---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioLinkBERT-LitCovid-v1.2.3
  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-v1.2.3

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: 0.9691
- F1 micro: 0.8266
- F1 macro: 0.3107
- F1 weighted: 0.8821
- F1 samples: 0.8868
- Precision micro: 0.7335
- Precision macro: 0.2518
- Precision weighted: 0.8347
- Precision samples: 0.8699
- Recall micro: 0.9468
- Recall macro: 0.7764
- Recall weighted: 0.9468
- Recall samples: 0.9538
- Roc Auc: 0.9640
- Accuracy: 0.7104

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 1.1059        | 1.0   | 2272 | 0.7606          | 0.7517   | 0.2617   | 0.8428      | 0.8566     | 0.6257          | 0.2096          | 0.7778             | 0.8302            | 0.9412       | 0.7947       | 0.9412          | 0.9501         | 0.9553  | 0.6325   |
| 0.6408        | 2.0   | 4544 | 0.8639          | 0.8057   | 0.2965   | 0.8751      | 0.8786     | 0.7057          | 0.2399          | 0.8315             | 0.8626            | 0.9389       | 0.8070       | 0.9389          | 0.9484         | 0.9588  | 0.6961   |
| 0.6275        | 3.0   | 6816 | 0.9691          | 0.8266   | 0.3107   | 0.8821      | 0.8868     | 0.7335          | 0.2518          | 0.8347             | 0.8699            | 0.9468       | 0.7764       | 0.9468          | 0.9538         | 0.9640  | 0.7104   |


### Framework versions

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