# 768-dimensional vector

Nomic-embed-text-v1.5 is a text embedding model developed by Nomic AI, based on the sentence-transformers library, focusing on sentence similarity tasks.

This is a Korean 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 clustering or semantic search.

A SimCSE model based on mBERT, specifically trained for Thai language, used to generate 768-dimensional vector representations of sentences and paragraphs

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 clustering or 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 sentence embedding model based on sentence-transformers, which can convert text into a 768-dimensional vector representation.

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

This is a sentence similarity model based on sentence-transformers, which can map text to a 768-dimensional vector space and is suitable for tasks such as semantic search and text clustering.

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 clustering and semantic search.

This is a sentence embedding model based on sentence-transformers, which can convert text into a 768-dimensional dense vector representation.

This is a Korean sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

This is a Korean sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

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

This is a Korean sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

A Chinese text embedding model based on the CoSENT (Cosine Sentence) model, which can map sentences to a 768-dimensional dense vector space and is suitable for tasks such as sentence embedding, text matching, or 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 that can map sentences and paragraphs to a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.