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bert-finetuned-ncbi

This model is a fine-tuned version of bert-base-cased on the ncbi_disease dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0679
  • Precision: 0.7807
  • Recall: 0.8640
  • F1: 0.8203
  • Accuracy: 0.9831

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
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.1146 1.0 680 0.0686 0.7450 0.8056 0.7741 0.9805
0.0458 2.0 1360 0.0612 0.7646 0.8628 0.8107 0.9815
0.0161 3.0 2040 0.0679 0.7807 0.8640 0.8203 0.9831

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2
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Dataset used to train Umesh/bert-finetuned-ncbi

Evaluation results