Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
3.4487	1.0	798	1.5956	0.6532	0.6532
1.1691	2.0	1596	0.8475	0.8325	0.8325
0.7795	3.0	2394	0.5025	0.9042	0.9042
0.3981	4.0	3192	0.3407	0.9377	0.9377
0.3072	5.0	3990	0.2610	0.9505	0.9505
0.1834	6.0	4788	0.2134	0.9571	0.9571
0.1303	7.0	5586	0.1850	0.9634	0.9634
0.1096	8.0	6384	0.1689	0.9697	0.9697
0.0849	9.0	7182	0.1605	0.9706	0.9706
0.0824	10.0	7980	0.1557	0.9717	0.9717

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.0
Tokenizers 0.13.2

Citation

If you use this model, please cite the following:

@inproceedings{kubis2023caiccaic,
	author={Marek Kubis and Paweł Skórzewski and Marcin Sowański and Tomasz Ziętkiewicz},
	pages={1319–1324},
	title={Center for Artificial Intelligence Challenge on Conversational AI Correctness},
	booktitle={Proceedings of the 18th Conference on Computer Science and Intelligence Systems},
	year={2023},
	doi={10.15439/2023B6058},
	url={http://dx.doi.org/10.15439/2023B6058},
	volume={35},
	series={Annals of Computer Science and Information Systems}
}

cartesinus/fedcsis-intent_baseline-xlm_r-leyzer_pl

fedcsis-intent_baseline-xlm_r-leyzer_pl

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Citation

cartesinus/
fedcsis-intent_baseline-xlm_r-leyzer_pl