Update README.md

README.md

@@ -9,6 +9,8 @@ fine-tuning: true
 tags:
 - nvidia
 - llama2
+datasets:
+- Anthropic/hh-rlhf
 ---
 
 # Llama2-13B-RLHF-RM 
@@ -24,8 +26,4 @@ Llama2-13B-RLHF-RM is trained with NVIDIA [NeMo Aligner](https://github.com/NVID
 
 ## Usage:
 
-Training a reward model is an essential component of Reinforcement Learning from Human Feedback (RLHF). By developing a strong reward model, we can mitigate the risks of reward hacking and ensure that the actor is incentivized to produce helpful responses. We are open-sourcing this reward model so that users can seamlessly integrate it with Proximal Policy Optimization (PPO) training using [NeMo Aligner](https://github.com/NVIDIA/NeMo-Aligner). For detailed instructions on how to conduct the training, please refer to our [RLHF training user guide](https://github.com/NVIDIA/NeMo-Aligner/blob/main/docs/user-guide/RLHF.rst). 
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+Training a reward model is an essential component of Reinforcement Learning from Human Feedback (RLHF). By developing a strong reward model, we can mitigate the risks of reward hacking and ensure that the actor is incentivized to produce helpful responses. We are open-sourcing this reward model so that users can seamlessly integrate it with Proximal Policy Optimization (PPO) training using [NeMo Aligner](https://github.com/NVIDIA/NeMo-Aligner). For detailed instructions on how to conduct the training, please refer to our [RLHF training user guide](https://github.com/NVIDIA/NeMo-Aligner/blob/main/docs/user-guide/RLHF.rst).