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SCRATCH_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: 1.5583
- Otaku Benchmark VN BLEU: 19.12
- Otaku Benchmark LN BLEU: 11.55
- Otaku Benchmark MANGA BLEU: 12.98
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: 96
- 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 |
|---|---|---|---|
| 3.0252 | 0.02 | 2000 | 2.4140 |
| 2.8406 | 0.03 | 4000 | 2.2819 |
| 2.7505 | 0.05 | 6000 | 2.3018 |
| 2.6948 | 0.06 | 8000 | 2.1931 |
| 2.6408 | 0.08 | 10000 | 2.1724 |
| 2.6004 | 0.09 | 12000 | 2.1583 |
| 2.5685 | 0.11 | 14000 | 2.1203 |
| 2.5432 | 0.12 | 16000 | 2.1593 |
| 2.5153 | 0.14 | 18000 | 2.1009 |
| 2.4906 | 0.15 | 20000 | 2.0899 |
| 2.4709 | 0.17 | 22000 | 2.0512 |
| 2.4471 | 0.18 | 24000 | 2.0208 |
| 2.4295 | 0.2 | 26000 | 2.0773 |
| 2.4154 | 0.21 | 28000 | 2.0441 |
| 2.4008 | 0.23 | 30000 | 2.0235 |
| 2.3834 | 0.24 | 32000 | 2.0190 |
| 2.3709 | 0.26 | 34000 | 1.9831 |
| 2.3537 | 0.27 | 36000 | 1.9870 |
| 2.3486 | 0.29 | 38000 | 1.9692 |
| 2.3346 | 0.3 | 40000 | 1.9517 |
| 2.3195 | 0.32 | 42000 | 1.9800 |
| 2.3104 | 0.33 | 44000 | 1.9676 |
| 2.298 | 0.35 | 46000 | 1.9563 |
| 2.2905 | 0.36 | 48000 | 1.9217 |
| 2.2792 | 0.38 | 50000 | 1.9195 |
| 2.2714 | 0.39 | 52000 | 1.9109 |
| 2.2593 | 0.41 | 54000 | 1.9044 |
| 2.2582 | 0.42 | 56000 | 1.8876 |
| 2.2482 | 0.44 | 58000 | 1.8860 |
| 2.2394 | 0.45 | 60000 | 1.8887 |
| 2.2273 | 0.47 | 62000 | 1.8862 |
| 2.2255 | 0.48 | 64000 | 1.8705 |
| 2.2166 | 0.5 | 66000 | 1.8696 |
| 2.2075 | 0.51 | 68000 | 1.8657 |
| 2.1992 | 0.53 | 70000 | 1.8585 |
| 2.1969 | 0.54 | 72000 | 1.8526 |
| 2.1894 | 0.56 | 74000 | 1.8493 |
| 2.1817 | 0.57 | 76000 | 1.8480 |
| 2.1771 | 0.59 | 78000 | 1.8333 |
| 2.1683 | 0.6 | 80000 | 1.8342 |
| 2.1667 | 0.62 | 82000 | 1.8537 |
| 2.1546 | 0.63 | 84000 | 1.8261 |
| 2.1467 | 0.65 | 86000 | 1.8092 |
| 2.1421 | 0.66 | 88000 | 1.8137 |
| 2.1395 | 0.68 | 90000 | 1.8286 |
| 2.1313 | 0.69 | 92000 | 1.8042 |
| 2.1241 | 0.71 | 94000 | 1.7934 |
| 2.1214 | 0.72 | 96000 | 1.7940 |
| 2.12 | 0.74 | 98000 | 1.8064 |
| 2.1096 | 0.75 | 100000 | 1.7983 |
| 2.1035 | 0.77 | 102000 | 1.8089 |
| 2.0937 | 0.78 | 104000 | 1.7941 |
| 2.0893 | 0.8 | 106000 | 1.7791 |
| 2.0869 | 0.81 | 108000 | 1.7807 |
| 2.0845 | 0.83 | 110000 | 1.7852 |
| 2.0782 | 0.84 | 112000 | 1.7675 |
| 2.0755 | 0.86 | 114000 | 1.7756 |
| 2.0657 | 0.87 | 116000 | 1.7604 |
| 2.0614 | 0.89 | 118000 | 1.7447 |
| 2.0591 | 0.9 | 120000 | 1.7489 |
| 2.0586 | 0.92 | 122000 | 1.7550 |
| 2.0498 | 0.93 | 124000 | 1.7543 |
| 2.0455 | 0.95 | 126000 | 1.7510 |
| 2.04 | 0.96 | 128000 | 1.7439 |
| 2.0385 | 0.98 | 130000 | 1.7407 |
| 2.0267 | 0.99 | 132000 | 1.7467 |
| 2.0088 | 1.01 | 134000 | 1.7455 |
| 1.9826 | 1.02 | 136000 | 1.7210 |
| 1.9785 | 1.04 | 138000 | 1.7524 |
| 1.9777 | 1.05 | 140000 | 1.7272 |
| 1.9763 | 1.07 | 142000 | 1.7283 |
| 1.9736 | 1.08 | 144000 | 1.7210 |
| 1.9704 | 1.1 | 146000 | 1.7001 |
| 1.9625 | 1.11 | 148000 | 1.7112 |
| 1.9665 | 1.13 | 150000 | 1.7236 |
| 1.9592 | 1.14 | 152000 | 1.7169 |
| 1.9606 | 1.16 | 154000 | 1.6962 |
| 1.9571 | 1.17 | 156000 | 1.7064 |
| 1.9532 | 1.19 | 158000 | 1.6898 |
| 1.9465 | 1.2 | 160000 | 1.7004 |
| 1.9438 | 1.22 | 162000 | 1.7092 |
| 1.9435 | 1.23 | 164000 | 1.6927 |
| 1.9361 | 1.25 | 166000 | 1.6838 |
| 1.9369 | 1.26 | 168000 | 1.6784 |
| 1.9287 | 1.28 | 170000 | 1.6709 |
| 1.928 | 1.29 | 172000 | 1.6735 |
| 1.9227 | 1.31 | 174000 | 1.6689 |
| 1.9213 | 1.32 | 176000 | 1.6685 |
| 1.9152 | 1.34 | 178000 | 1.6635 |
| 1.9092 | 1.35 | 180000 | 1.6561 |
| 1.9059 | 1.37 | 182000 | 1.6673 |
| 1.9094 | 1.38 | 184000 | 1.6717 |
| 1.9006 | 1.4 | 186000 | 1.6593 |
| 1.8956 | 1.41 | 188000 | 1.6483 |
| 1.8972 | 1.43 | 190000 | 1.6635 |
| 1.8907 | 1.44 | 192000 | 1.6604 |
| 1.8885 | 1.46 | 194000 | 1.6465 |
| 1.8844 | 1.47 | 196000 | 1.6444 |
| 1.8799 | 1.49 | 198000 | 1.6307 |
| 1.8813 | 1.5 | 200000 | 1.6240 |
| 1.8693 | 1.52 | 202000 | 1.6102 |
| 1.8768 | 1.53 | 204000 | 1.6197 |
| 1.8678 | 1.55 | 206000 | 1.6275 |
| 1.8588 | 1.56 | 208000 | 1.6183 |
| 1.8585 | 1.58 | 210000 | 1.6197 |
| 1.8564 | 1.59 | 212000 | 1.6004 |
| 1.8493 | 1.61 | 214000 | 1.6078 |
| 1.85 | 1.62 | 216000 | 1.6001 |
| 1.8428 | 1.64 | 218000 | 1.6106 |
| 1.8428 | 1.65 | 220000 | 1.5866 |
| 1.8423 | 1.67 | 222000 | 1.5993 |
| 1.8352 | 1.68 | 224000 | 1.6052 |
| 1.8385 | 1.7 | 226000 | 1.5959 |
| 1.8307 | 1.71 | 228000 | 1.6024 |
| 1.8248 | 1.73 | 230000 | 1.5969 |
| 1.82 | 1.74 | 232000 | 1.5878 |
| 1.8254 | 1.76 | 234000 | 1.5934 |
| 1.8188 | 1.77 | 236000 | 1.5827 |
| 1.813 | 1.79 | 238000 | 1.5797 |
| 1.8128 | 1.8 | 240000 | 1.5758 |
| 1.8044 | 1.82 | 242000 | 1.5752 |
| 1.808 | 1.83 | 244000 | 1.5818 |
| 1.8025 | 1.85 | 246000 | 1.5772 |
| 1.7992 | 1.86 | 248000 | 1.5738 |
| 1.8021 | 1.88 | 250000 | 1.5752 |
| 1.7988 | 1.89 | 252000 | 1.5717 |
| 1.7967 | 1.91 | 254000 | 1.5690 |
| 1.7909 | 1.92 | 256000 | 1.5607 |
| 1.7942 | 1.94 | 258000 | 1.5618 |
| 1.7897 | 1.95 | 260000 | 1.5585 |
| 1.7871 | 1.97 | 262000 | 1.5576 |
| 1.7843 | 1.98 | 264000 | 1.5577 |
| 1.7888 | 2.0 | 266000 | 1.5583 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
