# 768-dimensional vector space

This is a multilingual sentence embedding model that can map sentences and paragraphs to a 768-dimensional vector space, suitable for tasks such as clustering and semantic search.

A Portuguese sentence encoder based on sentence-transformers that can map text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

This is a Hebrew 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.

ONNX version of the Dutch sentence transformer based on the RobBERT model, mapping text to a 768-dimensional vector space, suitable for semantic search and clustering tasks

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

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

This is a sentence embedding model based on IndoBERT, fine-tuned using Indonesian Natural Language Inference datasets, capable of mapping text to a 768-dimensional vector space.

This is a multilingual sentence embedding model based on the ConGen framework, which maps sentences to a 768-dimensional vector space, suitable for tasks such as semantic search.

Marathi sentence similarity model fine-tuned on STS dataset, supporting sentence embeddings and semantic similarity calculation

This is a multilingual sentence embedding model based on sentence-transformers, supporting English, German, and Italian, capable of mapping text into a 768-dimensional vector space.

This is an Italian sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

A BERTimbau-based Portuguese sentence similarity calculation model that maps sentences and paragraphs to a 768-dimensional vector space

Italian sentence embedding model based on sentence-transformers, mapping text to a 768-dimensional vector space, suitable for semantic search and clustering tasks

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

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

This is a multilingual sentence transformation model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional vector space, suitable for sentence similarity and feature extraction 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.

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