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BioBERT-LitCovid-v1.3hh

Text Classification
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bert
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text-embeddings-inference
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: BioBERT-LitCovid-v1.3hh
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # BioBERT-LitCovid-v1.3hh
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+
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+ This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9050
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+ - Hamming loss: 0.0147
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+ - F1 micro: 0.8717
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+ - F1 macro: 0.4368
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+ - F1 weighted: 0.8882
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+ - F1 samples: 0.8857
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+ - Precision micro: 0.8176
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+ - Precision macro: 0.3560
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+ - Precision weighted: 0.8520
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+ - Precision samples: 0.8728
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+ - Recall micro: 0.9334
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+ - Recall macro: 0.7011
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+ - Recall weighted: 0.9334
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+ - Recall samples: 0.9438
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+ - Roc Auc: 0.9608
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+ - Accuracy: 0.7014
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.11492820779210673
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+ - num_epochs: 5
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | 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 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
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+ | 1.1889 | 1.0 | 2272 | 0.4213 | 0.0512 | 0.6596 | 0.2446 | 0.8084 | 0.7608 | 0.5126 | 0.1941 | 0.7385 | 0.7077 | 0.9250 | 0.8376 | 0.9250 | 0.9404 | 0.9376 | 0.4492 |
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+ | 0.8405 | 2.0 | 4544 | 0.4523 | 0.0234 | 0.8101 | 0.3434 | 0.8586 | 0.8435 | 0.7177 | 0.2700 | 0.8104 | 0.8130 | 0.9296 | 0.7802 | 0.9296 | 0.9421 | 0.9544 | 0.5954 |
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+ | 0.6991 | 3.0 | 6816 | 0.5218 | 0.0214 | 0.8253 | 0.3595 | 0.8703 | 0.8563 | 0.7327 | 0.2829 | 0.8184 | 0.8238 | 0.9447 | 0.7721 | 0.9447 | 0.9534 | 0.9626 | 0.6190 |
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+ | 0.3865 | 4.0 | 9088 | 0.8428 | 0.0155 | 0.8655 | 0.4279 | 0.8826 | 0.8808 | 0.8092 | 0.3453 | 0.8458 | 0.8667 | 0.9302 | 0.6992 | 0.9302 | 0.9417 | 0.9589 | 0.6917 |
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+ | 0.1332 | 5.0 | 11360 | 0.9050 | 0.0147 | 0.8717 | 0.4368 | 0.8882 | 0.8857 | 0.8176 | 0.3560 | 0.8520 | 0.8728 | 0.9334 | 0.7011 | 0.9334 | 0.9438 | 0.9608 | 0.7014 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.0
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.13.3