multilingual-xlm-roberta-large-lumasaba-ner-v1
This model is a fine-tuned version of xlm-roberta-large on the Beijuka/Multilingual_PII_NER_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 2.4160
- Precision: 0.2570
- Recall: 0.4679
- F1: 0.3318
- Accuracy: 0.2570
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: 4
- eval_batch_size: 4
- 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 |
|---|---|---|---|---|---|---|---|
| 2.4785 | 1.0 | 796 | 2.2775 | 0.2916 | 0.5325 | 0.3768 | 0.2916 |
| 2.3894 | 2.0 | 1592 | 2.4192 | 0.2916 | 0.5325 | 0.3768 | 0.2916 |
| 2.3051 | 3.0 | 2388 | 2.3613 | 0.2916 | 0.5325 | 0.3768 | 0.2916 |
| 2.3278 | 4.0 | 3184 | 2.8233 | 0.2916 | 0.5325 | 0.3768 | 0.2916 |
Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4