multilingual-xlm-roberta-base-lumasaba-ner-v1
This model is a fine-tuned version of xlm-roberta-base on the Beijuka/Multilingual_PII_NER_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.4011
- Precision: 0.9757
- Recall: 0.9511
- F1: 0.9632
- Accuracy: 0.9546
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 398 | 0.8255 | 0.8041 | 0.7424 | 0.7720 | 0.7204 |
| 1.3835 | 2.0 | 796 | 0.4975 | 0.8921 | 0.8348 | 0.8625 | 0.8448 |
| 0.5267 | 3.0 | 1194 | 0.3900 | 0.8921 | 0.8739 | 0.8829 | 0.8842 |
| 0.3019 | 4.0 | 1592 | 0.3742 | 0.9178 | 0.8919 | 0.9047 | 0.9065 |
| 0.3019 | 5.0 | 1990 | 0.2945 | 0.9358 | 0.9358 | 0.9358 | 0.9314 |
| 0.1825 | 6.0 | 2388 | 0.3457 | 0.9406 | 0.9303 | 0.9354 | 0.9275 |
| 0.1222 | 7.0 | 2786 | 0.3336 | 0.9418 | 0.9507 | 0.9462 | 0.9421 |
| 0.0787 | 8.0 | 3184 | 0.4745 | 0.9610 | 0.9264 | 0.9434 | 0.9374 |
| 0.0563 | 9.0 | 3582 | 0.4344 | 0.9472 | 0.9421 | 0.9446 | 0.9340 |
| 0.0563 | 10.0 | 3980 | 0.3393 | 0.9594 | 0.9616 | 0.9605 | 0.9481 |
| 0.0528 | 11.0 | 4378 | 0.4272 | 0.9518 | 0.9287 | 0.9402 | 0.9305 |
| 0.0356 | 12.0 | 4776 | 0.3165 | 0.9708 | 0.9632 | 0.9670 | 0.9580 |
| 0.0283 | 13.0 | 5174 | 0.3633 | 0.9629 | 0.9546 | 0.9587 | 0.9533 |
| 0.0254 | 14.0 | 5572 | 0.3616 | 0.9490 | 0.9616 | 0.9553 | 0.9520 |
| 0.0254 | 15.0 | 5970 | 0.3528 | 0.9490 | 0.9624 | 0.9557 | 0.9498 |
Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4