---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioLinkBERT-LitCovid-v1.2.1
  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-v1.2.1

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: 0.2205
- F1 micro: 0.9016
- F1 macro: 0.8505
- F1 weighted: 0.9044
- F1 samples: 0.9056
- Precision micro: 0.8545
- Precision macro: 0.7857
- Precision weighted: 0.8625
- Precision samples: 0.8862
- Recall micro: 0.9540
- Recall macro: 0.9431
- Recall weighted: 0.9540
- Recall samples: 0.9610
- Roc Auc: 0.9578
- Accuracy: 0.7211

## 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: 4

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.2839        | 1.0   | 2211 | 0.2205          | 0.9016   | 0.8505   | 0.9044      | 0.9056     | 0.8545          | 0.7857          | 0.8625             | 0.8862            | 0.9540       | 0.9431       | 0.9540          | 0.9610         | 0.9578  | 0.7211   |
| 0.1926        | 2.0   | 4422 | 0.2477          | 0.9134   | 0.8734   | 0.9147      | 0.9159     | 0.8770          | 0.8309          | 0.8808             | 0.9026            | 0.9529       | 0.9283       | 0.9529          | 0.9590         | 0.9607  | 0.7554   |
| 0.1341        | 3.0   | 6633 | 0.2667          | 0.9155   | 0.8749   | 0.9164      | 0.9170     | 0.8823          | 0.8328          | 0.8851             | 0.9059            | 0.9513       | 0.9251       | 0.9513          | 0.9569         | 0.9606  | 0.7642   |
| 0.1161        | 4.0   | 8844 | 0.2864          | 0.9188   | 0.8783   | 0.9195      | 0.9202     | 0.8938          | 0.8451          | 0.8958             | 0.9150            | 0.9452       | 0.9173       | 0.9452          | 0.9525         | 0.9593  | 0.7758   |


### Framework versions

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