BGE-M3 - AI Embedding Models Tool

Overview

BGE-M3 is an embedding model from the Beijing Academy of Artificial Intelligence that supports dense, multi-vector, and sparse retrieval for text embeddings. It covers over 100 languages and accepts inputs up to 8192 tokens; see the model page on Hugging Face for details.

Key Features

• Supports dense, multi-vector, and sparse retrieval for text embeddings
• Works in over 100 languages
• Handles inputs up to 8192 tokens
• Suitable for short sentences to long documents
• Designed for retrieval and embedding workflows

Ideal Use Cases

• Semantic search across multilingual document collections
• Dense retrieval in search and question-answering pipelines
• Multi-vector retrieval for composite or segmented documents
• Sparse retrieval integration with inverted-index systems
• Embedding long documents up to 8192 tokens for retrieval
• Clustering and semantic similarity on multilingual corpora

Getting Started

• Open the model page on Hugging Face
• Read the README and available usage examples
• Select a retrieval mode: dense, multi-vector, or sparse
• Prepare and tokenize texts, keeping inputs within 8192 tokens
• Integrate embeddings into your search or analytic pipeline
• Test retrieval with representative queries and measure relevance

Pricing

No pricing information is provided in the supplied model metadata; check the Hugging Face model page for hosting or usage costs.

Key Information

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