---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioLinkBERT-LitCovid-v1.2.2
  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-v1.2.2

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: 0.2409
- F1 micro: 0.9209
- F1 macro: 0.8813
- F1 weighted: 0.9216
- F1 samples: 0.9216
- Precision micro: 0.8926
- Precision macro: 0.8430
- Precision weighted: 0.8949
- Precision samples: 0.9138
- Recall micro: 0.9510
- Recall macro: 0.9272
- Recall weighted: 0.9510
- Recall samples: 0.9564
- Roc Auc: 0.9622
- Accuracy: 0.7805

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.2394        | 1.0   | 2183 | 0.2237          | 0.9040   | 0.8670   | 0.9056      | 0.9069     | 0.8548          | 0.8161          | 0.8601             | 0.8857            | 0.9592       | 0.9364       | 0.9592          | 0.9624         | 0.9607  | 0.7319   |
| 0.1798        | 2.0   | 4366 | 0.2275          | 0.9171   | 0.8758   | 0.9182      | 0.9191     | 0.8855          | 0.8336          | 0.8888             | 0.9097            | 0.9510       | 0.9288       | 0.9510          | 0.9571         | 0.9612  | 0.7705   |
| 0.1408        | 3.0   | 6549 | 0.2409          | 0.9209   | 0.8813   | 0.9216      | 0.9216     | 0.8926          | 0.8430          | 0.8949             | 0.9138            | 0.9510       | 0.9272       | 0.9510          | 0.9564         | 0.9622  | 0.7805   |


### Framework versions

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