# Multilingual embeddings

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

This is a sentence-transformers model fine-tuned on a Korean triplet dataset based on Alibaba NLP/gte-multilingual-base, designed for semantic textual similarity tasks.

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

Solon embeddings base version 0.1 is the most advanced open-source French embedding model, supporting both French and English, specializing in sentence similarity and feature extraction tasks.

MMLW (I Must Get Better News) is a Polish neural text encoder optimized for information retrieval tasks, capable of converting queries and passages into 768-dimensional vectors.

This is a Portuguese sentence embedding model based on the DistilBERT architecture, specifically optimized for semantic similarity tasks.

This is a Portuguese sentence transformer model based on DistilBERT, specifically designed for sentence similarity and semantic search tasks.

A pre-trained model for detecting unassimilated English lexical borrowings in Spanish news, capable of identifying foreign words such as 'fake news' and 'machine learning'.

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 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