bert-base-cased-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0586
- Precision: 0.9318
- Recall: 0.9389
- F1: 0.9353
- Accuracy: 0.9844
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2373 | 1.0 | 878 | 0.0740 | 0.9115 | 0.9164 | 0.9139 | 0.9796 |
| 0.047 | 2.0 | 1756 | 0.0586 | 0.9318 | 0.9389 | 0.9353 | 0.9844 |
| 0.0287 | 3.0 | 2634 | 0.0586 | 0.9362 | 0.9446 | 0.9404 | 0.9852 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1