NMTBaliIndoBART / README.md
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metadata
license: apache-2.0
base_model: facebook/bart-base
tags:
  - generated_from_keras_callback
model-index:
  - name: pijarcandra22/NMTBaliIndoBART
    results: []

pijarcandra22/NMTBaliIndoBART

This model is a fine-tuned version of facebook/bart-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 5.5001
  • Validation Loss: 5.8867
  • Epoch: 81

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:

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.02, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
  • training_precision: float32

Training results

Train Loss Validation Loss Epoch
9.3368 5.6757 0
5.5627 5.5987 1
5.5311 5.5419 2
5.5152 5.5201 3
5.5005 5.6477 4
5.4704 5.5914 5
5.4610 6.0922 6
5.4584 5.7137 7
5.4528 5.8658 8
5.4820 5.5628 9
5.4874 5.5309 10
5.4917 5.7595 11
5.4898 5.7333 12
5.4833 5.6789 13
5.4767 5.9588 14
5.4883 5.9895 15
5.4694 6.0100 16
5.4663 6.0316 17
5.4602 5.9233 18
5.4576 6.0051 19
5.4559 5.9966 20
5.4651 6.0025 21
5.4660 6.0160 22
5.4626 5.8324 23
5.4647 5.8383 24
5.4695 6.0272 25
5.4614 6.0724 26
5.4623 5.9454 27
5.4678 6.0196 28
5.4860 5.5949 29
5.4851 5.8838 30
5.4666 5.8506 31
5.4715 6.0391 32
5.4630 6.0870 33
5.4646 6.2195 34
5.4574 5.9696 35
5.4564 5.8970 36
5.4570 5.9522 37
5.4559 6.1518 38
5.4584 6.1860 39
5.4732 6.1168 40
5.4625 6.1588 41
5.4601 5.9868 42
5.4645 5.9606 43
5.4664 6.1495 44
5.4698 6.0152 45
5.4666 6.2713 46
5.4557 6.2708 47
5.4557 6.0003 48
5.4693 5.9321 49
5.4928 5.8971 50
5.5032 6.0766 51
5.4749 5.8919 52
5.4689 5.9853 53
5.4665 5.9329 54
5.4574 5.9770 55
5.4686 6.1022 56
5.4727 5.8973 57
5.4692 5.9633 58
5.4608 6.0480 59
5.4613 5.9596 60
5.4607 6.1158 61
5.4531 6.0617 62
5.4610 6.0375 63
5.4631 6.1184 64
5.4627 6.0465 65
5.4685 6.0011 66
5.4642 6.0828 67
5.4577 6.0883 68
5.4615 5.9523 69
5.4673 5.7216 70
5.4724 6.0274 71
5.4601 6.0344 72
5.4640 5.9661 73
5.4590 6.0013 74
5.4622 6.0172 75
5.4666 5.8407 76
5.4669 6.0261 77
5.4859 5.9295 78
5.5042 6.1254 79
5.4845 5.8930 80
5.5001 5.8867 81

Framework versions

  • Transformers 4.40.2
  • TensorFlow 2.15.0
  • Datasets 2.19.1
  • Tokenizers 0.19.1