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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
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