distilbert-finetuned-ner
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0713
- Precision: 0.9184
- Recall: 0.9381
- F1: 0.9281
- Accuracy: 0.9832
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0926 | 1.0 | 1756 | 0.0848 | 0.8764 | 0.9090 | 0.8924 | 0.9757 |
| 0.0451 | 2.0 | 3512 | 0.0715 | 0.9184 | 0.9325 | 0.9254 | 0.9826 |
| 0.0293 | 3.0 | 5268 | 0.0713 | 0.9184 | 0.9381 | 0.9281 | 0.9832 |
Framework versions
- Transformers 5.18.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.2