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bert

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

  • Loss: 0.3752
  • Precision: 0.5495
  • Recall: 0.5949
  • F1: 0.5713
  • Accuracy: 0.9455

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: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 151 0.1826 0.4095 0.4084 0.4089 0.9362
No log 2.0 302 0.1684 0.4941 0.5303 0.5116 0.9442
No log 3.0 453 0.2528 0.5197 0.4477 0.4810 0.9398
0.1001 4.0 604 0.2100 0.5182 0.5583 0.5375 0.9439
0.1001 5.0 755 0.2556 0.5207 0.4783 0.4986 0.9419
0.1001 6.0 906 0.2908 0.4132 0.4204 0.4168 0.9365
0.0205 7.0 1057 0.3046 0.5 0.6236 0.5550 0.9435
0.0205 8.0 1208 0.3057 0.5324 0.5750 0.5529 0.9458
0.0205 9.0 1359 0.3122 0.5626 0.5776 0.5700 0.9469
0.0082 10.0 1510 0.3673 0.5733 0.5263 0.5488 0.9441
0.0082 11.0 1661 0.3432 0.5482 0.5270 0.5374 0.9455
0.0082 12.0 1812 0.3305 0.5590 0.5716 0.5652 0.9445
0.0082 13.0 1963 0.3293 0.5434 0.6009 0.5707 0.9431
0.005 14.0 2114 0.4080 0.5627 0.5803 0.5713 0.9451
0.005 15.0 2265 0.3752 0.5495 0.5949 0.5713 0.9455
0.005 16.0 2416 0.4140 0.5823 0.5470 0.5641 0.9455
0.002 17.0 2567 0.4308 0.5555 0.5670 0.5612 0.9438
0.002 18.0 2718 0.4389 0.5594 0.5676 0.5635 0.9436
0.002 19.0 2869 0.4463 0.5609 0.5676 0.5642 0.9444
0.0007 20.0 3020 0.4512 0.5648 0.5636 0.5642 0.9448

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.2
  • Tokenizers 0.13.3
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