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gbert-large-finetuned-cust

This model is a fine-tuned version of deepset/gbert-large on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1846

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: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
0.8251 1.0 157 0.5204
0.508 2.0 314 0.3953
0.4009 3.0 471 0.3242
0.3587 4.0 628 0.3300
0.3276 5.0 785 0.3137
0.302 6.0 942 0.2826
0.2777 7.0 1099 0.2768
0.2609 8.0 1256 0.2726
0.244 9.0 1413 0.2660
0.2274 10.0 1570 0.2391
0.2132 11.0 1727 0.2353
0.2014 12.0 1884 0.2134
0.1835 13.0 2041 0.2278
0.1896 14.0 2198 0.2110
0.1974 15.0 2355 0.2132
0.1775 16.0 2512 0.1973
0.1715 17.0 2669 0.1941
0.1777 18.0 2826 0.2105
0.1741 19.0 2983 0.2127
0.1607 20.0 3140 0.1762
0.1562 21.0 3297 0.2095
0.1548 22.0 3454 0.1805
0.1534 23.0 3611 0.1852
0.1484 24.0 3768 0.1773
0.1473 25.0 3925 0.1759
0.1354 26.0 4082 0.1734
0.136 27.0 4239 0.1902
0.1306 28.0 4396 0.1769
0.1353 29.0 4553 0.1705
0.1368 30.0 4710 0.1846

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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