| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: PubMedBERT-LitCovid-v1.2 |
| | 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-v1.2 |
| |
|
| | This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0998 |
| | - F1: 0.9200 |
| | - Roc Auc: 0.9529 |
| | - Accuracy: 0.7868 |
| |
|
| | ## 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 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 5 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
| | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:| |
| | | 0.1017 | 1.0 | 2211 | 0.0897 | 0.9155 | 0.9492 | 0.7722 | |
| | | 0.0742 | 2.0 | 4422 | 0.0868 | 0.9177 | 0.9508 | 0.7778 | |
| | | 0.0559 | 3.0 | 6633 | 0.0903 | 0.9191 | 0.9521 | 0.7827 | |
| | | 0.0396 | 4.0 | 8844 | 0.0955 | 0.9184 | 0.9512 | 0.7814 | |
| | | 0.0282 | 5.0 | 11055 | 0.0998 | 0.9200 | 0.9529 | 0.7868 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.28.0 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.1.0 |
| | - Tokenizers 0.13.3 |
| |
|
