fedcsis-intent_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: 0.4646
- Accuracy: 0.9082
- F1: 0.9082
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 |
|---|---|---|---|---|---|
| 3.4394 | 1.0 | 814 | 1.7504 | 0.6457 | 0.6457 |
| 1.355 | 2.0 | 1628 | 0.9345 | 0.8164 | 0.8164 |
| 0.9344 | 3.0 | 2442 | 0.5652 | 0.8841 | 0.8841 |
| 0.4972 | 4.0 | 3256 | 0.3784 | 0.9295 | 0.9295 |
| 0.2867 | 5.0 | 4070 | 0.2496 | 0.9562 | 0.9562 |
| 0.2216 | 6.0 | 4884 | 0.1962 | 0.9689 | 0.9689 |
| 0.1354 | 7.0 | 5698 | 0.1570 | 0.9716 | 0.9716 |
| 0.0957 | 8.0 | 6512 | 0.1376 | 0.9774 | 0.9774 |
| 0.0827 | 9.0 | 7326 | 0.1289 | 0.9783 | 0.9783 |
| 0.0711 | 10.0 | 8140 | 0.1248 | 0.9794 | 0.9794 |
Framework versions
- Transformers 4.26.1
- 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}
}