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tmvar_0.0001

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0162
  • Precision: 0.8877
  • Recall: 0.8973
  • F1: 0.8925
  • Accuracy: 0.9971

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: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 500

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.2263 1.47 25 0.0776 0.0 0.0 0.0 0.9843
0.05 2.94 50 0.0400 0.2868 0.4216 0.3414 0.9872
0.0271 4.41 75 0.0219 0.5381 0.6486 0.5882 0.9925
0.0108 5.88 100 0.0132 0.8324 0.8324 0.8324 0.9965
0.0029 7.35 125 0.0107 0.8934 0.9514 0.9215 0.9979
0.0025 8.82 150 0.0123 0.8691 0.8973 0.8830 0.9972
0.0011 10.29 175 0.0127 0.8579 0.9135 0.8848 0.9969
0.0006 11.76 200 0.0102 0.8969 0.9405 0.9182 0.9981
0.0005 13.24 225 0.0118 0.8942 0.9135 0.9037 0.9978
0.0005 14.71 250 0.0106 0.8768 0.9622 0.9175 0.9981
0.0015 16.18 275 0.0119 0.855 0.9243 0.8883 0.9976
0.0006 17.65 300 0.0134 0.8814 0.9243 0.9024 0.9977
0.0004 19.12 325 0.0177 0.8617 0.8757 0.8686 0.9969
0.0003 20.59 350 0.0162 0.8877 0.8973 0.8925 0.9971

Framework versions

  • Transformers 4.27.4
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.2
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