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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: PubMedELECTRA-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. -->
# PubMedELECTRA-LitCovid-1.4
This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedELECTRA-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedELECTRA-base-uncased-abstract) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5898
- Hamming loss: 0.0967
- F1 micro: 0.5691
- F1 macro: 0.4329
- F1 weighted: 0.6693
- F1 samples: 0.5791
- Precision micro: 0.4198
- Precision macro: 0.3211
- Precision weighted: 0.5820
- Precision samples: 0.4666
- Recall micro: 0.8834
- Recall macro: 0.8456
- Recall weighted: 0.8834
- Recall samples: 0.8983
- Roc Auc: 0.8941
- Accuracy: 0.0504
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.7482 | 1.0 | 1151 | 0.7061 | 0.1518 | 0.4528 | 0.3484 | 0.6073 | 0.4584 | 0.3063 | 0.2528 | 0.5187 | 0.3313 | 0.8684 | 0.8422 | 0.8684 | 0.8869 | 0.8575 | 0.0023 |
| 0.5987 | 2.0 | 2303 | 0.6241 | 0.1287 | 0.4983 | 0.3783 | 0.6327 | 0.5120 | 0.3469 | 0.2766 | 0.5412 | 0.3888 | 0.8840 | 0.8571 | 0.8840 | 0.8996 | 0.8771 | 0.0193 |
| 0.5194 | 3.0 | 3454 | 0.5960 | 0.1079 | 0.5399 | 0.4108 | 0.6584 | 0.5500 | 0.3903 | 0.3056 | 0.5764 | 0.4339 | 0.8752 | 0.8513 | 0.8752 | 0.8921 | 0.8843 | 0.0351 |
| 0.4471 | 4.0 | 4606 | 0.5900 | 0.0982 | 0.5653 | 0.4286 | 0.6681 | 0.5747 | 0.4157 | 0.3179 | 0.5810 | 0.4609 | 0.8830 | 0.8468 | 0.8830 | 0.8983 | 0.8931 | 0.0460 |
| 0.422 | 5.0 | 5755 | 0.5898 | 0.0967 | 0.5691 | 0.4329 | 0.6693 | 0.5791 | 0.4198 | 0.3211 | 0.5820 | 0.4666 | 0.8834 | 0.8456 | 0.8834 | 0.8983 | 0.8941 | 0.0504 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3
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