| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: BioLinkBERT-Large-LitCovid-v1.3.1c |
| | 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. --> |
| |
|
| | # BioLinkBERT-Large-LitCovid-v1.3.1c |
| |
|
| | This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.1423 |
| | - Hamming loss: 0.0115 |
| | - F1 micro: 0.8955 |
| | - F1 macro: 0.5189 |
| | - F1 weighted: 0.8999 |
| | - F1 samples: 0.9001 |
| | - Precision micro: 0.8699 |
| | - Precision macro: 0.4571 |
| | - Precision weighted: 0.8797 |
| | - Precision samples: 0.8987 |
| | - Recall micro: 0.9228 |
| | - Recall macro: 0.6461 |
| | - Recall weighted: 0.9228 |
| | - Recall samples: 0.9346 |
| | - Roc Auc: 0.9575 |
| | - Accuracy: 0.7369 |
| |
|
| | ## 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: 4 |
| | - eval_batch_size: 4 |
| | - 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 | 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 | |
| | |:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:| |
| | | 2.3002 | 1.0 | 9086 | 0.8219 | 0.0155 | 0.8654 | 0.4167 | 0.8761 | 0.8797 | 0.8090 | 0.3554 | 0.8345 | 0.8627 | 0.9302 | 0.6257 | 0.9302 | 0.9414 | 0.9589 | 0.6796 | |
| | | 1.273 | 2.0 | 18172 | 0.7056 | 0.0145 | 0.8730 | 0.4396 | 0.8878 | 0.8898 | 0.8208 | 0.3638 | 0.8526 | 0.8772 | 0.9323 | 0.6919 | 0.9323 | 0.9439 | 0.9604 | 0.7050 | |
| | | 0.8734 | 3.0 | 27258 | 0.8218 | 0.0123 | 0.8896 | 0.4846 | 0.8969 | 0.8958 | 0.8557 | 0.4219 | 0.8729 | 0.8893 | 0.9264 | 0.6671 | 0.9264 | 0.9378 | 0.9588 | 0.7249 | |
| | | 0.7889 | 4.0 | 36344 | 0.9218 | 0.0118 | 0.8931 | 0.5037 | 0.9001 | 0.8983 | 0.8651 | 0.4391 | 0.8810 | 0.8966 | 0.9230 | 0.6716 | 0.9230 | 0.9349 | 0.9574 | 0.7333 | |
| | | 0.4284 | 5.0 | 45430 | 1.1423 | 0.0115 | 0.8955 | 0.5189 | 0.8999 | 0.9001 | 0.8699 | 0.4571 | 0.8797 | 0.8987 | 0.9228 | 0.6461 | 0.9228 | 0.9346 | 0.9575 | 0.7369 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.28.0 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.1.0 |
| | - Tokenizers 0.13.3 |
| |
|
