Update README.md

README.md

@@ -18,7 +18,7 @@ DSE-Phi3-Docmatix-V2 is a bi-encoder model designed to encode document screensho
 The model, `Tevatron/dse-phi3-docmatix-v2`, is trained using 1/10 of the `Tevatron/docmatix-ir` dataset, a variant of `HuggingFaceM4/Docmatix` specifically adapted for training PDF retrievers with Vision Language Models in open-domain question answering scenarios. For more information on dataset filtering and hard negative mining, refer to the [docmatix-ir](https://huggingface.co/datasets/Tevatron/docmatix-ir/blob/main/README.md) dataset page.
 
 DSE has strong zero-shot effectiveness for document retrieval both with visual input and text input.
-For example, DSE-Phi3-Docmatix-V2 achieves 77.6 nDCG@5 on [ViDoRE](https://huggingface.co/spaces/vidore/vidore-leaderboard) leaderboard in **zero-shot setting** (without finetuning with ViDoRe training data).
+For example, DSE-Phi3-Docmatix-V2 achieves **77.6** nDCG@5 on [ViDoRE](https://huggingface.co/spaces/vidore/vidore-leaderboard) leaderboard in **zero-shot setting** (without finetuning with ViDoRe training data).
 
 
 ## How to Use the Model