fedcsis-slot_baseline-xlm_r-leyzer_en
This model is a fine-tuned version of xlm-roberta-base on the leyzer-fedcsis dataset. It achieves the following results on the evaluation set:
- Loss: 0.1017
- Precision: 0.9735
- Recall: 0.9722
- F1: 0.9729
- Accuracy: 0.9852
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.2565 | 1.0 | 814 | 0.2709 | 0.8433 | 0.8717 | 0.8573 | 0.9313 |
| 0.183 | 2.0 | 1628 | 0.1226 | 0.9413 | 0.9568 | 0.9490 | 0.9758 |
| 0.0961 | 3.0 | 2442 | 0.1082 | 0.9561 | 0.9612 | 0.9586 | 0.9798 |
| 0.0528 | 4.0 | 3256 | 0.0795 | 0.9678 | 0.9690 | 0.9684 | 0.9855 |
| 0.0334 | 5.0 | 4070 | 0.0742 | 0.9720 | 0.9709 | 0.9715 | 0.9855 |
| 0.027 | 6.0 | 4884 | 0.0960 | 0.9705 | 0.9714 | 0.9710 | 0.9838 |
| 0.0234 | 7.0 | 5698 | 0.0910 | 0.9730 | 0.9736 | 0.9733 | 0.9861 |
| 0.0111 | 8.0 | 6512 | 0.0871 | 0.9732 | 0.9728 | 0.9730 | 0.9871 |
| 0.0067 | 9.0 | 7326 | 0.1016 | 0.9714 | 0.9716 | 0.9715 | 0.9861 |
| 0.0067 | 10.0 | 8140 | 0.1017 | 0.9735 | 0.9722 | 0.9729 | 0.9852 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- 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}
}