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license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Bioformer-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. -->
# Bioformer-LitCovid-v1.3.1
This model is a fine-tuned version of [bioformers/bioformer-litcovid](https://huggingface.co/bioformers/bioformer-litcovid) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4639
- Hamming loss: 0.0375
- F1 micro: 0.7254
- F1 macro: 0.2721
- F1 weighted: 0.8153
- F1 samples: 0.8091
- Precision micro: 0.5970
- Precision macro: 0.2139
- Precision weighted: 0.7445
- Precision samples: 0.7700
- Recall micro: 0.9243
- Recall macro: 0.7966
- Recall weighted: 0.9243
- Recall samples: 0.9342
- Roc Auc: 0.9445
- Accuracy: 0.5243
## 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: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.9561 | 1.0 | 1136 | 0.5778 | 0.0745 | 0.5683 | 0.2036 | 0.7263 | 0.6631 | 0.4123 | 0.1552 | 0.6235 | 0.5852 | 0.9144 | 0.7912 | 0.9144 | 0.9216 | 0.9203 | 0.2653 |
| 0.7759 | 2.0 | 2272 | 0.4875 | 0.0440 | 0.6899 | 0.2545 | 0.7872 | 0.7686 | 0.5543 | 0.1978 | 0.7076 | 0.7196 | 0.9134 | 0.7626 | 0.9134 | 0.9238 | 0.9359 | 0.4380 |
| 0.6398 | 3.0 | 3408 | 0.4722 | 0.0385 | 0.7188 | 0.2699 | 0.8005 | 0.7910 | 0.5907 | 0.2101 | 0.7250 | 0.7463 | 0.9179 | 0.7580 | 0.9179 | 0.9274 | 0.9409 | 0.4832 |
| 0.5712 | 4.0 | 4544 | 0.4652 | 0.0374 | 0.7264 | 0.2754 | 0.8096 | 0.8018 | 0.5980 | 0.2151 | 0.7347 | 0.7582 | 0.9250 | 0.7774 | 0.9250 | 0.9343 | 0.9449 | 0.5034 |
| 0.4337 | 5.0 | 5680 | 0.4639 | 0.0375 | 0.7254 | 0.2721 | 0.8153 | 0.8091 | 0.5970 | 0.2139 | 0.7445 | 0.7700 | 0.9243 | 0.7966 | 0.9243 | 0.9342 | 0.9445 | 0.5243 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
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