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: 0.0596
- Precision: 0.9340
- Recall: 0.9509
- F1: 0.9424
- Accuracy: 0.9862
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 |
---|---|---|---|---|---|---|---|
0.0769 | 1.0 | 1756 | 0.0678 | 0.9050 | 0.9317 | 0.9182 | 0.9809 |
0.0354 | 2.0 | 3512 | 0.0641 | 0.9333 | 0.9492 | 0.9412 | 0.9858 |
0.0219 | 3.0 | 5268 | 0.0596 | 0.9340 | 0.9509 | 0.9424 | 0.9862 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 3