ner_model
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1098
- Accuracy: 0.9780
- Precision: 0.8828
- Recall: 0.9028
- F1: 0.8927
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: 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0686 | 1.0 | 878 | 0.0540 | 0.9842 | 0.9127 | 0.9214 | 0.9170 |
| 0.0409 | 2.0 | 1756 | 0.0494 | 0.9870 | 0.9277 | 0.9413 | 0.9344 |
| 0.0219 | 3.0 | 2634 | 0.0504 | 0.9871 | 0.9337 | 0.9408 | 0.9372 |
Framework versions
- Transformers 5.15.0
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.22.2