| | --- |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: BioBERT-LitCovid-v1.3hh |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # BioBERT-LitCovid-v1.3hh |
| |
|
| | This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.9050 |
| | - Hamming loss: 0.0147 |
| | - F1 micro: 0.8717 |
| | - F1 macro: 0.4368 |
| | - F1 weighted: 0.8882 |
| | - F1 samples: 0.8857 |
| | - Precision micro: 0.8176 |
| | - Precision macro: 0.3560 |
| | - Precision weighted: 0.8520 |
| | - Precision samples: 0.8728 |
| | - Recall micro: 0.9334 |
| | - Recall macro: 0.7011 |
| | - Recall weighted: 0.9334 |
| | - Recall samples: 0.9438 |
| | - Roc Auc: 0.9608 |
| | - Accuracy: 0.7014 |
| |
|
| | ## 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: 5e-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 |
| | - lr_scheduler_warmup_ratio: 0.11492820779210673 |
| | - num_epochs: 5 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Hamming loss | F1 micro | F1 macro | F1 weighted | F1 samples | Precision micro | Precision macro | Precision weighted | Precision samples | Recall micro | Recall macro | Recall weighted | Recall samples | Roc Auc | Accuracy | |
| | |:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:| |
| | | 1.1889 | 1.0 | 2272 | 0.4213 | 0.0512 | 0.6596 | 0.2446 | 0.8084 | 0.7608 | 0.5126 | 0.1941 | 0.7385 | 0.7077 | 0.9250 | 0.8376 | 0.9250 | 0.9404 | 0.9376 | 0.4492 | |
| | | 0.8405 | 2.0 | 4544 | 0.4523 | 0.0234 | 0.8101 | 0.3434 | 0.8586 | 0.8435 | 0.7177 | 0.2700 | 0.8104 | 0.8130 | 0.9296 | 0.7802 | 0.9296 | 0.9421 | 0.9544 | 0.5954 | |
| | | 0.6991 | 3.0 | 6816 | 0.5218 | 0.0214 | 0.8253 | 0.3595 | 0.8703 | 0.8563 | 0.7327 | 0.2829 | 0.8184 | 0.8238 | 0.9447 | 0.7721 | 0.9447 | 0.9534 | 0.9626 | 0.6190 | |
| | | 0.3865 | 4.0 | 9088 | 0.8428 | 0.0155 | 0.8655 | 0.4279 | 0.8826 | 0.8808 | 0.8092 | 0.3453 | 0.8458 | 0.8667 | 0.9302 | 0.6992 | 0.9302 | 0.9417 | 0.9589 | 0.6917 | |
| | | 0.1332 | 5.0 | 11360 | 0.9050 | 0.0147 | 0.8717 | 0.4368 | 0.8882 | 0.8857 | 0.8176 | 0.3560 | 0.8520 | 0.8728 | 0.9334 | 0.7011 | 0.9334 | 0.9438 | 0.9608 | 0.7014 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.0 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.13.3 |
| | |
