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medlid-byAbstract
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: 0.1660
- Precision: 0.1640
- Recall: 0.1890
- F1: 0.1756
- Accuracy: 0.9373
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: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 19 | 0.1925 | 0.0 | 0.0 | 0.0 | 0.9466 |
| No log | 2.0 | 38 | 0.1698 | 0.0 | 0.0 | 0.0 | 0.9451 |
| No log | 3.0 | 57 | 0.1712 | 0.0966 | 0.1129 | 0.1041 | 0.9386 |
| No log | 4.0 | 76 | 0.1607 | 0.1611 | 0.1260 | 0.1414 | 0.9427 |
| No log | 5.0 | 95 | 0.1603 | 0.1854 | 0.1601 | 0.1718 | 0.9429 |
| No log | 6.0 | 114 | 0.1660 | 0.1640 | 0.1890 | 0.1756 | 0.9373 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.11.0
- Datasets 2.13.1
- Tokenizers 0.13.3
