# Multilingual sentence embedding

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.

Snowflake Arctic Embed M v2.0 is a multilingual sentence embedding model focused on sentence similarity tasks, supporting over 50 languages.

This is a multilingual sentence embedding model supporting English, Spanish, and Portuguese, capable of mapping text to a 384-dimensional vector space, suitable for tasks such as semantic similarity calculation.

This is a sentence transformer model fine - tuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2, which can map text to a 384 - dimensional vector space for tasks such as semantic similarity calculation.

The GTE multilingual base model is a dense sentence transformer that supports sentence similarity calculation and text embedding tasks in multiple languages.

This is a sentence-transformers model fine-tuned on intfloat/multilingual-e5-small, designed to map sentences and paragraphs into a 384-dimensional dense vector space, supporting tasks such as semantic text similarity and semantic search.

LaBSE is a multilingual sentence embedding model that maps sentences from 109 languages into a shared vector space.

SPhilBERTa is a sentence transformer model specifically designed for classical philology, used to identify cross-language citations between Latin and Ancient Greek texts.

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

LEALLA-small is a lightweight, language-agnostic sentence embedding model that supports 109 languages, suitable for multilingual sentence embedding and bilingual text retrieval tasks.

This model is a fine-tuned version of paraphrase-multilingual-mpnet-base-v2, supporting English and German sentence similarity calculation, suitable for multilingual text embedding tasks.

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.

A dual-tower network model trained for zero-shot and few-shot text classification, supporting multilingual sentence embedding