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: 0.0608
- Precision: 0.9371
- Recall: 0.9520
- F1: 0.9445
- Accuracy: 0.9867
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 |
|---|---|---|---|---|---|---|---|
| 0.0755 | 1.0 | 1756 | 0.0634 | 0.9148 | 0.9374 | 0.9259 | 0.9816 |
| 0.0361 | 2.0 | 3512 | 0.0685 | 0.9378 | 0.9465 | 0.9421 | 0.9855 |
| 0.0217 | 3.0 | 5268 | 0.0608 | 0.9371 | 0.9520 | 0.9445 | 0.9867 |
Framework versions
- Transformers 5.5.0
- Pytorch 2.11.0
- Datasets 4.8.5
- Tokenizers 0.22.2