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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: PubMedBERT-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-LitCovid-1.4
This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5628
- Hamming loss: 0.0745
- F1 micro: 0.6343
- F1 macro: 0.4913
- F1 weighted: 0.7105
- F1 samples: 0.6391
- Precision micro: 0.4918
- Precision macro: 0.3747
- Precision weighted: 0.6260
- Precision samples: 0.5363
- Recall micro: 0.8930
- Recall macro: 0.8406
- Recall weighted: 0.8930
- Recall samples: 0.9098
- Roc Auc: 0.9106
- Accuracy: 0.0952
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.6486 | 1.0 | 1151 | 0.6207 | 0.1099 | 0.5362 | 0.4107 | 0.6522 | 0.5433 | 0.3858 | 0.3021 | 0.5651 | 0.4237 | 0.8791 | 0.8500 | 0.8791 | 0.8964 | 0.8850 | 0.0234 |
| 0.5189 | 2.0 | 2303 | 0.5572 | 0.0981 | 0.5696 | 0.4299 | 0.6739 | 0.5815 | 0.4170 | 0.3178 | 0.5825 | 0.4655 | 0.8984 | 0.8672 | 0.8984 | 0.9143 | 0.9002 | 0.0501 |
| 0.4426 | 3.0 | 3454 | 0.5516 | 0.0853 | 0.6029 | 0.4632 | 0.6947 | 0.6086 | 0.4545 | 0.3493 | 0.6085 | 0.4966 | 0.8951 | 0.8538 | 0.8951 | 0.9116 | 0.9057 | 0.0650 |
| 0.3771 | 4.0 | 4606 | 0.5647 | 0.0735 | 0.6371 | 0.4944 | 0.7110 | 0.6402 | 0.4955 | 0.3779 | 0.6258 | 0.5377 | 0.8920 | 0.8363 | 0.8920 | 0.9087 | 0.9106 | 0.0924 |
| 0.3467 | 5.0 | 5755 | 0.5628 | 0.0745 | 0.6343 | 0.4913 | 0.7105 | 0.6391 | 0.4918 | 0.3747 | 0.6260 | 0.5363 | 0.8930 | 0.8406 | 0.8930 | 0.9098 | 0.9106 | 0.0952 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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