medlid-identify
This model is a fine-tuned version of bert-large-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1708
- Precision: 0.3912
- Recall: 0.4603
- F1: 0.4229
- Accuracy: 0.9463
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: 81
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 381 | 0.1567 | 0.2689 | 0.3180 | 0.2914 | 0.9377 |
| 0.1618 | 2.0 | 762 | 0.1399 | 0.4016 | 0.3847 | 0.3930 | 0.9492 |
| 0.0978 | 3.0 | 1143 | 0.1505 | 0.3773 | 0.4239 | 0.3993 | 0.9468 |
| 0.0636 | 4.0 | 1524 | 0.1708 | 0.3912 | 0.4603 | 0.4229 | 0.9463 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.11.0
- Datasets 2.13.1
- Tokenizers 0.13.3