README.md

---
inference:
  parameters:
    function_to_apply: "none"
widget:
- text: "I cuddled with my dog today."
---

# Conditional Utilitarian Roberta 01

## Model description

This is a [Roberta-based](https://huggingface.co/roberta-large) model. It was first fine-tuned on for computing utility estimates of experiences (see [utilitarian-roberta-01](https://huggingface.co/pfr/utilitarian-roberta-01). It was then further fine-tuned on 160 examples of pairwise comparisons of conditional utilities.

## Intended use

The main use case is the computation of utility estimates of first-person text scenarios, under extra contextual information.

## Limitations

The model was fine-tuned on only 160 examples, so it should be expected to have limited performance.

Further, while the base model was trained on ~10000 examples, they are still restricted, 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

Given a scenario S under a context C, and the model U, one computes the estimated conditional utility with `U(f'{C} {S}') - U(C)`.

## Training data

The first training data is the train split from the Utilitarianism part of the [ETHICS dataset](https://arxiv.org/abs/2008.02275).

The second training data consists of 160 crowdsourced examples of triples (S, C0, C1) consisting of one scenario and two possible contexts, where `U(S | C0) > U(S | C1)`.

## Training procedure

Starting from [utilitarian-roberta-01](https://huggingface.co/pfr/utilitarian-roberta-01), we fine-tune the model over the training data of 160 examples, with a learning rate of `1e-5`, a batch size of `8`, and for 2 epochs.

## Evaluation results

The model achieves ~70% accuracy over 40 crowdsourced examples, from the same distribution as the training data.