#embedding model

Explore a comprehensive toolkit of embedding models aimed at enhancing retrieval-augmented language models. Includes models for inference, fine-tuning, and evaluation across multiple languages. Achieve high performance in text and image retrieval with models like OmniGen for image generation. Follow the latest innovations such as MemoRAG and lightweight rerankers for efficiency. Access community support for continuous updates. Improve your NLP projects with easy installation and integration via the FlagEmbedding platform.

MS MARCO Web Search provides a vast dataset with millions of real user query-document interactions from ClueWeb22's 10 billion pages, aiding in research tasks like embedding models and retrieval systems. This resource supports studies across 93 languages, fostering advancements in machine learning and information retrieval.