bert-base-NER-model
This model is a fine-tuned version of dslim/bert-base-NER on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3093
- Precision: 0.5601
- Recall: 0.4059
- F1: 0.4707
- Accuracy: 0.9488
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.3253 | 0.5176 | 0.4096 | 0.4573 | 0.9472 |
| No log | 2.0 | 426 | 0.3093 | 0.5601 | 0.4059 | 0.4707 | 0.9488 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Tokenizers 0.20.3