Update README.md

README.md

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 ## Uses and limitations
 ### Intended use
+This reward model was developed primarily for commercial purposes. It learns an inner representation of response quality rated by humans that can be used to conduct best-of-N sampling and Reinforcement Leanring with the PPO framework.
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+In addition to scientific uses, you may also further fine-tune and adapt this reward model for deployment, as long as your use is in accordance with the cc-by-nc-4.0 license, i.e. non-commercial use. This model works with the Transformers Library. If you decide to this pre-trained reward model as a basis for your fine-tuned model, please note that you need to conduct your own risk and bias assessment.
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 ### Out-of-scope use
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+This reward model is **not** intended for deployment as-is. It is not a product and cannot be used for human-facing interactions without supervision.
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+This model **has not** been optimised for common reward-model objectives such as harmfulness, truthfulness and helpfulness, it is only trained based on user actions present on the Chai mobile app platform. Therefore, this model will **not** rank responses appropriately when evaluating on common open-sourced datasets. All base model responses within the training data were generated using an in-house variant of [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B), therefore the model performance may degrade when the input is generated using other language models.
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 ### How to use
 
 This reward model can be loaded using the `AutoModelForSequenceClassification` functionality, with a GPT2 tokenizer where the `pad_token_id` is set to the EOS token id, padding sides need to be set according to the configurations used during model training.