e-care-bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6763
- Accuracy: 0.6904
- Macro F1: 0.6899
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: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|---|---|---|---|---|---|
| 0.6974 | 1.0 | 933 | 0.6049 | 0.6565 | 0.6516 |
| 0.5368 | 2.0 | 1866 | 0.5729 | 0.6843 | 0.6781 |
| 0.3853 | 3.0 | 2799 | 0.6744 | 0.6909 | 0.6904 |
| 0.2540 | 4.0 | 3732 | 0.8499 | 0.6871 | 0.6870 |
| 0.1812 | 5.0 | 4665 | 1.1347 | 0.6852 | 0.6850 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.14.0+cu130
- Datasets 3.6.0
- Tokenizers 0.23.2