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: 0.0587
- Precision: 0.9348
- Recall: 0.9502
- F1: 0.9424
- Accuracy: 0.9866
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 |
|---|---|---|---|---|---|---|---|
| 0.0762 | 1.0 | 1756 | 0.0628 | 0.9089 | 0.9354 | 0.9220 | 0.9816 |
| 0.0355 | 2.0 | 3512 | 0.0666 | 0.9321 | 0.9448 | 0.9384 | 0.9847 |
| 0.0226 | 3.0 | 5268 | 0.0587 | 0.9348 | 0.9502 | 0.9424 | 0.9866 |
Framework versions
- Transformers 5.14.1
- Pytorch 2.13.0+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2