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SUBTITLE_ja-en_helsinki
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.4097
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: 0.0003
- 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: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
3.025 | 0.05 | 2000 | 5.1692 |
2.9548 | 0.09 | 4000 | 5.7128 |
2.8762 | 0.14 | 6000 | 5.9297 |
2.821 | 0.18 | 8000 | 6.0415 |
2.7826 | 0.23 | 10000 | 6.0416 |
2.7386 | 0.27 | 12000 | 6.0069 |
2.7036 | 0.32 | 14000 | 6.0192 |
2.678 | 0.37 | 16000 | 5.9286 |
2.6499 | 0.41 | 18000 | 5.9587 |
2.6261 | 0.46 | 20000 | 5.9044 |
2.6032 | 0.5 | 22000 | 5.8482 |
2.5708 | 0.55 | 24000 | 5.7760 |
2.5517 | 0.59 | 26000 | 5.7546 |
2.5336 | 0.64 | 28000 | 5.7447 |
2.5196 | 0.69 | 30000 | 5.7373 |
2.4957 | 0.73 | 32000 | 5.6429 |
2.483 | 0.78 | 34000 | 5.6874 |
2.4599 | 0.82 | 36000 | 5.6482 |
2.4468 | 0.87 | 38000 | 5.5951 |
2.4344 | 0.92 | 40000 | 5.6355 |
2.4223 | 0.96 | 42000 | 5.6135 |
2.3878 | 1.01 | 44000 | 5.6164 |
2.294 | 1.05 | 46000 | 5.5802 |
2.2896 | 1.1 | 48000 | 5.5924 |
2.2815 | 1.14 | 50000 | 5.5296 |
2.2702 | 1.19 | 52000 | 5.5119 |
2.2741 | 1.24 | 54000 | 5.4775 |
2.2586 | 1.28 | 56000 | 5.4663 |
2.2492 | 1.33 | 58000 | 5.4764 |
2.2411 | 1.37 | 60000 | 5.4444 |
2.2275 | 1.42 | 62000 | 5.4566 |
2.218 | 1.46 | 64000 | 5.4845 |
2.2086 | 1.51 | 66000 | 5.4681 |
2.1976 | 1.56 | 68000 | 5.4775 |
2.1877 | 1.6 | 70000 | 5.4619 |
2.177 | 1.65 | 72000 | 5.4621 |
2.1722 | 1.69 | 74000 | 5.4322 |
2.1599 | 1.74 | 76000 | 5.4348 |
2.1475 | 1.78 | 78000 | 5.4432 |
2.1477 | 1.83 | 80000 | 5.4239 |
2.134 | 1.88 | 82000 | 5.4182 |
2.1302 | 1.92 | 84000 | 5.4089 |
2.125 | 1.97 | 86000 | 5.4097 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1