BGE-M3 - AI Embedding Models Tool

Overview

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

Key Features

• Dense retrieval embeddings
• Multi-vector retrieval support
• Sparse retrieval support
• Text embeddings for short and long inputs
• Handles inputs up to 8192 tokens
• Supports over 100 languages
• From Beijing Academy of Artificial Intelligence

Ideal Use Cases

• Semantic search across multilingual corpora
• Embedding and retrieval for long documents
• Multi-vector retrieval for complex queries
• Sparse retrieval or hybrid search pipelines
• Clustering and semantic similarity analysis
• Cross-lingual matching and retrieval

Getting Started

• Open the model page: https://huggingface.co/BAAI/bge-m3
• Read the model card and usage instructions on Hugging Face
• Download or pull the model from the repository
• Load the model into your embedding or inference framework
• Generate embeddings and integrate them into your retrieval pipeline

Pricing

Pricing not disclosed. Check the Hugging Face model page or provider for licensing and usage costs.

Key Information

• Category: Embedding Models
• Type: AI Embedding Models Tool