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---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioELECTRA-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. -->
# 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
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