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LN_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: 2.5382
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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.5108 | 0.02 | 2000 | 2.8405 |
| 2.2729 | 0.04 | 4000 | 2.7758 |
| 2.1673 | 0.06 | 6000 | 2.7098 |
| 2.0981 | 0.08 | 8000 | 2.6487 |
| 2.048 | 0.1 | 10000 | 2.7008 |
| 2.0077 | 0.12 | 12000 | 2.6614 |
| 1.9677 | 0.13 | 14000 | 2.6227 |
| 1.9445 | 0.15 | 16000 | 2.5895 |
| 1.9137 | 0.17 | 18000 | 2.5897 |
| 1.8911 | 0.19 | 20000 | 2.6771 |
| 1.8695 | 0.21 | 22000 | 2.6332 |
| 1.8479 | 0.23 | 24000 | 2.6130 |
| 1.8378 | 0.25 | 26000 | 2.6518 |
| 1.8191 | 0.27 | 28000 | 2.6401 |
| 1.8024 | 0.29 | 30000 | 2.6617 |
| 1.7933 | 0.31 | 32000 | 2.6705 |
| 1.7787 | 0.33 | 34000 | 2.6280 |
| 1.7661 | 0.35 | 36000 | 2.6911 |
| 1.7514 | 0.36 | 38000 | 2.6766 |
| 1.7444 | 0.38 | 40000 | 2.6996 |
| 1.7363 | 0.4 | 42000 | 2.6276 |
| 1.722 | 0.42 | 44000 | 2.6466 |
| 1.7177 | 0.44 | 46000 | 2.5937 |
| 1.7055 | 0.46 | 48000 | 2.6386 |
| 1.6956 | 0.48 | 50000 | 2.6794 |
| 1.6885 | 0.5 | 52000 | 2.7336 |
| 1.679 | 0.52 | 54000 | 2.7266 |
| 1.6715 | 0.54 | 56000 | 2.6945 |
| 1.6666 | 0.56 | 58000 | 2.7111 |
| 1.6599 | 0.58 | 60000 | 2.7205 |
| 1.6566 | 0.59 | 62000 | 2.7194 |
| 1.6481 | 0.61 | 64000 | 2.6582 |
| 1.6434 | 0.63 | 66000 | 2.6859 |
| 1.6315 | 0.65 | 68000 | 2.7058 |
| 1.6258 | 0.67 | 70000 | 2.7428 |
| 1.6189 | 0.69 | 72000 | 2.7411 |
| 1.6169 | 0.71 | 74000 | 2.7039 |
| 1.6087 | 0.73 | 76000 | 2.6844 |
| 1.6021 | 0.75 | 78000 | 2.6454 |
| 1.6034 | 0.77 | 80000 | 2.6596 |
| 1.5941 | 0.79 | 82000 | 2.6903 |
| 1.5862 | 0.81 | 84000 | 2.7099 |
| 1.5836 | 0.83 | 86000 | 2.6929 |
| 1.5827 | 0.84 | 88000 | 2.7181 |
| 1.5747 | 0.86 | 90000 | 2.6888 |
| 1.5678 | 0.88 | 92000 | 2.6662 |
| 1.5643 | 0.9 | 94000 | 2.6663 |
| 1.561 | 0.92 | 96000 | 2.6699 |
| 1.5565 | 0.94 | 98000 | 2.6667 |
| 1.5501 | 0.96 | 100000 | 2.6828 |
| 1.5476 | 0.98 | 102000 | 2.6531 |
| 1.5444 | 1.0 | 104000 | 2.6799 |
| 1.5057 | 1.02 | 106000 | 2.6525 |
| 1.5003 | 1.04 | 108000 | 2.6996 |
| 1.4996 | 1.06 | 110000 | 2.6649 |
| 1.4996 | 1.07 | 112000 | 2.6974 |
| 1.4966 | 1.09 | 114000 | 2.7594 |
| 1.4967 | 1.11 | 116000 | 2.6966 |
| 1.492 | 1.13 | 118000 | 2.6929 |
| 1.4923 | 1.15 | 120000 | 2.6522 |
| 1.4838 | 1.17 | 122000 | 2.6363 |
| 1.4839 | 1.19 | 124000 | 2.6849 |
| 1.4807 | 1.21 | 126000 | 2.6667 |
| 1.4778 | 1.23 | 128000 | 2.6684 |
| 1.4731 | 1.25 | 130000 | 2.6338 |
| 1.4727 | 1.27 | 132000 | 2.6093 |
| 1.4695 | 1.29 | 134000 | 2.6020 |
| 1.4656 | 1.3 | 136000 | 2.6341 |
| 1.4648 | 1.32 | 138000 | 2.6509 |
| 1.4578 | 1.34 | 140000 | 2.6807 |
| 1.4606 | 1.36 | 142000 | 2.6357 |
| 1.4529 | 1.38 | 144000 | 2.6404 |
| 1.4488 | 1.4 | 146000 | 2.6347 |
| 1.4442 | 1.42 | 148000 | 2.6058 |
| 1.4447 | 1.44 | 150000 | 2.6645 |
| 1.4432 | 1.46 | 152000 | 2.6070 |
| 1.437 | 1.48 | 154000 | 2.5987 |
| 1.4345 | 1.5 | 156000 | 2.6309 |
| 1.43 | 1.52 | 158000 | 2.5947 |
| 1.4301 | 1.54 | 160000 | 2.5938 |
| 1.4267 | 1.55 | 162000 | 2.6146 |
| 1.426 | 1.57 | 164000 | 2.6519 |
| 1.4193 | 1.59 | 166000 | 2.6163 |
| 1.416 | 1.61 | 168000 | 2.5793 |
| 1.4146 | 1.63 | 170000 | 2.6031 |
| 1.4091 | 1.65 | 172000 | 2.5826 |
| 1.4067 | 1.67 | 174000 | 2.5891 |
| 1.4081 | 1.69 | 176000 | 2.6006 |
| 1.4023 | 1.71 | 178000 | 2.5697 |
| 1.4003 | 1.73 | 180000 | 2.5633 |
| 1.3986 | 1.75 | 182000 | 2.5494 |
| 1.3924 | 1.77 | 184000 | 2.5577 |
| 1.3931 | 1.78 | 186000 | 2.5888 |
| 1.3851 | 1.8 | 188000 | 2.5716 |
| 1.3869 | 1.82 | 190000 | 2.5570 |
| 1.3825 | 1.84 | 192000 | 2.5702 |
| 1.3787 | 1.86 | 194000 | 2.5754 |
| 1.3738 | 1.88 | 196000 | 2.5901 |
| 1.3734 | 1.9 | 198000 | 2.5374 |
| 1.3693 | 1.92 | 200000 | 2.5897 |
| 1.3703 | 1.94 | 202000 | 2.5422 |
| 1.3685 | 1.96 | 204000 | 2.5825 |
| 1.3664 | 1.98 | 206000 | 2.5201 |
| 1.3607 | 2.0 | 208000 | 2.5733 |
| 1.3217 | 2.02 | 210000 | 2.5879 |
| 1.31 | 2.03 | 212000 | 2.5777 |
| 1.3125 | 2.05 | 214000 | 2.5724 |
| 1.3084 | 2.07 | 216000 | 2.5968 |
| 1.3087 | 2.09 | 218000 | 2.5976 |
| 1.3063 | 2.11 | 220000 | 2.5969 |
| 1.3057 | 2.13 | 222000 | 2.6353 |
| 1.3067 | 2.15 | 224000 | 2.6147 |
| 1.3013 | 2.17 | 226000 | 2.5897 |
| 1.3018 | 2.19 | 228000 | 2.5783 |
| 1.2968 | 2.21 | 230000 | 2.6172 |
| 1.2975 | 2.23 | 232000 | 2.6180 |
| 1.2946 | 2.25 | 234000 | 2.6192 |
| 1.299 | 2.26 | 236000 | 2.5895 |
| 1.2896 | 2.28 | 238000 | 2.5682 |
| 1.287 | 2.3 | 240000 | 2.5653 |
| 1.2902 | 2.32 | 242000 | 2.5501 |
| 1.2862 | 2.34 | 244000 | 2.5747 |
| 1.2841 | 2.36 | 246000 | 2.5654 |
| 1.2838 | 2.38 | 248000 | 2.5703 |
| 1.2813 | 2.4 | 250000 | 2.5919 |
| 1.2778 | 2.42 | 252000 | 2.5552 |
| 1.2821 | 2.44 | 254000 | 2.5603 |
| 1.2729 | 2.46 | 256000 | 2.5455 |
| 1.2718 | 2.48 | 258000 | 2.5688 |
| 1.2729 | 2.49 | 260000 | 2.5574 |
| 1.2699 | 2.51 | 262000 | 2.5468 |
| 1.2677 | 2.53 | 264000 | 2.5704 |
| 1.2647 | 2.55 | 266000 | 2.5665 |
| 1.2628 | 2.57 | 268000 | 2.5594 |
| 1.2636 | 2.59 | 270000 | 2.5426 |
| 1.2573 | 2.61 | 272000 | 2.5666 |
| 1.2576 | 2.63 | 274000 | 2.5580 |
| 1.2511 | 2.65 | 276000 | 2.5742 |
| 1.2513 | 2.67 | 278000 | 2.5646 |
| 1.2495 | 2.69 | 280000 | 2.5669 |
| 1.2472 | 2.71 | 282000 | 2.5700 |
| 1.2478 | 2.73 | 284000 | 2.5496 |
| 1.2471 | 2.74 | 286000 | 2.5335 |
| 1.2436 | 2.76 | 288000 | 2.5315 |
| 1.2411 | 2.78 | 290000 | 2.5302 |
| 1.2391 | 2.8 | 292000 | 2.5290 |
| 1.2352 | 2.82 | 294000 | 2.5303 |
| 1.2332 | 2.84 | 296000 | 2.5412 |
| 1.233 | 2.86 | 298000 | 2.5523 |
| 1.2298 | 2.88 | 300000 | 2.5524 |
| 1.2285 | 2.9 | 302000 | 2.5517 |
| 1.2297 | 2.92 | 304000 | 2.5419 |
| 1.2256 | 2.94 | 306000 | 2.5404 |
| 1.2239 | 2.96 | 308000 | 2.5390 |
| 1.2264 | 2.97 | 310000 | 2.5364 |
| 1.2259 | 2.99 | 312000 | 2.5382 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
