README.md

---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
metrics:
- pearsonr
- r_squared
model-index:
- name: https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k
  results: []
---

# Ocsai-D Base

This model is a trained model for scoring creativity - specifically figural (drawing-based) originality scoring. It is a fine-tuned version of [beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k).
It achieves the following results on the evaluation set:

- Mse: 0.0077
- Pearsonr: 0.82
- R2: 0.52
- Rmse: 0.088

It can be tried at <https://openscoring.du.edu/draw>.

## Model description

See the pre-print:

Acar, S.^, Organisciak, P.^, & Dumas, D. (2023). Automated Scoring of Figural Tests of Creativity with Computer Vision. http://dx.doi.org/10.13140/RG.2.2.26865.25444

*^Authors contributed equally.*

## Intended uses & limitations

This model judges the originality of figural drawings. There are some limitations.

First, there is a confound with elaboration - drawing more leads - partially - to higher originality.

Secondly, the training is specific to one test, and mileage may vary on other images.

## Training and evaluation data

This is trained on the Multi-Trial Creative Ideation task (MTCI; [Barbot 2018](https://pubmed.ncbi.nlm.nih.gov/30618952/)), with the [data](https://osf.io/kqn9v/) from Patterson et al. ([2023](https://doi.org/10.31234/osf.io/t63dm)).

The train/test splits aligned with the ones from Patterson et al. 2023.

### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1