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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}
}
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