Update README.md

README.md

@@ -132,7 +132,7 @@ Contrary to BERT, the masking is done dynamically during pretraining (e.g., it c
 
 ### Pretraining
 
-The model was trained on TPUv3-8 VM, sponsored by the Google TPU Research Cloud, for 2 epochs with a sequence length of 128 and continuing for one more epoch with a sequence length of 512. The optimizer used is Adafactor with a learning rate of 2e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), learning rate warmup for 1500 steps and linear decay of the learning rate after.
+The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 2 epochs with a sequence length of 128 and continuing for one more epoch with a sequence length of 512. The optimizer used is Adafactor with a learning rate of 2e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), learning rate warmup for 1500 steps and linear decay of the learning rate after.
 
 ## Evaluation results
 
@@ -149,6 +149,8 @@ To conclude, this model improves on our previous [Finnish RoBERTa-large](https:/
 
 ## Team Members
 
-- Aapo Tanskanen ([aapot](https://huggingface.co/aapot))
-- Rasmus Toivanen ([RASMUS](https://huggingface.co/RASMUS))
-- Tommi Vehviläinen ([Tommi](https://huggingface.co/Tommi))
+- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
+- Rasmus Toivanen [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
+- Tommi Vehviläinen [Hugging Face profile](https://huggingface.co/Tommi)
+
+Feel free to contact us for more details 🤗