distilbert-base-uncased_fold_9_ternary_v1

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

  • Loss: 1.9406
  • F1: 0.7841

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 25

Training results

Training Loss Epoch Step Validation Loss F1
No log 1.0 292 0.5684 0.7635
0.5656 2.0 584 0.5753 0.7725
0.5656 3.0 876 0.6159 0.7866
0.2499 4.0 1168 0.7743 0.7828
0.2499 5.0 1460 0.9820 0.7674
0.1153 6.0 1752 1.2383 0.7738
0.0547 7.0 2044 1.2468 0.7815
0.0547 8.0 2336 1.3480 0.7622
0.0233 9.0 2628 1.3791 0.7892
0.0233 10.0 2920 1.4344 0.7841
0.0142 11.0 3212 1.4958 0.7802
0.0087 12.0 3504 1.5714 0.7674
0.0087 13.0 3796 1.6129 0.7956
0.0111 14.0 4088 1.7799 0.7751
0.0111 15.0 4380 1.7272 0.7789
0.0055 16.0 4672 1.7696 0.7866
0.0055 17.0 4964 1.8622 0.7789
0.003 18.0 5256 1.8563 0.7802
0.0004 19.0 5548 1.8993 0.7815
0.0004 20.0 5840 1.9199 0.7853
0.0005 21.0 6132 1.9003 0.7879
0.0005 22.0 6424 1.9161 0.7828
0.0011 23.0 6716 1.9691 0.7815
0.0017 24.0 7008 1.9492 0.7841
0.0017 25.0 7300 1.9406 0.7841

Framework versions

  • Transformers 4.21.0
  • Pytorch 1.12.0+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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