distilbert-base-uncased_fold_4_ternary

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: 1.2981
  • F1: 0.7565

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 1.0 289 0.5588 0.6984
0.5547 2.0 578 0.5283 0.7336
0.5547 3.0 867 0.7038 0.7202
0.2479 4.0 1156 0.8949 0.7284
0.2479 5.0 1445 0.9959 0.7286
0.1181 6.0 1734 1.0663 0.7311
0.0508 7.0 2023 1.2377 0.7054
0.0508 8.0 2312 1.2981 0.7565
0.0185 9.0 2601 1.3532 0.7407
0.0185 10.0 2890 1.5365 0.7333
0.0103 11.0 3179 1.5184 0.7423
0.0103 12.0 3468 1.6009 0.7420
0.0123 13.0 3757 1.6395 0.7402
0.008 14.0 4046 1.6838 0.7429
0.008 15.0 4335 1.6176 0.7490
0.0012 16.0 4624 1.7873 0.7345
0.0012 17.0 4913 1.6761 0.7412
0.0044 18.0 5202 1.7356 0.7417
0.0044 19.0 5491 1.7686 0.7502
0.0045 20.0 5780 1.7668 0.7406
0.0017 21.0 6069 1.8411 0.7381
0.0017 22.0 6358 1.8147 0.7469
0.0012 23.0 6647 1.8028 0.7489
0.0012 24.0 6936 1.8147 0.7453
0.0026 25.0 7225 1.8257 0.7475

Framework versions

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