bert-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:
- Accuracy: 0.9826
- F1: 0.9216
- Loss: 0.0611
- Precision: 0.9068
- Recall: 0.9369
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.078 | 1.0 | 1756 | 0.9826 | 0.9216 | 0.0611 | 0.9068 | 0.9369 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.16.1
- Tokenizers 0.19.1