marker-associations-binary-base

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the marker-associations-binary-base dataset. It achieves the following results on the evaluation set:

Gene Results

  • Precision = 0.808
  • Recall = 0.940
  • F1 = 0.869
  • Accuracy = 0.862
  • AUC = 0.944

Chemical Results

  • Precision = 0.774
  • Recall = 1.0
  • F1 = 0.873
  • Accuracy = 0.926
  • AUC = 0.964

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 1
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy Auc
No log 1.0 88 0.3266 0.8191 0.8462 0.8324 0.8670 0.9313
No log 2.0 176 0.3335 0.7870 0.9341 0.8543 0.8755 0.9465
No log 3.0 264 0.4243 0.7982 0.9560 0.87 0.8884 0.9516
No log 4.0 352 0.5388 0.825 0.7253 0.7719 0.8326 0.9384
No log 5.0 440 0.7101 0.8537 0.7692 0.8092 0.8584 0.9416
0.1824 6.0 528 0.6175 0.8242 0.8242 0.8242 0.8627 0.9478

Framework versions

  • Transformers 4.11.3
  • Pytorch 1.9.0+cu111
  • Tokenizers 0.10.3
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Text Classification
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Evaluation results