Update README.md

README.md

@@ -41,13 +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 six human-preference datasets: 
-- [`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_conversations`](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations)
-- [`webgpt_comparisons`](https://huggingface.co/datasets/openai/webgpt_comparisons)
-- [`instruct-synthetic-prompt-responses`](https://huggingface.co/datasets/Dahoas/instruct-synthetic-prompt-responses).
+We trained PairRM on a diverse collection of six human-preference datasets (see more [here](https://huggingface.co/llm-blender/PairRM#training-datasets)).
 
 PairRM is part of the LLM-Blender project (ACL 2023). Please see our [paper](https://arxiv.org/abs/2306.02561) above to know more.