---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioLinkBERT-LitCovid-v1.2.4
  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. -->

# BioLinkBERT-LitCovid-v1.2.4

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: 0.2160
- F1 micro: 0.8926
- F1 macro: 0.3237
- F1 weighted: 0.9016
- F1 samples: 0.9024
- Precision micro: 0.8426
- Precision macro: 0.2736
- Precision weighted: 0.8627
- Precision samples: 0.8871
- Recall micro: 0.9490
- Recall macro: 0.4834
- Recall weighted: 0.9490
- Recall samples: 0.9544
- Roc Auc: 0.9697
- Accuracy: 0.7353

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.4454        | 1.0   | 2248 | 0.3019          | 0.8637   | 0.2988   | 0.8757      | 0.8789     | 0.7937          | 0.2500          | 0.8205             | 0.8518            | 0.9471       | 0.4390       | 0.9471          | 0.9528         | 0.9669  | 0.6618   |
| 0.2453        | 2.0   | 4496 | 0.2696          | 0.8852   | 0.3387   | 0.8917      | 0.8947     | 0.8231          | 0.2862          | 0.8377             | 0.8701            | 0.9574       | 0.4723       | 0.9574          | 0.9602         | 0.9731  | 0.7056   |
| 0.1271        | 3.0   | 6744 | 0.2160          | 0.8926   | 0.3237   | 0.9016      | 0.9024     | 0.8426          | 0.2736          | 0.8627             | 0.8871            | 0.9490       | 0.4834       | 0.9490          | 0.9544         | 0.9697  | 0.7353   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3