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---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioBERT-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. -->
# BioBERT-LitCovid-1.4
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5756
- Hamming loss: 0.0802
- F1 micro: 0.6160
- F1 macro: 0.4740
- F1 weighted: 0.6962
- F1 samples: 0.6217
- Precision micro: 0.4710
- Precision macro: 0.3578
- Precision weighted: 0.6089
- Precision samples: 0.5156
- Recall micro: 0.8901
- Recall macro: 0.8404
- Recall weighted: 0.8901
- Recall samples: 0.9055
- Roc Auc: 0.9061
- Accuracy: 0.0775
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.6673 | 1.0 | 1151 | 0.6365 | 0.1262 | 0.5023 | 0.3822 | 0.6341 | 0.5084 | 0.3513 | 0.2799 | 0.5428 | 0.3829 | 0.8808 | 0.8538 | 0.8808 | 0.8981 | 0.8770 | 0.0088 |
| 0.5371 | 2.0 | 2303 | 0.5721 | 0.1080 | 0.5442 | 0.4060 | 0.6607 | 0.5578 | 0.3916 | 0.2993 | 0.5701 | 0.4391 | 0.8917 | 0.8644 | 0.8917 | 0.9074 | 0.8919 | 0.0365 |
| 0.4628 | 3.0 | 3454 | 0.5620 | 0.0940 | 0.5780 | 0.4370 | 0.6776 | 0.5874 | 0.4280 | 0.3248 | 0.5909 | 0.4739 | 0.8899 | 0.8572 | 0.8899 | 0.9054 | 0.8986 | 0.0510 |
| 0.3925 | 4.0 | 4606 | 0.5744 | 0.0796 | 0.6160 | 0.4742 | 0.6960 | 0.6208 | 0.4728 | 0.3591 | 0.6113 | 0.5160 | 0.8837 | 0.8377 | 0.8837 | 0.9004 | 0.9035 | 0.0752 |
| 0.3647 | 5.0 | 5755 | 0.5756 | 0.0802 | 0.6160 | 0.4740 | 0.6962 | 0.6217 | 0.4710 | 0.3578 | 0.6089 | 0.5156 | 0.8901 | 0.8404 | 0.8901 | 0.9055 | 0.9061 | 0.0775 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3
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