bert-finetuned-named-entity-recognition-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: 0.0654
- Precision: 0.9360
- Recall: 0.9498
- F1: 0.9429
- Accuracy: 0.9861
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 | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0727 | 1.0 | 1756 | 0.0650 | 0.9127 | 0.9372 | 0.9248 | 0.9826 |
| 0.0346 | 2.0 | 3512 | 0.0662 | 0.9329 | 0.9446 | 0.9387 | 0.9853 |
| 0.0216 | 3.0 | 5268 | 0.0654 | 0.9360 | 0.9498 | 0.9429 | 0.9861 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1