xlm-roberta-base-finetuned-lener-br
This model is a fine-tuned version of xlm-roberta-base on the lener_br dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8443
- Recall: 0.8845
- F1: 0.8639
- Accuracy: 0.9752
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0832 | 1.0 | 1957 | nan | 0.6752 | 0.8625 | 0.7575 | 0.9578 |
| 0.0477 | 2.0 | 3914 | nan | 0.8391 | 0.8839 | 0.8609 | 0.9704 |
| 0.029 | 3.0 | 5871 | nan | 0.7530 | 0.9059 | 0.8224 | 0.9648 |
| 0.0223 | 4.0 | 7828 | nan | 0.7488 | 0.8744 | 0.8067 | 0.9659 |
| 0.0234 | 5.0 | 9785 | nan | 0.7216 | 0.8783 | 0.7923 | 0.9644 |
| 0.0171 | 6.0 | 11742 | nan | 0.7072 | 0.8969 | 0.7908 | 0.9642 |
| 0.0121 | 7.0 | 13699 | nan | 0.7769 | 0.8775 | 0.8241 | 0.9681 |
| 0.0093 | 8.0 | 15656 | nan | 0.7218 | 0.8772 | 0.7920 | 0.9621 |
| 0.0074 | 9.0 | 17613 | nan | 0.8241 | 0.8767 | 0.8496 | 0.9739 |
| 0.0055 | 10.0 | 19570 | nan | 0.7369 | 0.8801 | 0.8021 | 0.9638 |
| 0.0055 | 11.0 | 21527 | nan | 0.8443 | 0.8845 | 0.8639 | 0.9752 |
| 0.0029 | 12.0 | 23484 | nan | 0.8338 | 0.8935 | 0.8626 | 0.9753 |
| 0.0026 | 13.0 | 25441 | nan | 0.7721 | 0.8992 | 0.8308 | 0.9694 |
| 0.004 | 14.0 | 27398 | nan | 0.7466 | 0.8886 | 0.8114 | 0.9672 |
| 0.0006 | 15.0 | 29355 | nan | 0.7518 | 0.8995 | 0.8190 | 0.9686 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1