| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| datasets: |
| - marker-associations-binary-base |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: marker-associations-binary-base |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: marker-associations-binary-base |
| type: marker-associations-binary-base |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.7981651376146789 |
| - name: Recall |
| type: recall |
| value: 0.9560439560439561 |
| - name: F1 |
| type: f1 |
| value: 0.87 |
| - name: Accuracy |
| type: accuracy |
| value: 0.8884120171673819 |
| --- |
| |
| <!-- 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. --> |
|
|
| # marker-associations-binary-base |
|
|
| This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the marker-associations-binary-base dataset. |
| It achieves the following results on the evaluation set: |
|
|
| ### Gene Results |
| - Precision = 0.808 |
| - Recall = 0.940 |
| - F1 = 0.869 |
| - Accuracy = 0.862 |
| - AUC = 0.944 |
|
|
| ### Chemical Results |
| - Precision = 0.774 |
| - Recall = 1.0 |
| - F1 = 0.873 |
| - Accuracy = 0.926 |
| - AUC = 0.964 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 1 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Auc | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:| |
| | No log | 1.0 | 88 | 0.3266 | 0.8191 | 0.8462 | 0.8324 | 0.8670 | 0.9313 | |
| | No log | 2.0 | 176 | 0.3335 | 0.7870 | 0.9341 | 0.8543 | 0.8755 | 0.9465 | |
| | No log | 3.0 | 264 | 0.4243 | 0.7982 | 0.9560 | 0.87 | 0.8884 | 0.9516 | |
| | No log | 4.0 | 352 | 0.5388 | 0.825 | 0.7253 | 0.7719 | 0.8326 | 0.9384 | |
| | No log | 5.0 | 440 | 0.7101 | 0.8537 | 0.7692 | 0.8092 | 0.8584 | 0.9416 | |
| | 0.1824 | 6.0 | 528 | 0.6175 | 0.8242 | 0.8242 | 0.8242 | 0.8627 | 0.9478 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.11.3 |
| - Pytorch 1.9.0+cu111 |
| - Tokenizers 0.10.3 |
|
|
