degree-bert-finetuning-2

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

  • Loss: 0.5890
  • Accuracy: 0.698
  • F1: 0.6983

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.9822 1.0 104 0.7872 0.638 0.6374
0.8237 2.0 208 0.7235 0.656 0.6552
0.787 3.0 312 0.7133 0.658 0.6521
0.7552 4.0 416 0.6904 0.68 0.6780
0.7366 5.0 520 0.6555 0.704 0.7045
0.7158 6.0 624 0.6500 0.7 0.6994
0.7025 7.0 728 0.6429 0.71 0.7101
0.6912 8.0 832 0.6097 0.698 0.6972
0.6764 9.0 936 0.6033 0.7 0.6996
0.667 10.0 1040 0.6040 0.69 0.6886
0.6571 11.0 1144 0.6022 0.702 0.7011
0.6532 12.0 1248 0.5941 0.71 0.7107
0.6369 13.0 1352 0.5956 0.706 0.7066
0.6385 14.0 1456 0.5929 0.688 0.6864
0.6328 15.0 1560 0.5880 0.708 0.7085
0.6199 16.0 1664 0.5956 0.69 0.6885
0.6109 17.0 1768 0.5865 0.712 0.7125
0.6116 18.0 1872 0.5863 0.716 0.7169
0.6092 19.0 1976 0.5908 0.704 0.7037
0.594 20.0 2080 0.5890 0.698 0.6983

Framework versions

  • Transformers 4.38.1
  • Pytorch 2.2.0
  • Datasets 2.17.1
  • Tokenizers 0.15.2
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