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---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
metrics:
- pearsonr
- r_squared
model-index:
- name: motes_mtci_microsoft-beit-large-patch16-224-pt22k-ft22k
results: []
---
# Ocsai-D Large
This model is a trained model for scoring creativity - specifically figural (drawing-based) originality scoring. It is a fine-tuned version of [beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224-pt22k-ft22k).
It achieves the following results on the evaluation set:
- Mse: 0.0067
- Pearsonr: 0.85
- R2: 0.63
- Rmse: 0.082
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 |