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KoBART_jeju_dialect

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KoBART_jeju_dialect / README.md
Kyungmin Jeon
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metadata
license: mit
base_model: gogamza/kobart-base-v2
tags:
  - generated_from_trainer
model-index:
  - name: KoBART_base_v2-trial2
    results: []

KoBART_base_v2-trial2

This model is a fine-tuned version of gogamza/kobart-base-v2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1820

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: 0.0005
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 20
  • num_epochs: 5
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
2.3889 0.11 50 0.5425
0.5339 0.22 100 0.4328
0.4609 0.32 150 0.4180
0.4631 0.43 200 0.4167
0.4065 0.54 250 0.3775
0.3898 0.65 300 0.3539
0.3637 0.76 350 0.3389
0.3347 0.87 400 0.3275
0.3428 0.97 450 0.3087
0.2871 1.08 500 0.3189
0.2843 1.19 550 0.3016
0.2685 1.3 600 0.2954
0.2603 1.41 650 0.2860
0.2636 1.52 700 0.2804
0.2586 1.62 750 0.2821
0.2485 1.73 800 0.2674
0.2483 1.84 850 0.2662
0.2322 1.95 900 0.2525
0.2052 2.06 950 0.2634
0.1838 2.16 1000 0.2472
0.1859 2.27 1050 0.2432
0.1887 2.38 1100 0.2392
0.1756 2.49 1150 0.2314
0.1697 2.6 1200 0.2332
0.1741 2.71 1250 0.2257
0.1665 2.81 1300 0.2204
0.1655 2.92 1350 0.2097
0.1539 3.03 1400 0.2141
0.126 3.14 1450 0.2129
0.1241 3.25 1500 0.2068
0.1266 3.35 1550 0.1999
0.1161 3.46 1600 0.1996
0.1183 3.57 1650 0.1943
0.1123 3.68 1700 0.1914
0.1096 3.79 1750 0.1881
0.1089 3.9 1800 0.1835
0.1096 4.0 1850 0.1803
0.0857 4.11 1900 0.1873
0.0833 4.22 1950 0.1857
0.0791 4.33 2000 0.1871
0.0825 4.44 2050 0.1852
0.0813 4.55 2100 0.1834
0.0806 4.65 2150 0.1830
0.0805 4.76 2200 0.1822
0.0786 4.87 2250 0.1820
0.0775 4.98 2300 0.1820

Framework versions

  • Transformers 4.36.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.15.0
  • Tokenizers 0.15.0