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license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioLinkBERT-Large-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-Large-LitCovid-1.4
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5976
- Hamming loss: 0.0604
- F1 micro: 0.6804
- F1 macro: 0.5425
- F1 weighted: 0.7357
- F1 samples: 0.6807
- Precision micro: 0.5509
- Precision macro: 0.4271
- Precision weighted: 0.6552
- Precision samples: 0.5921
- Recall micro: 0.8895
- Recall macro: 0.8221
- Recall weighted: 0.8895
- Recall samples: 0.9063
- Roc Auc: 0.9165
- Accuracy: 0.1370
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.5869 | 1.0 | 1151 | 0.5737 | 0.0978 | 0.5682 | 0.4375 | 0.6759 | 0.5754 | 0.4172 | 0.3269 | 0.5906 | 0.4591 | 0.8901 | 0.8593 | 0.8901 | 0.9076 | 0.8966 | 0.0421 |
| 0.4636 | 2.0 | 2302 | 0.5316 | 0.0805 | 0.6179 | 0.4702 | 0.7052 | 0.6237 | 0.4704 | 0.3554 | 0.6181 | 0.5153 | 0.9005 | 0.8611 | 0.9005 | 0.9160 | 0.9107 | 0.0812 |
| 0.3782 | 3.0 | 3453 | 0.5382 | 0.0760 | 0.6321 | 0.4929 | 0.7146 | 0.6327 | 0.4864 | 0.3757 | 0.6293 | 0.5230 | 0.9027 | 0.8556 | 0.9027 | 0.9183 | 0.9142 | 0.0797 |
| 0.3031 | 4.0 | 4605 | 0.5807 | 0.0619 | 0.6754 | 0.5346 | 0.7343 | 0.6744 | 0.5437 | 0.4189 | 0.6531 | 0.5820 | 0.8915 | 0.8274 | 0.8915 | 0.9089 | 0.9166 | 0.1235 |
| 0.2625 | 5.0 | 5755 | 0.5976 | 0.0604 | 0.6804 | 0.5425 | 0.7357 | 0.6807 | 0.5509 | 0.4271 | 0.6552 | 0.5921 | 0.8895 | 0.8221 | 0.8895 | 0.9063 | 0.9165 | 0.1370 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3
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