reward-model-deberta-v3-large-v2
Maintainer: OpenAssistant
182
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
The reward-model-deberta-v3-large-v2 is a model trained by OpenAssistant to predict which generated answer is better judged by a human, given a question. This reward model (RM) can be useful for evaluating QA models, serving as a reward score in RLHF, and detecting potential toxic responses via ranking. The model was trained on datasets including webgpt_comparisons, summarize_from_feedback, synthetic-instruct-gptj-pairwise, and anthropic_hh-rlhf.
Model inputs and outputs
Inputs
- Question: The question to be answered
- Answer: The generated answer to be evaluated
Outputs
- Score: A score indicating the quality of the generated answer
Capabilities
The reward-model-deberta-v3-large-v2 can be used to evaluate the quality of generated answers for a given question. It can help determine which answer is better, which can be useful for improving QA models or detecting potential toxic responses.
What can I use it for?
The reward-model-deberta-v3-large-v2 model can be used for a variety of applications, such as:
- QA model evaluation: Use the model to score and compare the quality of answers generated by different QA models.
- Reward score in RLHF: Leverage the model's scoring mechanism as a reward signal to fine-tune language models using Reinforcement Learning from Human Feedback (RLHF).
- Toxic response detection: Use the model to detect potentially toxic or harmful responses by comparing the scores of different candidate responses.
Things to try
One key insight about the reward-model-deberta-v3-large-v2 model is its ability to detect and score the quality of generated answers. This can be particularly useful for improving QA systems or identifying potentially toxic responses, which can help ensure the safe and responsible deployment of language models.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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