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indobart-indonlg-nusax-500-jv-id

This model is a fine-tuned version of indobenchmark/indobart-v2 on IndoNLG, NusaX, and manual translation dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6148
  • Bleu: 20.3132
  • Gen Len: 19.3294

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Javanese Most Common Words image/png

Indonesian Most Common Words image/png

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 64
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Bleu Gen Len
No log 1.0 114 0.8182 12.2063 19.4575
No log 2.0 228 0.7390 14.2482 19.3932
No log 3.0 342 0.6975 15.9974 19.4014
No log 4.0 456 0.6706 16.578 19.3601
0.849 5.0 570 0.6527 17.2313 19.3713
0.849 6.0 684 0.6392 17.9 19.3477
0.849 7.0 798 0.6285 18.0166 19.3495
0.849 8.0 912 0.6242 18.2424 19.3347
0.4461 9.0 1026 0.6170 18.6378 19.3648
0.4461 10.0 1140 0.6148 19.0513 19.3365
0.4461 11.0 1254 0.6106 19.5335 19.3383
0.4461 12.0 1368 0.6097 19.2886 19.3353
0.4461 13.0 1482 0.6079 19.5712 19.3347
0.3344 14.0 1596 0.6067 19.4256 19.3684
0.3344 15.0 1710 0.6078 19.7062 19.3483
0.3344 16.0 1824 0.6052 19.6353 19.3506
0.3344 17.0 1938 0.6072 19.8745 19.3318
0.2674 18.0 2052 0.6076 20.0834 19.3318
0.2674 19.0 2166 0.6078 20.082 19.3506
0.2674 20.0 2280 0.6098 20.1934 19.3117
0.2674 21.0 2394 0.6094 20.1326 19.3453
0.225 22.0 2508 0.6109 20.2045 19.3329
0.225 23.0 2622 0.6133 20.0595 19.3495
0.225 24.0 2736 0.6117 20.2089 19.3442
0.225 25.0 2850 0.6141 20.2566 19.3264
0.225 26.0 2964 0.6143 20.3092 19.3329
0.199 27.0 3078 0.6134 20.3808 19.33
0.199 28.0 3192 0.6141 20.412 19.34
0.199 29.0 3306 0.6148 20.2613 19.3359
0.199 30.0 3420 0.6148 20.3132 19.3294

Framework versions

  • Transformers 4.33.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.13.3
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