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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioLinkBERT-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. -->
# BioLinkBERT-LitCovid-1.4
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5613
- Hamming loss: 0.0775
- F1 micro: 0.6253
- F1 macro: 0.4797
- F1 weighted: 0.7043
- F1 samples: 0.6321
- Precision micro: 0.4806
- Precision macro: 0.3631
- Precision weighted: 0.6169
- Precision samples: 0.5276
- Recall micro: 0.8947
- Recall macro: 0.8442
- Recall weighted: 0.8947
- Recall samples: 0.9099
- Roc Auc: 0.9097
- Accuracy: 0.0849
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.6654 | 1.0 | 1151 | 0.6313 | 0.1143 | 0.5259 | 0.3963 | 0.6460 | 0.5359 | 0.3756 | 0.2909 | 0.5586 | 0.4182 | 0.8764 | 0.8497 | 0.8764 | 0.8940 | 0.8814 | 0.0227 |
| 0.5313 | 2.0 | 2303 | 0.5682 | 0.0997 | 0.5655 | 0.4266 | 0.6717 | 0.5784 | 0.4128 | 0.3161 | 0.5789 | 0.4624 | 0.8972 | 0.8620 | 0.8972 | 0.9120 | 0.8988 | 0.0492 |
| 0.4594 | 3.0 | 3454 | 0.5529 | 0.0884 | 0.5938 | 0.4517 | 0.6907 | 0.6012 | 0.4446 | 0.3394 | 0.6041 | 0.4883 | 0.8939 | 0.8549 | 0.8939 | 0.9094 | 0.9034 | 0.0586 |
| 0.3966 | 4.0 | 4606 | 0.5580 | 0.0797 | 0.6193 | 0.4739 | 0.7014 | 0.6245 | 0.4731 | 0.3579 | 0.6129 | 0.5166 | 0.8965 | 0.8476 | 0.8965 | 0.9109 | 0.9093 | 0.0751 |
| 0.3693 | 5.0 | 5755 | 0.5613 | 0.0775 | 0.6253 | 0.4797 | 0.7043 | 0.6321 | 0.4806 | 0.3631 | 0.6169 | 0.5276 | 0.8947 | 0.8442 | 0.8947 | 0.9099 | 0.9097 | 0.0849 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3
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