Update README.md

README.md

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 Welcome to the LCO-Embedding project - Scaling Language-centric Omnimodal Representation Learning.
 
 ### Highlights:
-- We introduce LCO-Embedding, a language-centric omnimodal representation learning method and the LCO-Embedding model families, setting a new state-of-the-art on MIEB (Massive Image Embedding Benchmark) while supporting audio and videos.
-- We introduce Generation-Representation Scaling law, and connect models' generative capabilities and their representation upper bound.
-- We introduce SeaDoc, a challenging visual document retrieval task in southeast Asian languages; and show that continual generative pretraining before contrastive learning raises the representation upper bound.
+- We introduce **LCO-Embedding**, a language-centric omnimodal representation learning method and the LCO-Embedding model families, setting a new state-of-the-art on MIEB (Massive Image Embedding Benchmark) while supporting audio and videos.
+- We introduce the **Generation-Representation Scaling Law**, and connect models' generative capabilities and their representation upper bound.
+- We introduce **SeaDoc**, a challenging visual document retrieval task in Southeast Asian languages, and show that continual generative pretraining before contrastive learning raises the representation upper bound.
 
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