bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0664
- Precision: 0.9436
- Recall: 0.9464
- F1: 0.9450
- Accuracy: 0.9859
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 |
|---|---|---|---|---|---|---|---|
| 0.0807 | 1.0 | 1756 | 0.0750 | 0.9084 | 0.9223 | 0.9153 | 0.9795 |
| 0.0386 | 2.0 | 3512 | 0.0704 | 0.9395 | 0.9394 | 0.9395 | 0.9848 |
| 0.0224 | 3.0 | 5268 | 0.0664 | 0.9436 | 0.9464 | 0.9450 | 0.9859 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.13.0
- Datasets 5.0.1
- Tokenizers 0.22.2