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opus-mt-en-lg-finetuned-en-to-lg-finetuned-en-to-lm

This model is a fine-tuned version of Tobius/opus-mt-en-lg-finetuned-en-to-lg on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7639
  • Bleu: 30.9462
  • Gen Len: 21.6305

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: 0.0001
  • 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 Bleu Gen Len
No log 1.0 146 3.6537 0.5489 30.8399
No log 2.0 292 3.0866 0.5298 22.7759
No log 3.0 438 2.7528 0.8364 26.5419
3.8771 4.0 584 2.4983 1.6885 25.3966
3.8771 5.0 730 2.2893 2.265 23.0296
3.8771 6.0 876 2.1164 3.5594 21.9409
2.5379 7.0 1022 1.9606 3.8191 22.8325
2.5379 8.0 1168 1.8145 4.7176 22.5025
2.5379 9.0 1314 1.6974 5.5036 21.7931
2.5379 10.0 1460 1.5895 7.0622 21.9483
2.0088 11.0 1606 1.4934 7.9064 22.2143
2.0088 12.0 1752 1.4120 8.7831 20.9754
2.0088 13.0 1898 1.3244 10.7815 21.899
1.6503 14.0 2044 1.2544 11.9959 21.835
1.6503 15.0 2190 1.1843 12.9721 21.766
1.6503 16.0 2336 1.1271 15.3546 21.5542
1.6503 17.0 2482 1.0670 15.9538 21.7906
1.3905 18.0 2628 1.0184 19.1229 21.9212
1.3905 19.0 2774 0.9797 20.3025 21.0148
1.3905 20.0 2920 0.9358 22.3064 21.6946
1.1903 21.0 3066 0.9012 24.9192 21.3128
1.1903 22.0 3212 0.8704 25.8138 21.3202
1.1903 23.0 3358 0.8481 26.8542 21.5591
1.0573 24.0 3504 0.8228 28.4838 21.3251
1.0573 25.0 3650 0.8069 29.1448 21.399
1.0573 26.0 3796 0.7892 29.8567 21.5985
1.0573 27.0 3942 0.7777 29.8862 21.6404
0.9649 28.0 4088 0.7703 30.9483 21.7315
0.9649 29.0 4234 0.7660 30.9831 21.6108
0.9649 30.0 4380 0.7639 30.9462 21.6305

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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