---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioLinkBERT-LitCovid-v1.3.1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# BioLinkBERT-LitCovid-v1.3.1

This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6883
- Hamming loss: 0.0171
- F1 micro: 0.8542
- F1 macro: 0.3828
- F1 weighted: 0.8818
- F1 samples: 0.8804
- Precision micro: 0.7855
- Precision macro: 0.3067
- Precision weighted: 0.8407
- Precision samples: 0.8641
- Recall micro: 0.9360
- Recall macro: 0.7145
- Recall weighted: 0.9360
- Recall samples: 0.9459
- Roc Auc: 0.9607
- Accuracy: 0.6896

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 1.0638        | 1.0   | 2272  | 0.4414          | 0.0398       | 0.7141   | 0.2594   | 0.8318      | 0.8178     | 0.5807          | 0.2077          | 0.7729             | 0.7843            | 0.9269       | 0.8062       | 0.9269          | 0.9422         | 0.9445  | 0.5545   |
| 0.8571        | 2.0   | 4544  | 0.4364          | 0.0230       | 0.8122   | 0.3367   | 0.8645      | 0.8517     | 0.7236          | 0.2666          | 0.8255             | 0.8284            | 0.9254       | 0.7835       | 0.9254          | 0.9396         | 0.9527  | 0.6211   |
| 0.6709        | 3.0   | 6816  | 0.4827          | 0.0218       | 0.8222   | 0.3405   | 0.8723      | 0.8638     | 0.7297          | 0.2708          | 0.8239             | 0.8381            | 0.9415       | 0.7770       | 0.9415          | 0.9513         | 0.9609  | 0.6488   |
| 0.5093        | 4.0   | 9088  | 0.5695          | 0.0184       | 0.8457   | 0.3795   | 0.8781      | 0.8753     | 0.7692          | 0.3006          | 0.8333             | 0.8556            | 0.9390       | 0.7605       | 0.9390          | 0.9482         | 0.9615  | 0.6760   |
| 0.2957        | 5.0   | 11360 | 0.6883          | 0.0171       | 0.8542   | 0.3828   | 0.8818      | 0.8804     | 0.7855          | 0.3067          | 0.8407             | 0.8641            | 0.9360       | 0.7145       | 0.9360          | 0.9459         | 0.9607  | 0.6896   |


### Framework versions

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