| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: bioformer-LitCovid-v1.0 |
| | 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. --> |
| |
|
| | # bioformer-LitCovid-v1.0 |
| |
|
| | This model is a fine-tuned version of [bioformers/bioformer-litcovid](https://huggingface.co/bioformers/bioformer-litcovid) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1191 |
| | - F1: 0.8969 |
| | - Roc Auc: 0.9366 |
| | - Accuracy: 0.7895 |
| |
|
| | ## 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 |
| | - 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 | |
| | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:| |
| | | 0.1209 | 1.0 | 3120 | 0.1164 | 0.8921 | 0.9300 | 0.7863 | |
| | | 0.1058 | 2.0 | 6240 | 0.1163 | 0.8909 | 0.9283 | 0.7825 | |
| | | 0.0795 | 3.0 | 9360 | 0.1135 | 0.8963 | 0.9404 | 0.7881 | |
| | | 0.0649 | 4.0 | 12480 | 0.1156 | 0.8989 | 0.9384 | 0.7953 | |
| | | 0.0504 | 5.0 | 15600 | 0.1191 | 0.8969 | 0.9366 | 0.7895 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.28.0 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.3 |
| |
|
