---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: PubMedELECTRA-LitCovid-1.4
  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. -->

# PubMedELECTRA-LitCovid-1.4

This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedELECTRA-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedELECTRA-base-uncased-abstract) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5898
- Hamming loss: 0.0967
- F1 micro: 0.5691
- F1 macro: 0.4329
- F1 weighted: 0.6693
- F1 samples: 0.5791
- Precision micro: 0.4198
- Precision macro: 0.3211
- Precision weighted: 0.5820
- Precision samples: 0.4666
- Recall micro: 0.8834
- Recall macro: 0.8456
- Recall weighted: 0.8834
- Recall samples: 0.8983
- Roc Auc: 0.8941
- Accuracy: 0.0504

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.7482        | 1.0   | 1151 | 0.7061          | 0.1518       | 0.4528   | 0.3484   | 0.6073      | 0.4584     | 0.3063          | 0.2528          | 0.5187             | 0.3313            | 0.8684       | 0.8422       | 0.8684          | 0.8869         | 0.8575  | 0.0023   |
| 0.5987        | 2.0   | 2303 | 0.6241          | 0.1287       | 0.4983   | 0.3783   | 0.6327      | 0.5120     | 0.3469          | 0.2766          | 0.5412             | 0.3888            | 0.8840       | 0.8571       | 0.8840          | 0.8996         | 0.8771  | 0.0193   |
| 0.5194        | 3.0   | 3454 | 0.5960          | 0.1079       | 0.5399   | 0.4108   | 0.6584      | 0.5500     | 0.3903          | 0.3056          | 0.5764             | 0.4339            | 0.8752       | 0.8513       | 0.8752          | 0.8921         | 0.8843  | 0.0351   |
| 0.4471        | 4.0   | 4606 | 0.5900          | 0.0982       | 0.5653   | 0.4286   | 0.6681      | 0.5747     | 0.4157          | 0.3179          | 0.5810             | 0.4609            | 0.8830       | 0.8468       | 0.8830          | 0.8983         | 0.8931  | 0.0460   |
| 0.422         | 5.0   | 5755 | 0.5898          | 0.0967       | 0.5691   | 0.4329   | 0.6693      | 0.5791     | 0.4198          | 0.3211          | 0.5820             | 0.4666            | 0.8834       | 0.8456       | 0.8834          | 0.8983         | 0.8941  | 0.0504   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3