medlid
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2976
- Precision: 0.4183
- Recall: 0.5546
- F1: 0.4769
- Accuracy: 0.9343
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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 19 | 0.2900 | 0.3809 | 0.5710 | 0.4569 | 0.9285 |
| No log | 2.0 | 38 | 0.2976 | 0.4183 | 0.5546 | 0.4769 | 0.9343 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.11.0
- Datasets 2.13.1
- Tokenizers 0.13.3