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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
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