---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioELECTRA-LitCovid-v1.3.1
  results: []
---

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# BioELECTRA-LitCovid-v1.3.1

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: 0.6749
- Hamming loss: 0.0257
- F1 micro: 0.7955
- F1 macro: 0.3005
- F1 weighted: 0.8714
- F1 samples: 0.8642
- Precision micro: 0.6936
- Precision macro: 0.2470
- Precision weighted: 0.8294
- Precision samples: 0.8463
- Recall micro: 0.9326
- Recall macro: 0.7358
- Recall weighted: 0.9326
- Recall samples: 0.9427
- Roc Auc: 0.9546
- Accuracy: 0.6664

## 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
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 1.385         | 1.0   | 2272  | 0.6961          | 0.0592       | 0.6188   | 0.2176   | 0.7539      | 0.7422     | 0.4725          | 0.1706          | 0.6672             | 0.6890            | 0.8965       | 0.6896       | 0.8965          | 0.9065         | 0.9199  | 0.3887   |
| 1.2034        | 2.0   | 4544  | 0.6242          | 0.0342       | 0.7421   | 0.2668   | 0.8404      | 0.8354     | 0.6231          | 0.2180          | 0.7922             | 0.8120            | 0.9172       | 0.6872       | 0.9172          | 0.9319         | 0.9429  | 0.5906   |
| 1.0857        | 3.0   | 6816  | 0.6185          | 0.0270       | 0.7869   | 0.2949   | 0.8615      | 0.8587     | 0.6815          | 0.2402          | 0.8153             | 0.8382            | 0.9308       | 0.7164       | 0.9308          | 0.9437         | 0.9531  | 0.6444   |
| 0.8846        | 4.0   | 9088  | 0.6143          | 0.0260       | 0.7936   | 0.2994   | 0.8677      | 0.8626     | 0.6916          | 0.2460          | 0.8237             | 0.8444            | 0.9309       | 0.7254       | 0.9309          | 0.9421         | 0.9537  | 0.6594   |
| 0.6753        | 5.0   | 11360 | 0.6749          | 0.0257       | 0.7955   | 0.3005   | 0.8714      | 0.8642     | 0.6936          | 0.2470          | 0.8294             | 0.8463            | 0.9326       | 0.7358       | 0.9326          | 0.9427         | 0.9546  | 0.6664   |


### Framework versions

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