| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| datasets: |
| - marker-associations-snp-binary-base |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: marker-associations-snp-binary-base |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: marker-associations-snp-binary-base |
| type: marker-associations-snp-binary-base |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.9384057971014492 |
| - name: Recall |
| type: recall |
| value: 0.9055944055944056 |
| - name: F1 |
| type: f1 |
| value: 0.9217081850533808 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9107505070993914 |
| --- |
| |
| <!-- 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-snp-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-snp-binary-base dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4027 |
| - Precision: 0.9384 |
| - Recall: 0.9056 |
| - F1: 0.9217 |
| - Accuracy: 0.9108 |
| - Auc: 0.9578 |
|
|
| ## 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 | 153 | 0.2776 | 0.9 | 0.9441 | 0.9215 | 0.9067 | 0.9613 | |
| | No log | 2.0 | 306 | 0.4380 | 0.9126 | 0.9126 | 0.9126 | 0.8986 | 0.9510 | |
| | No log | 3.0 | 459 | 0.4027 | 0.9384 | 0.9056 | 0.9217 | 0.9108 | 0.9578 | |
| | 0.2215 | 4.0 | 612 | 0.3547 | 0.9449 | 0.8986 | 0.9211 | 0.9108 | 0.9642 | |
| | 0.2215 | 5.0 | 765 | 0.4465 | 0.9107 | 0.9266 | 0.9185 | 0.9047 | 0.9636 | |
| | 0.2215 | 6.0 | 918 | 0.5770 | 0.8970 | 0.9441 | 0.9199 | 0.9047 | 0.9666 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.11.3 |
| - Pytorch 1.9.0+cu111 |
| - Tokenizers 0.10.3 |
|
|
