# Multilingual sentence embeddings

This is a fine-tuned sentence transformer model based on xlm-roberta-base, supporting English and Ukrainian, suitable for tasks like semantic textual similarity and semantic search.

This is a fine-tuned sentence transformer model on measurement domain datasets, used for generating semantic embedding vectors, supporting tasks like semantic text similarity and semantic search.

Jina Embeddings V3 is a multilingual sentence embedding model supporting over 100 languages, focusing on sentence similarity calculation and feature extraction tasks.

Jina Embeddings V3 is a multilingual sentence embedding model supporting over 100 languages, focusing on sentence similarity calculation and feature extraction tasks.

A sentence-transformers model based on XLM-R, trained using the SimCSE method, which maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

A multilingual sentence embedding model based on sentence-transformers, supporting Catalan, capable of mapping text to a 768-dimensional vector space

SONAR 200 Text Encoder is a multilingual text embedding model that supports sentence similarity computation for 202 languages.

This is a sentence-transformers based model for mapping Sinhala sentences into a 768-dimensional vector space, supporting tasks like sentence similarity calculation and semantic search.

This is a multilingual sentence embedding model based on sentence-transformers, capable of mapping text to a 1024-dimensional vector space, suitable for semantic search and clustering tasks.

This is a multilingual sentence embedding model that maps text to a 512-dimensional vector space, suitable for semantic search and clustering tasks.

m-ST5 is a multilingual sentence embedding model based on the mT5 encoder, specifically optimized for cross-lingual semantic textual similarity and sentence retrieval tasks.

LEALLA is a collection of lightweight, language-agnostic sentence embedding models supporting 109 languages, distilled from LaBSE. Suitable for multilingual sentence embeddings and bilingual text retrieval.

This is a BERT-based uncased Italian model specifically designed for sentence and paragraph vector representations, suitable for semantic search and clustering tasks.

Multilingual sentence embedding model that maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks

LaBSE is a multilingual sentence embedding model based on BERT, supporting 109 languages, suitable for sentence similarity calculation and bilingual text retrieval.

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.

A multilingual sentence embedding model optimized for Bengali, mapping text to a 768-dimensional vector space

This is a multilingual sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks such as sentence similarity computation and semantic search.

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.

A multilingual sentence encoder based on BERT architecture, supporting sentence embeddings and similarity computation for 15 languages, serving as a distilled version of the original LaBSE model.

A sentence embedding model supporting English and Italian, used for generating vector representations of sentences.

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.

LaBSE is a BERT-based multilingual sentence embedding model that supports 109 languages, suitable for sentence similarity calculation and bilingual text retrieval.

Multilingual sentence embedding model based on XLM-RoBERTa architecture, supporting semantic similarity calculation and search tasks for English and French

This is a multilingual sentence transformer model capable of mapping sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a multilingual sentence embedding model that maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.

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.

A 101-language sentence representation model based on multilingual BERT, generating sentence representations through mean-pooling of word embeddings