---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioELECTRA-LitCovid-v1.3h
  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. -->

# BioELECTRA-LitCovid-v1.3h

This model is a fine-tuned version of [kamalkraj/bioelectra-base-discriminator-pubmed](https://huggingface.co/kamalkraj/bioelectra-base-discriminator-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7785
- Hamming loss: 0.0198
- F1 micro: 0.8361
- F1 macro: 0.3559
- F1 weighted: 0.8787
- F1 samples: 0.8727
- Precision micro: 0.7527
- Precision macro: 0.2860
- Precision weighted: 0.8332
- Precision samples: 0.8513
- Recall micro: 0.9403
- Recall macro: 0.7383
- Recall weighted: 0.9403
- Recall samples: 0.9483
- Roc Auc: 0.9614
- Accuracy: 0.6748

## 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: 5e-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
- lr_scheduler_warmup_ratio: 0.1866747178469669
- num_epochs: 5
- mixed_precision_training: Native AMP

### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 1.5951        | 1.0   | 2272  | 0.6305          | 0.0577       | 0.6142   | 0.2231   | 0.7544      | 0.7325     | 0.4787          | 0.1794          | 0.7016             | 0.6957            | 0.8568       | 0.7122       | 0.8568          | 0.8824         | 0.9020  | 0.3878   |
| 1.1968        | 2.0   | 4544  | 0.4865          | 0.0353       | 0.7393   | 0.2825   | 0.8470      | 0.8156     | 0.6122          | 0.2305          | 0.7890             | 0.7790            | 0.9330       | 0.7538       | 0.9330          | 0.9449         | 0.9497  | 0.5560   |
| 0.9573        | 3.0   | 6816  | 0.5637          | 0.0247       | 0.8033   | 0.3292   | 0.8474      | 0.8430     | 0.7019          | 0.2574          | 0.7851             | 0.8066            | 0.9389       | 0.7380       | 0.9389          | 0.9470         | 0.9582  | 0.5918   |
| 0.7604        | 4.0   | 9088  | 0.6811          | 0.0206       | 0.8306   | 0.3558   | 0.8726      | 0.8675     | 0.7441          | 0.2835          | 0.8239             | 0.8438            | 0.9400       | 0.7574       | 0.9400          | 0.9483         | 0.9608  | 0.6561   |
| 0.4404        | 5.0   | 11360 | 0.7785          | 0.0198       | 0.8361   | 0.3559   | 0.8787      | 0.8727     | 0.7527          | 0.2860          | 0.8332             | 0.8513            | 0.9403       | 0.7383       | 0.9403          | 0.9483         | 0.9614  | 0.6748   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.13.3