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: 0.0650
- Accuracy: 0.9872
- Precision: 0.9399
- Recall: 0.9536
- F1 score: 0.9467
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 | Accuracy | Precision | Recall | F1 score |
|---|---|---|---|---|---|---|---|
| 0.0238 | 1.0 | 1756 | 0.0687 | 0.9853 | 0.9290 | 0.9463 | 0.9376 |
| 0.0189 | 2.0 | 3512 | 0.0653 | 0.9867 | 0.9368 | 0.9498 | 0.9433 |
| 0.0113 | 3.0 | 5268 | 0.0650 | 0.9872 | 0.9399 | 0.9536 | 0.9467 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.13.0
- Datasets 5.0.1
- Tokenizers 0.23.2