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CTEBMSP_ANAT_DISO
This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0909
- Anat Precision: 0.7522
- Anat Recall: 0.7147
- Anat F1: 0.7330
- Anat Number: 361
- Diso Precision: 0.8915
- Diso Recall: 0.8919
- Diso F1: 0.8917
- Diso Number: 2645
- Overall Precision: 0.8755
- Overall Recall: 0.8706
- Overall F1: 0.8731
- Overall Accuracy: 0.9873
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: 8e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Anat Precision | Anat Recall | Anat F1 | Anat Number | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0592 | 1.0 | 2133 | 0.0506 | 0.6950 | 0.4986 | 0.5806 | 361 | 0.8635 | 0.8609 | 0.8622 | 2645 | 0.8484 | 0.8174 | 0.8326 | 0.9843 |
| 0.0323 | 2.0 | 4266 | 0.0583 | 0.7899 | 0.6039 | 0.6845 | 361 | 0.8780 | 0.8817 | 0.8798 | 2645 | 0.8697 | 0.8483 | 0.8589 | 0.9858 |
| 0.0201 | 3.0 | 6399 | 0.0580 | 0.6565 | 0.7147 | 0.6844 | 361 | 0.8598 | 0.8764 | 0.8680 | 2645 | 0.8339 | 0.8570 | 0.8453 | 0.9851 |
| 0.0121 | 4.0 | 8532 | 0.0758 | 0.7240 | 0.6759 | 0.6991 | 361 | 0.8976 | 0.8752 | 0.8863 | 2645 | 0.8776 | 0.8513 | 0.8642 | 0.9863 |
| 0.0078 | 5.0 | 10665 | 0.0814 | 0.7219 | 0.7119 | 0.7169 | 361 | 0.8776 | 0.8975 | 0.8875 | 2645 | 0.8595 | 0.8752 | 0.8673 | 0.9862 |
| 0.0031 | 6.0 | 12798 | 0.0974 | 0.7599 | 0.6399 | 0.6947 | 361 | 0.8895 | 0.8915 | 0.8905 | 2645 | 0.8761 | 0.8613 | 0.8686 | 0.9867 |
| 0.002 | 7.0 | 14931 | 0.0980 | 0.7143 | 0.6787 | 0.6960 | 361 | 0.8813 | 0.8957 | 0.8884 | 2645 | 0.8624 | 0.8696 | 0.8660 | 0.9860 |
| 0.0005 | 8.0 | 17064 | 0.0909 | 0.7522 | 0.7147 | 0.7330 | 361 | 0.8915 | 0.8919 | 0.8917 | 2645 | 0.8755 | 0.8706 | 0.8731 | 0.9873 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
