| | --- |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: BioELECTRA-LitCovid-v1.3h |
| | 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. --> |
| |
|
| | # BioELECTRA-LitCovid-v1.3h |
| |
|
| | This model is a fine-tuned version of [kamalkraj/bioelectra-base-discriminator-pubmed](https://huggingface.co/kamalkraj/bioelectra-base-discriminator-pubmed) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.7785 |
| | - Hamming loss: 0.0198 |
| | - F1 micro: 0.8361 |
| | - F1 macro: 0.3559 |
| | - F1 weighted: 0.8787 |
| | - F1 samples: 0.8727 |
| | - Precision micro: 0.7527 |
| | - Precision macro: 0.2860 |
| | - Precision weighted: 0.8332 |
| | - Precision samples: 0.8513 |
| | - Recall micro: 0.9403 |
| | - Recall macro: 0.7383 |
| | - Recall weighted: 0.9403 |
| | - Recall samples: 0.9483 |
| | - Roc Auc: 0.9614 |
| | - Accuracy: 0.6748 |
| |
|
| | ## 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: 0.1866747178469669 |
| | - 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 | |
| | |:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:| |
| | | 1.5951 | 1.0 | 2272 | 0.6305 | 0.0577 | 0.6142 | 0.2231 | 0.7544 | 0.7325 | 0.4787 | 0.1794 | 0.7016 | 0.6957 | 0.8568 | 0.7122 | 0.8568 | 0.8824 | 0.9020 | 0.3878 | |
| | | 1.1968 | 2.0 | 4544 | 0.4865 | 0.0353 | 0.7393 | 0.2825 | 0.8470 | 0.8156 | 0.6122 | 0.2305 | 0.7890 | 0.7790 | 0.9330 | 0.7538 | 0.9330 | 0.9449 | 0.9497 | 0.5560 | |
| | | 0.9573 | 3.0 | 6816 | 0.5637 | 0.0247 | 0.8033 | 0.3292 | 0.8474 | 0.8430 | 0.7019 | 0.2574 | 0.7851 | 0.8066 | 0.9389 | 0.7380 | 0.9389 | 0.9470 | 0.9582 | 0.5918 | |
| | | 0.7604 | 4.0 | 9088 | 0.6811 | 0.0206 | 0.8306 | 0.3558 | 0.8726 | 0.8675 | 0.7441 | 0.2835 | 0.8239 | 0.8438 | 0.9400 | 0.7574 | 0.9400 | 0.9483 | 0.9608 | 0.6561 | |
| | | 0.4404 | 5.0 | 11360 | 0.7785 | 0.0198 | 0.8361 | 0.3559 | 0.8787 | 0.8727 | 0.7527 | 0.2860 | 0.8332 | 0.8513 | 0.9403 | 0.7383 | 0.9403 | 0.9483 | 0.9614 | 0.6748 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.0 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.13.3 |
| | |
