Update README.md

README.md

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 ---
 license: mit
+datasets:
+- openai/summarize_from_feedback
+- openai/webgpt_comparisons
+- Dahoas/instruct-synthetic-prompt-responses
+language:
+- en
+metrics:
+- accuracy
+tags:
+- reward-model
+- reward_model
+- RLHF
 ---
+# Reward model trained from human feedback
+
+Reward model (RM) trained to predict which generated answer is better judged by a human, given a question.
+
+RM are useful in these domain:
+
+- QA model evaluation
+
+- serves as reward score in RLHF 
+
+
+All models are train on these dataset with a same split seed across datasets (if validation split wasn't available)
+
+- [webgpt_comparisons](https://huggingface.co/datasets/openai/webgpt_comparisons)
+
+- [summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback)
+
+- [synthetic-instruct-gptj-pairwise](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise)
+
+
+# Performance
+
+Validation split accuracy
+
+| Model  | [WebGPT](https://huggingface.co/datasets/openai/webgpt_comparisons)  | [Summary](https://huggingface.co/datasets/openai/summarize_from_feedback)  | [SytheticGPT](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise)  |
+|---|---|---|---|
+| [electra-large-discriminator](https://huggingface.co/OpenAssistant/reward-model-electra-large-discriminator)  | 59.30  | 68.66  | 99.85  |
+| [deberta-v3-large](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large) | 61.13  | 72.23  | 99.94  |
+| [deberta-v3-base](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-base)  | 59.07  | 66.84  | 99.85  |
+
+Its likely SytheticGPT has somekind of surface pattern on the choosen-rejected pair which makes it trivial to differentiate between better the answer.