BGE-M3 - AI Embedding Models Tool

Overview

BGE-M3 is a production-ready embedding model from the Beijing Academy of Artificial Intelligence (BAAI) designed for retrieval and semantic search across a very wide set of languages and document lengths. The model supports dense, multi-vector, and sparse retrieval paradigms, making it suitable for single-vector semantic search, segment-level (multi-vector) retrieval for long documents, and hybrid sparse/dense pipelines. BGE-M3 is advertised to handle inputs up to 8,192 tokens and cover more than 100 languages, enabling cross-lingual search, long-form document retrieval, and downstream tasks such as clustering or semantic similarity. BGE-M3 is hosted on Hugging Face, where it has seen substantial community adoption (millions of downloads and thousands of likes), and is distributed under an MIT license. The model is exposed through Hugging Face inference endpoints and can be used via the sentence-similarity pipeline or the Hugging Face Inference API for embeddings, allowing easy integration into retrieval-augmented-generation (RAG), search, and analytics systems (see the model page for details and changelogs) (source: https://huggingface.co/BAAI/bge-m3).

Model Statistics

• Downloads: 7,163,243
• Likes: 2644
• Pipeline: sentence-similarity

License: mit

Model Details

Architecture and parameters: The public model card does not disclose a base backbone or total parameter count; the model card lists the base model field as None. The model is presented primarily as an embedding engine rather than a language-generation model (source: https://huggingface.co/BAAI/bge-m3). Embedding capabilities and retrieval modes: BGE-M3 provides three retrieval modes: dense (single-vector) embeddings for semantic search, multi-vector embeddings where a document can be represented by multiple vectors for fine-grained matching, and sparse retrieval compatible with sparse index approaches. These modes allow flexibility for common retrieval patterns — e.g., single-vector for short-query similarity, multi-vector for long documents broken into segments, and hybrid sparse/dense systems for precision and recall tradeoffs (source: https://huggingface.co/BAAI/bge-m3). Input size and multilingual support: The model supports inputs up to 8,192 tokens and is designed to handle over 100 languages, making it suitable for long-form and multilingual retrieval applications (source: https://huggingface.co/BAAI/bge-m3). Integration and licensing: BGE-M3 is available through Hugging Face pipelines (pipeline type: sentence-similarity) and the Hugging Face Inference API, and is released under the permissive MIT license (source: https://huggingface.co/BAAI/bge-m3).

Key Features

• Supports dense, multi-vector, and sparse retrieval modes for flexible indexing strategies
• Handles long inputs up to 8,192 tokens for document-level and paragraph-level embeddings
• Multilingual support across 100+ languages for cross-lingual retrieval
• Available through Hugging Face pipelines and Inference API for easy integration
• Permissive MIT license enabling commercial and research use

Example Usage

Example (python):

from huggingface_hub import InferenceClient

# Create an Inference client (optionally set HF_TOKEN in your environment)
client = InferenceClient()

# Single text -> embedding
resp = client.text_embeddings(model="BAAI/bge-m3", inputs="The quick brown fox jumps over the lazy dog")
embeddings = resp[0]["embedding"]
print("Embedding length:", len(embeddings))

# Batch embeddings
texts = ["Document one text.", "Another document text."]
batch_resp = client.text_embeddings(model="BAAI/bge-m3", inputs=texts)
batch_embeddings = [r["embedding"] for r in batch_resp]
print("Batch embeddings retrieved:", len(batch_embeddings))

# Example note: for multi-vector retrieval, break long documents into segments first,
# then request embeddings per segment and store multiple vectors per document in your index.

Benchmarks

Hugging Face downloads: 7,163,243 (Source: https://huggingface.co/BAAI/bge-m3)

Hugging Face likes: 2644 (Source: https://huggingface.co/BAAI/bge-m3)

Pipeline (HF): sentence-similarity (Source: https://huggingface.co/BAAI/bge-m3)

Maximum input length: 8192 tokens (Source: https://huggingface.co/BAAI/bge-m3)

Languages supported: 100+ (Source: https://huggingface.co/BAAI/bge-m3)

License: MIT (Source: https://huggingface.co/BAAI/bge-m3)

Key Information

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