---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: bioformers/bioformer-litcovid
model-index:
- name: Bioformer-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. -->

# Bioformer-LitCovid-v1.2.3

This model is a fine-tuned version of [bioformers/bioformer-litcovid](https://huggingface.co/bioformers/bioformer-litcovid) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9317
- F1 micro: 0.7843
- F1 macro: 0.2816
- F1 weighted: 0.8523
- F1 samples: 0.8647
- Precision micro: 0.6737
- Precision macro: 0.2255
- Precision weighted: 0.7914
- Precision samples: 0.8417
- Recall micro: 0.9384
- Recall macro: 0.7715
- Recall weighted: 0.9384
- Recall samples: 0.9468
- Roc Auc: 0.9568
- Accuracy: 0.6515

## 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: 32
- eval_batch_size: 32
- 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 | 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.8024        | 1.0   | 1136 | 0.6487          | 0.6577   | 0.2288   | 0.7528      | 0.7600     | 0.5118          | 0.1747          | 0.6503             | 0.7007            | 0.9198       | 0.8185       | 0.9198          | 0.9253         | 0.9361  | 0.3919   |
| 0.639         | 2.0   | 2272 | 0.8280          | 0.7187   | 0.2482   | 0.8039      | 0.8088     | 0.5833          | 0.1935          | 0.7200             | 0.7602            | 0.9361       | 0.7535       | 0.9361          | 0.9441         | 0.9499  | 0.4986   |
| 0.5167        | 3.0   | 3408 | 0.8318          | 0.7589   | 0.2686   | 0.8342      | 0.8469     | 0.6372          | 0.2127          | 0.7628             | 0.8153            | 0.9382       | 0.7903       | 0.9382          | 0.9462         | 0.9546  | 0.6008   |
| 0.3641        | 4.0   | 4544 | 0.9231          | 0.7793   | 0.2788   | 0.8472      | 0.8578     | 0.6644          | 0.2220          | 0.7815             | 0.8290            | 0.9422       | 0.7678       | 0.9422          | 0.9497         | 0.9582  | 0.6311   |
| 0.3754        | 5.0   | 5680 | 0.9317          | 0.7843   | 0.2816   | 0.8523      | 0.8647     | 0.6737          | 0.2255          | 0.7914             | 0.8417            | 0.9384       | 0.7715       | 0.9384          | 0.9468         | 0.9568  | 0.6515   |


### Framework versions

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