| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: Bioformer-LitCovid-v1.4h |
| | 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-v1.4h |
| |
|
| | 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: 0.5733 |
| | - Hamming loss: 0.0842 |
| | - F1 micro: 0.6047 |
| | - F1 macro: 0.4622 |
| | - F1 weighted: 0.6887 |
| | - F1 samples: 0.6127 |
| | - Precision micro: 0.4576 |
| | - Precision macro: 0.3466 |
| | - Precision weighted: 0.5990 |
| | - Precision samples: 0.5038 |
| | - Recall micro: 0.8912 |
| | - Recall macro: 0.8446 |
| | - Recall weighted: 0.8912 |
| | - Recall samples: 0.9055 |
| | - Roc Auc: 0.9044 |
| | - Accuracy: 0.0708 |
| |
|
| | ## 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: 5.451682398151845e-05 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 64 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_ratio: 0.08129918921555689 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:| |
| | | 0.9164 | 1.0 | 576 | 0.6810 | 0.1510 | 0.4505 | 0.3468 | 0.6199 | 0.4653 | 0.3057 | 0.2568 | 0.5483 | 0.3450 | 0.8564 | 0.8656 | 0.8564 | 0.8750 | 0.8524 | 0.0078 | |
| | | 0.6032 | 2.0 | 1152 | 0.5983 | 0.1154 | 0.5273 | 0.4002 | 0.6493 | 0.5373 | 0.3746 | 0.2939 | 0.5587 | 0.4139 | 0.8902 | 0.8651 | 0.8902 | 0.9050 | 0.8872 | 0.0263 | |
| | | 0.4965 | 3.0 | 1728 | 0.5752 | 0.0975 | 0.5704 | 0.4372 | 0.6709 | 0.5795 | 0.4185 | 0.3237 | 0.5797 | 0.4617 | 0.8952 | 0.8536 | 0.8952 | 0.9089 | 0.8991 | 0.0479 | |
| | | 0.4354 | 4.0 | 2304 | 0.5655 | 0.0863 | 0.5978 | 0.4554 | 0.6872 | 0.6050 | 0.4508 | 0.3406 | 0.6021 | 0.4948 | 0.8870 | 0.8503 | 0.8870 | 0.9024 | 0.9014 | 0.0636 | |
| | | 0.3874 | 5.0 | 2880 | 0.5733 | 0.0842 | 0.6047 | 0.4622 | 0.6887 | 0.6127 | 0.4576 | 0.3466 | 0.5990 | 0.5038 | 0.8912 | 0.8446 | 0.8912 | 0.9055 | 0.9044 | 0.0708 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.28.0 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.1.0 |
| | - Tokenizers 0.13.3 |
| |
|
