# Sentence embedding

This is an 80M parameter M2-BERT pre-trained checkpoint with a sequence length of 2048, fine-tuned for long-context retrieval tasks.

This is a large Portuguese legal domain sentence transformer model based on BERTimbau, specifically designed for semantic similarity tasks in legal texts.

This is a model based on sentence-transformers that can map sentences and paragraphs to a 512-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 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.

A lightweight sentence embedding model based on the MiniLM architecture that can map text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.

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

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.

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.