---
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
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# 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