| --- |
| 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 |
