readme: ---
  tags:
    - deberta-v3
  inference:
    parameters:
      function_to_apply: "none"
  widget:
  - text: "I cuddled with my dog today."
  ---
  
  # Utilitarian Deberta 01
  
  ## Model description
  
  This is a [Deberta model](https://huggingface.co/microsoft/deberta-v3-large) fine-tuned on for computing utility estimates of experiences, represented in first-person sentences. It was trained from human-annotated pairwise utility comparisons, from the [ETHICS dataset](https://arxiv.org/abs/2008.02275).
  
  ## Intended use
  
  The main use case is the computation of utility estimates of first-person text scenarios.
  
  ## Limitations
  
  The model was only trained on a limited number of scenarios, and only on first-person sentences. It does not have the capability of interpreting highly complex or unusual scenarios, and it does not have hard guarantees on its domain of accuracy. 
  
  ## How to use
  
  The model receives a sentence describing a scenario in first-person, and outputs a scalar representing a utility estimate. 
  
  ## Training data
  
  The training data is the train split from the Utilitarianism part of the [ETHICS dataset](https://arxiv.org/abs/2008.02275).
  
  ## Training procedure
  
  Training can be reproduced by executing the training procedure from [`tune.py`](https://github.com/hendrycks/ethics/blob/3e4c09259a1b4022607da093e9452383fc1bb7e3/utilitarianism/tune.py) as follows:
  
  ```
  python tune.py --ngpus 1 --model microsoft/deberta-v3-large --learning_rate 1e-5 --batch_size 16 --nepochs 2
  ```
  
  ## Evaluation results
  
  The model achieves 92.2% accuracy on [The Moral Uncertainty Research Competition](https://moraluncertainty.mlsafety.org/), which consists of a subset of the ETHICS dataset.