distilbert-base-uncased_fold_10_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: 1.9887
  • F1: 0.7797

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 1.0 290 0.5701 0.7463
0.5651 2.0 580 0.5359 0.7748
0.5651 3.0 870 0.6043 0.7847
0.2605 4.0 1160 1.0124 0.7587
0.2605 5.0 1450 1.1140 0.7599
0.1223 6.0 1740 1.2713 0.7859
0.0469 7.0 2030 1.3188 0.7822
0.0469 8.0 2320 1.3819 0.7946
0.0279 9.0 2610 1.5444 0.7847
0.0279 10.0 2900 1.5851 0.7908
0.0084 11.0 3190 1.7003 0.7822
0.0084 12.0 3480 1.8148 0.7748
0.007 13.0 3770 1.7651 0.7748
0.008 14.0 4060 1.8423 0.7748
0.008 15.0 4350 1.7871 0.7809
0.0054 16.0 4640 1.9324 0.7748
0.0054 17.0 4930 1.8685 0.7809
0.0048 18.0 5220 1.9901 0.7797
0.002 19.0 5510 1.9273 0.7785
0.002 20.0 5800 1.9945 0.7809
0.0018 21.0 6090 1.9250 0.7785
0.0018 22.0 6380 1.9929 0.7822
0.0032 23.0 6670 1.9306 0.7859
0.0032 24.0 6960 1.9603 0.7847
0.0029 25.0 7250 1.9887 0.7797

Framework versions

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