# Sentence vectorization

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 Portuguese-English sentence similarity model fine-tuned based on multilingual-e5-small, capable of mapping sentences to a 384-dimensional vector space.

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.

A Dutch sentence embedding model based on the RobBERT model, which maps text to a 768-dimensional vector space, suitable for semantic search and text similarity calculation

A Chinese text vectorization model trained based on the CoSENT method, supporting tasks such as sentence embedding, text matching, and 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 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 clustering or semantic search.

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 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.

Korean sentence similarity model trained on the Sentence-transformers framework, used to measure semantic coherence between sentences

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 sentence embedding model fine-tuned based on the RobBERT v2 Dutch model, specifically optimized for Dutch FAQ question-answering pairs.

A Spanish-English bilingual model based on sentence-transformers that maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks

Basic Sentence BERT model for Japanese, fine-tuned based on BERT model, used for sentence similarity calculation

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.

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

This is a sentence-transformers model fine-tuned on Spanish semantic similarity datasets, capable of mapping text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

This is a small sentence transformer model based on DistilBERT, supporting Hindi-English mixed language (Hinglish), capable of mapping text to a 768-dimensional vector space.

This is a small BERT-based Hinglish (Hindi-English mixed) sentence transformer model that maps text to a 768-dimensional 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 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 sentence embedding model based on sentence-transformers that can map text to a 512-dimensional vector space, suitable for tasks such as semantic search and text similarity calculation.

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 BERT-based sentence transformer model specifically optimized for Hinglish (Hindi-English mixed language), capable of mapping sentences into a 768-dimensional vector space