| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: BioLinkBERT-Large-LitCovid-1.4 |
| | 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. --> |
| |
|
| | # BioLinkBERT-Large-LitCovid-1.4 |
| |
|
| | This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5976 |
| | - Hamming loss: 0.0604 |
| | - F1 micro: 0.6804 |
| | - F1 macro: 0.5425 |
| | - F1 weighted: 0.7357 |
| | - F1 samples: 0.6807 |
| | - Precision micro: 0.5509 |
| | - Precision macro: 0.4271 |
| | - Precision weighted: 0.6552 |
| | - Precision samples: 0.5921 |
| | - Recall micro: 0.8895 |
| | - Recall macro: 0.8221 |
| | - Recall weighted: 0.8895 |
| | - Recall samples: 0.9063 |
| | - Roc Auc: 0.9165 |
| | - Accuracy: 0.1370 |
| |
|
| | ## 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: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 32 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:| |
| | | 0.5869 | 1.0 | 1151 | 0.5737 | 0.0978 | 0.5682 | 0.4375 | 0.6759 | 0.5754 | 0.4172 | 0.3269 | 0.5906 | 0.4591 | 0.8901 | 0.8593 | 0.8901 | 0.9076 | 0.8966 | 0.0421 | |
| | | 0.4636 | 2.0 | 2302 | 0.5316 | 0.0805 | 0.6179 | 0.4702 | 0.7052 | 0.6237 | 0.4704 | 0.3554 | 0.6181 | 0.5153 | 0.9005 | 0.8611 | 0.9005 | 0.9160 | 0.9107 | 0.0812 | |
| | | 0.3782 | 3.0 | 3453 | 0.5382 | 0.0760 | 0.6321 | 0.4929 | 0.7146 | 0.6327 | 0.4864 | 0.3757 | 0.6293 | 0.5230 | 0.9027 | 0.8556 | 0.9027 | 0.9183 | 0.9142 | 0.0797 | |
| | | 0.3031 | 4.0 | 4605 | 0.5807 | 0.0619 | 0.6754 | 0.5346 | 0.7343 | 0.6744 | 0.5437 | 0.4189 | 0.6531 | 0.5820 | 0.8915 | 0.8274 | 0.8915 | 0.9089 | 0.9166 | 0.1235 | |
| | | 0.2625 | 5.0 | 5755 | 0.5976 | 0.0604 | 0.6804 | 0.5425 | 0.7357 | 0.6807 | 0.5509 | 0.4271 | 0.6552 | 0.5921 | 0.8895 | 0.8221 | 0.8895 | 0.9063 | 0.9165 | 0.1370 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.0 |
| | - Pytorch 2.3.0+cu121 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.13.3 |
| | |
