Update README.md

README.md

@@ -19,7 +19,7 @@ An intriguing aspect of adapting T-pro-it-1.0 is that this model was obtained th
 
 <img src="https://cdn-uploads.huggingface.co/production/uploads/652cedbdf120598322ae358a/sKwHvA9ztd7rHx37Ca2ey.png" style="display: block; margin: 0 auto; max-width: 50%; height: auto;">
 
-For adaptation, we use sampling from a combination of the open datasets HuggingFaceFW/fineweb-2 and IlyaGusev/rulmIlyaGusev/rulm. The study of the impact of data volume and quality on the current process is ongoing.
+For adaptation, we use sampling from a combination of the open datasets **HuggingFaceFW/fineweb-2** and **IlyaGusev/rulm**. The study of the impact of data volume and quality on the current process is ongoing.
 
 ## Papers
 Tikhomirov M., Chernyshov D. Facilitating Large Language Model Russian Adaptation with Learned Embedding Propagation //Journal of Language and Education. – 2024. – Т. 10. – №. 4. – С. 130-145. (Preprint: https://arxiv.org/abs/2412.21140)