M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Understand

In this paper, we introduce a new embedding model called M3-Embedding, which is distinguished for its versatility in \textit{Multi-Linguality}, \textit{Multi-Functionality}, and \textit{Multi-Granularity}.

• It provides a uniform support for the semantic retrieval of more than 100 working languages.
• It can simultaneously accomplish the three common retrieval functionalities: dense retrieval, multi-vector retrieval, and sparse retrieval.
• Besides, it is also capable of processing inputs of different granularities, spanning from short sentences to long documents of up to 8,192 tokens.