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BioLinkBERT
This model is a fine-tuned version of michiyasunaga/BioLinkBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6209
- Accuracy: 0.8987
- F1: 0.5922
- Precision: 0.6630
- Recall: 0.5351
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 | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2278 | 1.0 | 1626 | 0.2833 | 0.9050 | 0.5842 | 0.7334 | 0.4854 |
| 0.1896 | 2.0 | 3252 | 0.3267 | 0.9012 | 0.6006 | 0.6752 | 0.5409 |
| 0.144 | 3.0 | 4878 | 0.4336 | 0.8989 | 0.6246 | 0.6376 | 0.6121 |
| 0.1156 | 4.0 | 6504 | 0.4667 | 0.8939 | 0.5918 | 0.6280 | 0.5595 |
| 0.0864 | 5.0 | 8130 | 0.5413 | 0.8969 | 0.6103 | 0.6347 | 0.5877 |
| 0.0515 | 6.0 | 9756 | 0.6209 | 0.8987 | 0.5922 | 0.6630 | 0.5351 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
