bert_small

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.4537
  • Accuracy: 0.88
  • Precision: 0.625
  • Recall: 0.3571
  • F1: 0.4545
  • D-index: 1.6429

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: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1600
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 D-index
No log 1.0 200 0.3773 0.86 0.0 0.0 0.0 1.4803
No log 2.0 400 0.4271 0.86 0.0 0.0 0.0 1.4803
0.5126 3.0 600 0.4598 0.87 0.55 0.3929 0.4583 1.6431
0.5126 4.0 800 0.6620 0.865 0.52 0.4643 0.4906 1.6624
0.2953 5.0 1000 0.8149 0.855 0.4615 0.2143 0.2927 1.5575
0.2953 6.0 1200 0.7819 0.875 0.5714 0.4286 0.4898 1.6623
0.2953 7.0 1400 1.0426 0.86 0.5 0.3571 0.4167 1.6173
0.1565 8.0 1600 1.0078 0.885 0.7273 0.2857 0.4103 1.6231
0.1565 9.0 1800 1.2939 0.865 0.6 0.1071 0.1818 1.5294
0.0643 10.0 2000 1.2661 0.88 0.6429 0.3214 0.4286 1.6299
0.0643 11.0 2200 1.3556 0.87 0.5833 0.25 0.3500 1.5905
0.0643 12.0 2400 1.2393 0.87 0.625 0.1786 0.2778 1.5635
0.0306 13.0 2600 1.3059 0.88 0.625 0.3571 0.4545 1.6429
0.0306 14.0 2800 1.3446 0.88 0.625 0.3571 0.4545 1.6429
0.0019 15.0 3000 1.3618 0.885 0.6471 0.3929 0.4889 1.6622
0.0019 16.0 3200 1.3785 0.885 0.6471 0.3929 0.4889 1.6622
0.0019 17.0 3400 1.4361 0.88 0.625 0.3571 0.4545 1.6429
0.0098 18.0 3600 1.4466 0.88 0.625 0.3571 0.4545 1.6429
0.0098 19.0 3800 1.4518 0.88 0.625 0.3571 0.4545 1.6429
0.0 20.0 4000 1.4537 0.88 0.625 0.3571 0.4545 1.6429

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3
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