---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioElectra-LitCovid-1.4
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# BioElectra-LitCovid-1.4

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: 0.6551
- Hamming loss: 0.1096
- F1 micro: 0.5375
- F1 macro: 0.4017
- F1 weighted: 0.6519
- F1 samples: 0.5520
- Precision micro: 0.3867
- Precision macro: 0.2948
- Precision weighted: 0.5638
- Precision samples: 0.4347
- Recall micro: 0.8813
- Recall macro: 0.8425
- Recall weighted: 0.8813
- Recall samples: 0.8977
- Roc Auc: 0.8862
- Accuracy: 0.0375

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.8117        | 1.0   | 1151 | 0.7562          | 0.1732       | 0.4140   | 0.3137   | 0.5784      | 0.4179     | 0.2740          | 0.2255          | 0.4949             | 0.2947            | 0.8462       | 0.8285       | 0.8462          | 0.8675         | 0.8357  | 0.0005   |
| 0.639         | 2.0   | 2303 | 0.6690          | 0.1346       | 0.4836   | 0.3618   | 0.6199      | 0.4952     | 0.3347          | 0.2629          | 0.5289             | 0.3716            | 0.8714       | 0.8448       | 0.8714          | 0.8906         | 0.8682  | 0.0095   |
| 0.556         | 3.0   | 3454 | 0.6453          | 0.1253       | 0.5012   | 0.3747   | 0.6358      | 0.5147     | 0.3519          | 0.2750          | 0.5539             | 0.3944            | 0.8706       | 0.8536       | 0.8706          | 0.8895         | 0.8728  | 0.0220   |
| 0.4906        | 4.0   | 4606 | 0.6567          | 0.1111       | 0.5339   | 0.4013   | 0.6494      | 0.5469     | 0.3832          | 0.2946          | 0.5608             | 0.4282            | 0.8800       | 0.8428       | 0.8800          | 0.8976         | 0.8848  | 0.0312   |
| 0.4594        | 5.0   | 5755 | 0.6551          | 0.1096       | 0.5375   | 0.4017   | 0.6519      | 0.5520     | 0.3867          | 0.2948          | 0.5638             | 0.4347            | 0.8813       | 0.8425       | 0.8813          | 0.8977         | 0.8862  | 0.0375   |


### Framework versions

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