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: 0.0619
- Precision: 0.9327
- Recall: 0.9517
- F1: 0.9421
- Accuracy: 0.9865
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 |
|---|---|---|---|---|---|---|---|
| 0.0736 | 1.0 | 1756 | 0.0705 | 0.8998 | 0.9329 | 0.9160 | 0.9802 |
| 0.0353 | 2.0 | 3512 | 0.0672 | 0.9324 | 0.9468 | 0.9395 | 0.9850 |
| 0.0218 | 3.0 | 5268 | 0.0619 | 0.9327 | 0.9517 | 0.9421 | 0.9865 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.23.1