| | --- |
| | 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 |
| | |
