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