| | --- |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: BioElectra-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. --> |
| |
|
| | # BioElectra-LitCovid-1.4 |
| |
|
| | 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: 0.6551 |
| | - Hamming loss: 0.1096 |
| | - F1 micro: 0.5375 |
| | - F1 macro: 0.4017 |
| | - F1 weighted: 0.6519 |
| | - F1 samples: 0.5520 |
| | - Precision micro: 0.3867 |
| | - Precision macro: 0.2948 |
| | - Precision weighted: 0.5638 |
| | - Precision samples: 0.4347 |
| | - Recall micro: 0.8813 |
| | - Recall macro: 0.8425 |
| | - Recall weighted: 0.8813 |
| | - Recall samples: 0.8977 |
| | - Roc Auc: 0.8862 |
| | - Accuracy: 0.0375 |
| |
|
| | ## 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 |
| | - gradient_accumulation_steps: 2 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:| |
| | | 0.8117 | 1.0 | 1151 | 0.7562 | 0.1732 | 0.4140 | 0.3137 | 0.5784 | 0.4179 | 0.2740 | 0.2255 | 0.4949 | 0.2947 | 0.8462 | 0.8285 | 0.8462 | 0.8675 | 0.8357 | 0.0005 | |
| | | 0.639 | 2.0 | 2303 | 0.6690 | 0.1346 | 0.4836 | 0.3618 | 0.6199 | 0.4952 | 0.3347 | 0.2629 | 0.5289 | 0.3716 | 0.8714 | 0.8448 | 0.8714 | 0.8906 | 0.8682 | 0.0095 | |
| | | 0.556 | 3.0 | 3454 | 0.6453 | 0.1253 | 0.5012 | 0.3747 | 0.6358 | 0.5147 | 0.3519 | 0.2750 | 0.5539 | 0.3944 | 0.8706 | 0.8536 | 0.8706 | 0.8895 | 0.8728 | 0.0220 | |
| | | 0.4906 | 4.0 | 4606 | 0.6567 | 0.1111 | 0.5339 | 0.4013 | 0.6494 | 0.5469 | 0.3832 | 0.2946 | 0.5608 | 0.4282 | 0.8800 | 0.8428 | 0.8800 | 0.8976 | 0.8848 | 0.0312 | |
| | | 0.4594 | 5.0 | 5755 | 0.6551 | 0.1096 | 0.5375 | 0.4017 | 0.6519 | 0.5520 | 0.3867 | 0.2948 | 0.5638 | 0.4347 | 0.8813 | 0.8425 | 0.8813 | 0.8977 | 0.8862 | 0.0375 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.0 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.1.0 |
| | - Tokenizers 0.13.3 |
| | |
