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BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa

Text Classification PyTorch TensorBoard Transformers mit bert generated_from_trainer Eval Results
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BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa

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: 0.6748
  • Accuracy: 0.72

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: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 57 0.8396 0.58
No log 2.0 114 0.8608 0.58
No log 3.0 171 0.7642 0.68
No log 4.0 228 0.8196 0.64
No log 5.0 285 0.6477 0.72
No log 6.0 342 0.6861 0.72
No log 7.0 399 0.6735 0.74
No log 8.0 456 0.6516 0.72
0.6526 9.0 513 0.6707 0.72
0.6526 10.0 570 0.6748 0.72

Framework versions

  • Transformers 4.10.2
  • Pytorch 1.9.0+cu102
  • Datasets 1.12.0
  • Tokenizers 0.10.3
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Evaluation results

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