---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioBERT-LitCovid-v1.3.1
  results: []
---

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# BioBERT-LitCovid-v1.3.1

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: 0.6731
- Hamming loss: 0.0186
- F1 micro: 0.8434
- F1 macro: 0.3657
- F1 weighted: 0.8790
- F1 samples: 0.8763
- Precision micro: 0.7702
- Precision macro: 0.2942
- Precision weighted: 0.8399
- Precision samples: 0.8618
- Recall micro: 0.9320
- Recall macro: 0.7288
- Recall weighted: 0.9320
- Recall samples: 0.9432
- Roc Auc: 0.9581
- Accuracy: 0.6841

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 1.1065        | 1.0   | 2272  | 0.4899          | 0.0357       | 0.7347   | 0.2684   | 0.8280      | 0.8274     | 0.6112          | 0.2128          | 0.7700             | 0.7976            | 0.9207       | 0.7424       | 0.9207          | 0.9370         | 0.9437  | 0.5688   |
| 0.8552        | 2.0   | 4544  | 0.4641          | 0.0246       | 0.8018   | 0.3270   | 0.8588      | 0.8548     | 0.7057          | 0.2595          | 0.8123             | 0.8327            | 0.9282       | 0.7833       | 0.9282          | 0.9424         | 0.9531  | 0.6325   |
| 0.7061        | 3.0   | 6816  | 0.5058          | 0.0227       | 0.8166   | 0.3320   | 0.8679      | 0.8652     | 0.7201          | 0.2640          | 0.8146             | 0.8402            | 0.9429       | 0.7601       | 0.9429          | 0.9522         | 0.9611  | 0.6548   |
| 0.5914        | 4.0   | 9088  | 0.6116          | 0.0196       | 0.8368   | 0.3588   | 0.8758      | 0.8719     | 0.7572          | 0.2869          | 0.8321             | 0.8533            | 0.9353       | 0.7398       | 0.9353          | 0.9456         | 0.9591  | 0.6706   |
| 0.294         | 5.0   | 11360 | 0.6731          | 0.0186       | 0.8434   | 0.3657   | 0.8790      | 0.8763     | 0.7702          | 0.2942          | 0.8399             | 0.8618            | 0.9320       | 0.7288       | 0.9320          | 0.9432         | 0.9581  | 0.6841   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3