fedcsis-intent_baseline-xlm_r-es
This model is a fine-tuned version of xlm-roberta-base on the leyzer-fedcsis dataset.
Test set results:
- Accuracy: 0.970738
It achieves the following results on the evaluation set:
- Loss: 0.1440
- Accuracy: 0.9749
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 |
|---|---|---|---|---|---|
| 2.6266 | 1.0 | 941 | 0.9415 | 0.8074 | 0.8074 |
| 0.7228 | 2.0 | 1882 | 0.4892 | 0.8999 | 0.8999 |
| 0.3612 | 3.0 | 2823 | 0.3110 | 0.9346 | 0.9346 |
| 0.2236 | 4.0 | 3764 | 0.2433 | 0.9518 | 0.9518 |
| 0.1464 | 5.0 | 4705 | 0.1963 | 0.9594 | 0.9594 |
| 0.1056 | 6.0 | 5646 | 0.1698 | 0.9667 | 0.9667 |
| 0.0725 | 7.0 | 6587 | 0.1574 | 0.9693 | 0.9693 |
| 0.0602 | 8.0 | 7528 | 0.1476 | 0.9729 | 0.9729 |
| 0.0619 | 9.0 | 8469 | 0.1474 | 0.9743 | 0.9743 |
| 0.052 | 10.0 | 9410 | 0.1440 | 0.9749 | 0.9749 |
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}
}