---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Bioformer-LitCovid-v1.4h
  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-v1.4h

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: 0.5733
- Hamming loss: 0.0842
- F1 micro: 0.6047
- F1 macro: 0.4622
- F1 weighted: 0.6887
- F1 samples: 0.6127
- Precision micro: 0.4576
- Precision macro: 0.3466
- Precision weighted: 0.5990
- Precision samples: 0.5038
- Recall micro: 0.8912
- Recall macro: 0.8446
- Recall weighted: 0.8912
- Recall samples: 0.9055
- Roc Auc: 0.9044
- Accuracy: 0.0708

## 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: 5.451682398151845e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.08129918921555689
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.9164        | 1.0   | 576  | 0.6810          | 0.1510       | 0.4505   | 0.3468   | 0.6199      | 0.4653     | 0.3057          | 0.2568          | 0.5483             | 0.3450            | 0.8564       | 0.8656       | 0.8564          | 0.8750         | 0.8524  | 0.0078   |
| 0.6032        | 2.0   | 1152 | 0.5983          | 0.1154       | 0.5273   | 0.4002   | 0.6493      | 0.5373     | 0.3746          | 0.2939          | 0.5587             | 0.4139            | 0.8902       | 0.8651       | 0.8902          | 0.9050         | 0.8872  | 0.0263   |
| 0.4965        | 3.0   | 1728 | 0.5752          | 0.0975       | 0.5704   | 0.4372   | 0.6709      | 0.5795     | 0.4185          | 0.3237          | 0.5797             | 0.4617            | 0.8952       | 0.8536       | 0.8952          | 0.9089         | 0.8991  | 0.0479   |
| 0.4354        | 4.0   | 2304 | 0.5655          | 0.0863       | 0.5978   | 0.4554   | 0.6872      | 0.6050     | 0.4508          | 0.3406          | 0.6021             | 0.4948            | 0.8870       | 0.8503       | 0.8870          | 0.9024         | 0.9014  | 0.0636   |
| 0.3874        | 5.0   | 2880 | 0.5733          | 0.0842       | 0.6047   | 0.4622   | 0.6887      | 0.6127     | 0.4576          | 0.3466          | 0.5990             | 0.5038            | 0.8912       | 0.8446       | 0.8912          | 0.9055         | 0.9044  | 0.0708   |


### Framework versions

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