# Lightweight embedding

A Korean sentence transformer fine-tuned based on intfloat/multilingual-e5-small for Korean retrieval tasks

This is a sentence-transformers model fine-tuned from intfloat/multilingual-e5-small, specifically optimized for Korean retrieval tasks, mapping text to a 384-dimensional vector space.

General-purpose Japanese text embedding model, optimized for retrieval tasks with excellent performance on CPUs

This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 128-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

A sentence embedding model fine-tuned on over 1 billion sentence pairs through self-supervised contrastive learning based on MiniLM-L12, capable of generating high-quality semantic vector representations

A sentence embedding model based on MiniLM architecture, trained on over 1 billion sentence pairs through self-supervised contrastive learning, capable of generating high-quality sentence vector representations

A streamlined version based on google/mt5-base, optimized for Russian and English with 58% fewer parameters

A sentence embedding model based on MiniLM-L12 architecture, trained on over 1 billion sentence pairs through contrastive learning, capable of generating high-quality semantic vector representations

This is a tiny Russian BERT model suitable for various natural language processing tasks.