fedcsis-intent_baseline-xlm_r-en
This model is a fine-tuned version of xlm-roberta-base on the leyzer-fedcsis dataset. Results on test set:
- Accuracy: 0.904007
It achieves the following results on the evaluation set:
- Loss: 0.1286
- Accuracy: 0.9772
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 | Accuracy | F1 |
|---|---|---|---|---|---|
| 3.4583 | 1.0 | 814 | 1.7712 | 0.6193 | 0.6193 |
| 1.3828 | 2.0 | 1628 | 0.9693 | 0.8073 | 0.8073 |
| 0.9585 | 3.0 | 2442 | 0.5830 | 0.8893 | 0.8893 |
| 0.502 | 4.0 | 3256 | 0.3813 | 0.9295 | 0.9295 |
| 0.2907 | 5.0 | 4070 | 0.2699 | 0.9485 | 0.9485 |
| 0.2267 | 6.0 | 4884 | 0.2059 | 0.9615 | 0.9615 |
| 0.1437 | 7.0 | 5698 | 0.1648 | 0.9700 | 0.9700 |
| 0.0998 | 8.0 | 6512 | 0.1422 | 0.9741 | 0.9741 |
| 0.0856 | 9.0 | 7326 | 0.1334 | 0.9758 | 0.9758 |
| 0.0748 | 10.0 | 8140 | 0.1286 | 0.9772 | 0.9772 |
Framework versions
- Transformers 4.27.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- 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}
}