medicare_ner
This model is a fine-tuned version of prajjwal1/bert-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0001
- Overall Precision: 1.0
- Overall Recall: 1.0
- Overall F1: 1.0
- Overall Accuracy: 1.0
- Age F1: 1.0
- Yob F1: 1.0
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150
Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Age F1 | Yob F1 |
|---|---|---|---|---|---|---|---|---|---|
| 0.0276 | 43.48 | 1000 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 86.96 | 2000 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 130.43 | 3000 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1