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bert-zs-sentence-classifier

Text Classification
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PyTorch
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bert
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bert-zs-sentence-classifier

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: 0.3663
  • F1: 0.8483

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: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss F1
0.5973 0.01 500 0.5186 0.7538
0.5021 0.03 1000 0.4646 0.7996
0.4741 0.04 1500 0.4634 0.8064
0.4656 0.06 2000 0.4485 0.8142
0.4567 0.07 2500 0.4345 0.8160
0.4448 0.09 3000 0.4239 0.8228
0.4403 0.1 3500 0.4155 0.8294
0.4163 0.12 4000 0.4021 0.8290
0.4205 0.13 4500 0.4057 0.8283
0.416 0.14 5000 0.4049 0.8319
0.4115 0.16 5500 0.4095 0.8280
0.4156 0.17 6000 0.3927 0.8349
0.4042 0.19 6500 0.4003 0.8392
0.4057 0.2 7000 0.3929 0.8385
0.3977 0.22 7500 0.3915 0.8406
0.4049 0.23 8000 0.3785 0.8433
0.4027 0.24 8500 0.3807 0.8424
0.4096 0.26 9000 0.3768 0.8435
0.3958 0.27 9500 0.3846 0.8420
0.4037 0.29 10000 0.3808 0.8381
0.3813 0.3 10500 0.4004 0.8415
0.3934 0.32 11000 0.3821 0.8422
0.3895 0.33 11500 0.3844 0.8428
0.3907 0.35 12000 0.3847 0.8435
0.3862 0.36 12500 0.3803 0.8431
0.3958 0.37 13000 0.3739 0.8392
0.3845 0.39 13500 0.3817 0.8422
0.3914 0.4 14000 0.3857 0.8424
0.3814 0.42 14500 0.3793 0.8438
0.3816 0.43 15000 0.3843 0.8395
0.4022 0.45 15500 0.3737 0.8436
0.3879 0.46 16000 0.3750 0.8424
0.3794 0.48 16500 0.3743 0.8410
0.393 0.49 17000 0.3733 0.8461
0.384 0.5 17500 0.3765 0.8476
0.3782 0.52 18000 0.3748 0.8451
0.3931 0.53 18500 0.3807 0.8454
0.3889 0.55 19000 0.3653 0.8463
0.386 0.56 19500 0.3707 0.8445
0.3802 0.58 20000 0.3700 0.8474
0.3883 0.59 20500 0.3646 0.8463
0.3825 0.61 21000 0.3665 0.8513
0.382 0.62 21500 0.3620 0.8508
0.3795 0.63 22000 0.3692 0.8493
0.367 0.65 22500 0.3704 0.8479
0.3825 0.66 23000 0.3723 0.8472
0.3902 0.68 23500 0.3681 0.8465
0.3813 0.69 24000 0.3668 0.8515
0.3878 0.71 24500 0.3632 0.8506
0.3743 0.72 25000 0.3728 0.8463
0.3826 0.73 25500 0.3746 0.8465
0.3892 0.75 26000 0.3602 0.8518
0.3767 0.76 26500 0.3722 0.8513
0.3724 0.78 27000 0.3716 0.8499
0.3767 0.79 27500 0.3651 0.8483
0.3846 0.81 28000 0.3753 0.8493
0.3748 0.82 28500 0.3720 0.8458
0.3768 0.84 29000 0.3663 0.8508
0.3716 0.85 29500 0.3635 0.8531
0.3673 0.86 30000 0.3659 0.8485
0.3805 0.88 30500 0.3608 0.8518
0.3718 0.89 31000 0.3695 0.8520
0.374 0.91 31500 0.3631 0.8485
0.3871 0.92 32000 0.3659 0.8485
0.3724 0.94 32500 0.3584 0.8518
0.3756 0.95 33000 0.3587 0.8492
0.3709 0.97 33500 0.3700 0.8488
0.376 0.98 34000 0.3657 0.8492
0.372 0.99 34500 0.3663 0.8483

Framework versions

  • Transformers 4.19.2
  • Pytorch 1.11.0+cu113
  • Datasets 2.2.2
  • Tokenizers 0.12.1
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Evaluation results

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