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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: PubMedBERT-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. -->
# PubMedBERT-Large-LitCovid-1.4
This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6105
- Hamming loss: 0.0623
- F1 micro: 0.6724
- F1 macro: 0.5303
- F1 weighted: 0.7292
- F1 samples: 0.6741
- Precision micro: 0.5423
- Precision macro: 0.4146
- Precision weighted: 0.6499
- Precision samples: 0.5845
- Recall micro: 0.8849
- Recall macro: 0.8178
- Recall weighted: 0.8849
- Recall samples: 0.9022
- Roc Auc: 0.9133
- Accuracy: 0.1313
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.589 | 1.0 | 1151 | 0.5719 | 0.1031 | 0.5554 | 0.4307 | 0.6704 | 0.5629 | 0.4034 | 0.3213 | 0.5843 | 0.4435 | 0.8909 | 0.8673 | 0.8909 | 0.9062 | 0.8941 | 0.0363 |
| 0.4668 | 2.0 | 2302 | 0.5438 | 0.0836 | 0.6082 | 0.4623 | 0.6974 | 0.6147 | 0.4599 | 0.3478 | 0.6098 | 0.5052 | 0.8976 | 0.8556 | 0.8976 | 0.9123 | 0.9077 | 0.0774 |
| 0.3791 | 3.0 | 3453 | 0.5510 | 0.0790 | 0.6225 | 0.4829 | 0.7070 | 0.6247 | 0.4754 | 0.3661 | 0.6205 | 0.5140 | 0.9012 | 0.8541 | 0.9012 | 0.9165 | 0.9119 | 0.0759 |
| 0.307 | 4.0 | 4605 | 0.5954 | 0.0635 | 0.6688 | 0.5235 | 0.7280 | 0.6689 | 0.5371 | 0.4078 | 0.6477 | 0.5767 | 0.8863 | 0.8212 | 0.8863 | 0.9036 | 0.9134 | 0.1229 |
| 0.2687 | 5.0 | 5755 | 0.6105 | 0.0623 | 0.6724 | 0.5303 | 0.7292 | 0.6741 | 0.5423 | 0.4146 | 0.6499 | 0.5845 | 0.8849 | 0.8178 | 0.8849 | 0.9022 | 0.9133 | 0.1313 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3
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