# Sentence Vectorization

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

A sentence embedding model based on sentence-transformers that maps text to a 384-dimensional vector space, suitable for semantic search and text similarity calculation

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 sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for semantic similarity and feature extraction 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 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a lightweight sentence embedding model based on the BERT architecture, capable of converting sentences and paragraphs into 128-dimensional dense vectors, suitable for tasks such as semantic similarity calculation.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or 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.

A multilingual medical text sentence embedding model based on sentence-transformers, supporting 8 languages

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 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, clustering, and semantic search.

This is a model based on sentence-transformers that can map 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 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 and semantic search.

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

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

A sentence embedding model for the economics domain based on sentence-transformers, capable of mapping text to a 768-dimensional vector space

This is a sentence transformer model that maps text to a dense vector space, suitable for semantic search and clustering tasks.

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 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, clustering, and semantic search.

This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for semantic similarity and feature extraction tasks.

This is a model based on sentence-transformers that can map 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 can map 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 can map 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 maps sentences and paragraphs into a 768-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 can map 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, capable of mapping sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

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

A multilingual sentence embedding model based on sentence-transformers, specially optimized for Bengali, suitable for semantic similarity calculation and clustering analysis

This is a model based on sentence-transformers that can map sentences and paragraphs into a 16-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 sentence similarity calculation and semantic search.