Update README.md

README.md

@@ -23,12 +23,12 @@ In this work, we introduce *ConTEB* (Context-aware Text Embedding Benchmark), a
 We further find chunks embedded with our method are more robust to suboptimal chunking strategies and larger retrieval corpus sizes.
 We open-source all artifacts here and at https://github.com/illuin-tech/contextual-embeddings.
 
-## Models
+## Ressources
 
-- [*(Model) ModernBERT*](TODO) The Contextualized ModernBERT bi-encoder trained with InSENT loss and Late Chunking
-- [*(Model) ModernColBERT*](TODO) The Contextualized ModernColBERT trained with InSENT loss and Late Chunking
-- [*Leaderboard*](TODO)
-- [*(Data) ConTEB Benchmark Datasets*](TODO)
+- [*(Model) ModernBERT*](TODO): The Contextualized ModernBERT bi-encoder trained with InSENT loss and Late Chunking
+- [*(Model) ModernColBERT*](TODO): The Contextualized ModernColBERT trained with InSENT loss and Late Chunking
+- [*Leaderboard*](TODO): Coming Soon
+- [*(Data) ConTEB Benchmark Datasets*](TODO):
 - [*(Code) Contextual Document Engine*](https://github.com/illuin-tech/contextual-embeddings): The code used to train and run inference with our architecture.
 - [*(Code) ConTEB Benchmarkk*](https://github.com/illuin-tech/conteb): A Python package/CLI tool to evaluate document retrieval systems on the ConTEB benchmark.
 - [*Blog*](https://huggingface.co/XXX): TODO