---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Bioformer-LitCovid-v1.3.1
  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. -->

# Bioformer-LitCovid-v1.3.1

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: 0.4639
- Hamming loss: 0.0375
- F1 micro: 0.7254
- F1 macro: 0.2721
- F1 weighted: 0.8153
- F1 samples: 0.8091
- Precision micro: 0.5970
- Precision macro: 0.2139
- Precision weighted: 0.7445
- Precision samples: 0.7700
- Recall micro: 0.9243
- Recall macro: 0.7966
- Recall weighted: 0.9243
- Recall samples: 0.9342
- Roc Auc: 0.9445
- Accuracy: 0.5243

## 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 | Hamming 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.9561        | 1.0   | 1136 | 0.5778          | 0.0745       | 0.5683   | 0.2036   | 0.7263      | 0.6631     | 0.4123          | 0.1552          | 0.6235             | 0.5852            | 0.9144       | 0.7912       | 0.9144          | 0.9216         | 0.9203  | 0.2653   |
| 0.7759        | 2.0   | 2272 | 0.4875          | 0.0440       | 0.6899   | 0.2545   | 0.7872      | 0.7686     | 0.5543          | 0.1978          | 0.7076             | 0.7196            | 0.9134       | 0.7626       | 0.9134          | 0.9238         | 0.9359  | 0.4380   |
| 0.6398        | 3.0   | 3408 | 0.4722          | 0.0385       | 0.7188   | 0.2699   | 0.8005      | 0.7910     | 0.5907          | 0.2101          | 0.7250             | 0.7463            | 0.9179       | 0.7580       | 0.9179          | 0.9274         | 0.9409  | 0.4832   |
| 0.5712        | 4.0   | 4544 | 0.4652          | 0.0374       | 0.7264   | 0.2754   | 0.8096      | 0.8018     | 0.5980          | 0.2151          | 0.7347             | 0.7582            | 0.9250       | 0.7774       | 0.9250          | 0.9343         | 0.9449  | 0.5034   |
| 0.4337        | 5.0   | 5680 | 0.4639          | 0.0375       | 0.7254   | 0.2721   | 0.8153      | 0.8091     | 0.5970          | 0.2139          | 0.7445             | 0.7700            | 0.9243       | 0.7966       | 0.9243          | 0.9342         | 0.9445  | 0.5243   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3