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fedcsis-intent_baseline-xlm_r-all (en,es,pl)
This model is a fine-tuned version of xlm-roberta-base on the leyzer-fedcsis dataset. It was trained to predict intents and it was trained and evaluated on all three languages.
Results on test set:
- Accuracy: 0.950414
It achieves the following results on the evaluation set:
- Accuracy: 0.975555
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.7642 | 1.0 | 2552 | 0.6074 | 0.8696 | 0.8696 |
| 0.2846 | 2.0 | 5104 | 0.2371 | 0.9464 | 0.9464 |
| 0.1251 | 3.0 | 7656 | 0.1486 | 0.9662 | 0.9662 |
| 0.0749 | 4.0 | 10208 | 0.1226 | 0.9731 | 0.9731 |
| 0.0503 | 5.0 | 12760 | 0.1159 | 0.9756 | 0.9756 |
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}
}
