| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: Bioformer-LitCovid-v1.3h |
| | 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-v1.3h |
| |
|
| | 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: 0.8951 |
| | - Hamming loss: 0.0168 |
| | - F1 micro: 0.8565 |
| | - F1 macro: 0.3960 |
| | - F1 weighted: 0.8831 |
| | - F1 samples: 0.8789 |
| | - Precision micro: 0.7903 |
| | - Precision macro: 0.3221 |
| | - Precision weighted: 0.8426 |
| | - Precision samples: 0.8631 |
| | - Recall micro: 0.9348 |
| | - Recall macro: 0.6915 |
| | - Recall weighted: 0.9348 |
| | - Recall samples: 0.9435 |
| | - Roc Auc: 0.9604 |
| | - Accuracy: 0.6896 |
| |
|
| | ## 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: 0.0001 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 3257 |
| | - num_epochs: 5 |
| | |
| | ### 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 | |
| | |:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:| |
| | | 1.2033 | 1.0 | 2272 | 0.5628 | 0.0616 | 0.6107 | 0.2167 | 0.7918 | 0.7257 | 0.4618 | 0.1789 | 0.7347 | 0.6771 | 0.9014 | 0.7310 | 0.9014 | 0.9194 | 0.9209 | 0.3870 | |
| | | 1.2127 | 2.0 | 4544 | 0.5062 | 0.0325 | 0.7555 | 0.2834 | 0.8357 | 0.8037 | 0.6337 | 0.2273 | 0.7680 | 0.7535 | 0.9353 | 0.7100 | 0.9353 | 0.9434 | 0.9523 | 0.4954 | |
| | | 0.96 | 3.0 | 6816 | 0.4943 | 0.0245 | 0.8043 | 0.3363 | 0.8608 | 0.8409 | 0.7043 | 0.2676 | 0.8069 | 0.8048 | 0.9372 | 0.7637 | 0.9372 | 0.9477 | 0.9575 | 0.5735 | |
| | | 0.5852 | 4.0 | 9088 | 0.7306 | 0.0195 | 0.8371 | 0.3860 | 0.8687 | 0.8624 | 0.7568 | 0.3083 | 0.8212 | 0.8378 | 0.9365 | 0.7232 | 0.9365 | 0.9459 | 0.9597 | 0.6410 | |
| | | 0.3454 | 5.0 | 11360 | 0.8951 | 0.0168 | 0.8565 | 0.3960 | 0.8831 | 0.8789 | 0.7903 | 0.3221 | 0.8426 | 0.8631 | 0.9348 | 0.6915 | 0.9348 | 0.9435 | 0.9604 | 0.6896 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.0 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.1.0 |
| | - Tokenizers 0.13.3 |
| | |
