Update README.md

README.md

@@ -41,7 +41,7 @@ Apart from that, one can also use PairRM to further align instruction-tuned LLMs
 Unlike the other RMs that encode and score each candidate respectively, 
 PairRM takes a pair of candidates and compares them side-by-side to indentify the subtle differences between them.
 Also, PairRM is based on [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large), and thus it is super efficient: 0.4B.
-We trained PairRM on a diverse collection of human preference datasets such as `[UltraFeedback`](https://huggingface.co/datasets/openbmb/UltraFeedback), [`HH-RLHF`](https://huggingface.co/datasets/Anthropic/hh-rlhf),
+We trained PairRM on a diverse collection of human preference datasets such as [`UltraFeedback`](https://huggingface.co/datasets/openbmb/UltraFeedback), [`HH-RLHF`](https://huggingface.co/datasets/Anthropic/hh-rlhf),
 [`summarize_from_feedback`](https://huggingface.co/datasets/openai/summarize_from_feedback), [`chatbot-arena`](), etc.
 PairRM is part of the LLM-Blender project (ACL 2023). Please see our paper linked above to know more.