Best AI Embedding Models Tools

Explore 3 AI embedding models tools to find the perfect solution.

Embedding Models

3 tools

BGE-M3

BGE-M3 is a versatile embedding model from the Beijing Academy of Artificial Intelligence that supports dense retrieval, multi-vector retrieval, and sparse retrieval for text embeddings. It is designed to work in over 100 languages and can handle inputs ranging from short sentences to long documents of up to 8192 tokens.

ModernBERT Embed

ModernBERT Embed is an embedding model derived from ModernBERT-base designed for generating sentence embeddings. It supports tasks such as sentence similarity and search through both full (768-d) and truncated (256-d) embedding outputs. The page provides comprehensive usage examples using SentenceTransformers, Transformers, and Transformers.js, indicating its integration into various frameworks.

jina-embeddings-v3

jina-embeddings-v3 is a multilingual multi-task text embedding model developed by Jina AI. Built on the Jina-XLM-RoBERTa architecture, it employs task-specific LoRA adapters to generate embeddings for various NLP tasks such as retrieval, classification, text-matching, and more. It supports rotary position embeddings for input sequences up to 8192 tokens and offers flexible, adjustable embedding dimensions.