# Semantic similarity

SILMA Arabic Matryoshka Embedding Model 0.1 is an advanced Arabic text embedding model that uses innovative matryoshka embedding technology to optimize text representation in different dimensions, balancing speed, storage, and accuracy.

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.

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 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 sentence-transformers based model that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like sentence similarity computation and semantic search.

A patent parameter sentence recognition model based on all-MiniLM-L6-v2, which maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks like clustering or semantic search.

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

This model is a fine-tuned version of the Portuguese BERT base model (neuralmind/bert-base-portuguese-cased), with unspecified specific tasks

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-transformers based model that maps sentences and paragraphs to a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search.

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

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.

This is a sentence embedding model based on sentence-transformers that can convert text into a 768-dimensional vector representation, suitable for tasks such as semantic similarity and text clustering.

A RoBERTa Chinese model trained with the WECHSEL method, achieving efficient cross-lingual transfer from English to Chinese

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 sentence-transformers model based on XLM-R, specifically optimized for Thai language, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

This is a sentence embedding model based on sentence-transformers, which can map text to a 256-dimensional vector space and is suitable for semantic search and clustering tasks.

This is a small BERT-based Hinglish (Hindi-English mixed) sentence transformer model that maps text to a 768-dimensional vector space