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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: PubMedBERT-Large-LitCovid-1.4
    results: []

PubMedBERT-Large-LitCovid-1.4

This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6105
  • Hamming loss: 0.0623
  • F1 micro: 0.6724
  • F1 macro: 0.5303
  • F1 weighted: 0.7292
  • F1 samples: 0.6741
  • Precision micro: 0.5423
  • Precision macro: 0.4146
  • Precision weighted: 0.6499
  • Precision samples: 0.5845
  • Recall micro: 0.8849
  • Recall macro: 0.8178
  • Recall weighted: 0.8849
  • Recall samples: 0.9022
  • Roc Auc: 0.9133
  • Accuracy: 0.1313

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
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5
  • mixed_precision_training: Native AMP

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
0.589 1.0 1151 0.5719 0.1031 0.5554 0.4307 0.6704 0.5629 0.4034 0.3213 0.5843 0.4435 0.8909 0.8673 0.8909 0.9062 0.8941 0.0363
0.4668 2.0 2302 0.5438 0.0836 0.6082 0.4623 0.6974 0.6147 0.4599 0.3478 0.6098 0.5052 0.8976 0.8556 0.8976 0.9123 0.9077 0.0774
0.3791 3.0 3453 0.5510 0.0790 0.6225 0.4829 0.7070 0.6247 0.4754 0.3661 0.6205 0.5140 0.9012 0.8541 0.9012 0.9165 0.9119 0.0759
0.307 4.0 4605 0.5954 0.0635 0.6688 0.5235 0.7280 0.6689 0.5371 0.4078 0.6477 0.5767 0.8863 0.8212 0.8863 0.9036 0.9134 0.1229
0.2687 5.0 5755 0.6105 0.0623 0.6724 0.5303 0.7292 0.6741 0.5423 0.4146 0.6499 0.5845 0.8849 0.8178 0.8849 0.9022 0.9133 0.1313

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.13.3