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fedcsis-intent_baseline-xlm_r-pl
This model is a fine-tuned version of xlm-roberta-base on the leyzer-fedcsis dataset. Results on test set:
- Accuracy: 0.959451
It achieves the following results on the evaluation set:
- Loss: 0.1602
- Accuracy: 0.9671
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 |
|---|---|---|---|---|---|
| 3.4745 | 1.0 | 798 | 1.5821 | 0.6795 | 0.6795 |
| 1.1438 | 2.0 | 1596 | 0.8333 | 0.8259 | 0.8259 |
| 0.7546 | 3.0 | 2394 | 0.4991 | 0.9039 | 0.9039 |
| 0.3955 | 4.0 | 3192 | 0.3466 | 0.9302 | 0.9302 |
| 0.3016 | 5.0 | 3990 | 0.2571 | 0.9440 | 0.9440 |
| 0.183 | 6.0 | 4788 | 0.2147 | 0.9588 | 0.9588 |
| 0.1309 | 7.0 | 5586 | 0.1900 | 0.9605 | 0.9605 |
| 0.1128 | 8.0 | 6384 | 0.1750 | 0.9640 | 0.9640 |
| 0.0873 | 9.0 | 7182 | 0.1638 | 0.9663 | 0.9663 |
| 0.082 | 10.0 | 7980 | 0.1602 | 0.9671 | 0.9671 |
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}
}
