---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioBERT-LitCovid-v1.3hh
  results: []
---

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# BioBERT-LitCovid-v1.3hh

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: 0.9050
- Hamming loss: 0.0147
- F1 micro: 0.8717
- F1 macro: 0.4368
- F1 weighted: 0.8882
- F1 samples: 0.8857
- Precision micro: 0.8176
- Precision macro: 0.3560
- Precision weighted: 0.8520
- Precision samples: 0.8728
- Recall micro: 0.9334
- Recall macro: 0.7011
- Recall weighted: 0.9334
- Recall samples: 0.9438
- Roc Auc: 0.9608
- Accuracy: 0.7014

## 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: 0.11492820779210673
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 1.1889        | 1.0   | 2272  | 0.4213          | 0.0512       | 0.6596   | 0.2446   | 0.8084      | 0.7608     | 0.5126          | 0.1941          | 0.7385             | 0.7077            | 0.9250       | 0.8376       | 0.9250          | 0.9404         | 0.9376  | 0.4492   |
| 0.8405        | 2.0   | 4544  | 0.4523          | 0.0234       | 0.8101   | 0.3434   | 0.8586      | 0.8435     | 0.7177          | 0.2700          | 0.8104             | 0.8130            | 0.9296       | 0.7802       | 0.9296          | 0.9421         | 0.9544  | 0.5954   |
| 0.6991        | 3.0   | 6816  | 0.5218          | 0.0214       | 0.8253   | 0.3595   | 0.8703      | 0.8563     | 0.7327          | 0.2829          | 0.8184             | 0.8238            | 0.9447       | 0.7721       | 0.9447          | 0.9534         | 0.9626  | 0.6190   |
| 0.3865        | 4.0   | 9088  | 0.8428          | 0.0155       | 0.8655   | 0.4279   | 0.8826      | 0.8808     | 0.8092          | 0.3453          | 0.8458             | 0.8667            | 0.9302       | 0.6992       | 0.9302          | 0.9417         | 0.9589  | 0.6917   |
| 0.1332        | 5.0   | 11360 | 0.9050          | 0.0147       | 0.8717   | 0.4368   | 0.8882      | 0.8857     | 0.8176          | 0.3560          | 0.8520             | 0.8728            | 0.9334       | 0.7011       | 0.9334          | 0.9438         | 0.9608  | 0.7014   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.13.3