformers, capable of converting text into 768-dimensional vector representations, suitable for tasks such as semantic search and sentence similarity calculation.

This is a sentence transformer model adapted from facebook/dragon-plus-context-encoder, designed to map sentences and paragraphs into a 768-dimensional vector space, suitable for tasks such as clustering and semantic search.

This is an ONNX-based sentence transformer model that maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.

This is a sentence embedding model based on sentence-transformers, capable of converting text into 768-dimensional vector representations, suitable for tasks such as semantic search and text similarity calculation.