cs_mT5-large_0.01_100_v0.1
This model is a fine-tuned version of google/mt5-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.2112
- Bleu: 0.8171
- Gen Len: 19.0
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: 0.01
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 9.5426 | 1.0 | 6 | 13.2737 | 0.0 | 19.0 |
| 9.5598 | 2.0 | 12 | 57.9184 | 0.2088 | 19.0 |
| 8.9859 | 3.0 | 18 | 7.1357 | 0.2088 | 19.0 |
| 4.8419 | 4.0 | 24 | 6.5896 | 0.0 | 2.0 |
| 6.0099 | 5.0 | 30 | 5.9797 | 0.0 | 19.0 |
| 5.4071 | 6.0 | 36 | 6.0228 | 0.0 | 19.0 |
| 5.4716 | 7.0 | 42 | 6.0132 | 0.0 | 19.0 |
| 5.6419 | 8.0 | 48 | 5.9242 | 0.0 | 19.0 |
| 5.7044 | 9.0 | 54 | 6.0529 | 0.0 | 19.0 |
| 5.7007 | 10.0 | 60 | 5.8730 | 0.0 | 19.0 |
| 5.0052 | 11.0 | 66 | 6.0392 | 0.0 | 19.0 |
| 6.3889 | 12.0 | 72 | 6.0776 | 0.0 | 19.0 |
| 5.2703 | 13.0 | 78 | 70.6639 | 0.0 | 19.0 |
| 7.1444 | 14.0 | 84 | 7.6067 | 0.0 | 19.0 |
| 4.7785 | 15.0 | 90 | 6.5610 | 0.0 | 19.0 |
| 5.6738 | 16.0 | 96 | 6.0522 | 0.0 | 19.0 |
| 5.5087 | 17.0 | 102 | 6.0558 | 0.0 | 19.0 |
| 5.4367 | 18.0 | 108 | 5.9737 | 0.0 | 19.0 |
| 5.5081 | 19.0 | 114 | 6.0431 | 0.0 | 19.0 |
| 5.2506 | 20.0 | 120 | 5.9623 | 0.0 | 19.0 |
| 5.354 | 21.0 | 126 | 6.0081 | 0.0 | 19.0 |
| 5.5891 | 22.0 | 132 | 5.9859 | 0.0 | 19.0 |
| 5.2457 | 23.0 | 138 | 5.9296 | 0.0 | 19.0 |
| 4.9566 | 24.0 | 144 | 6.0038 | 0.0 | 19.0 |
| 5.3327 | 25.0 | 150 | 6.0421 | 0.0 | 19.0 |
| 4.946 | 26.0 | 156 | 6.0225 | 0.0 | 19.0 |
| 5.1903 | 27.0 | 162 | 5.9587 | 0.0 | 19.0 |
| 5.0797 | 28.0 | 168 | 5.9780 | 0.0 | 19.0 |
| 4.8033 | 29.0 | 174 | 6.0577 | 0.0 | 19.0 |
| 5.559 | 30.0 | 180 | 6.0250 | 0.0 | 19.0 |
| 5.7859 | 31.0 | 186 | 5.9493 | 0.0 | 19.0 |
| 5.4172 | 32.0 | 192 | 6.0647 | 0.0 | 19.0 |
| 4.9906 | 33.0 | 198 | 6.0617 | 0.0 | 19.0 |
| 4.9745 | 34.0 | 204 | 5.9800 | 0.0 | 19.0 |
| 5.2086 | 35.0 | 210 | 5.9942 | 0.0 | 19.0 |
| 5.7047 | 36.0 | 216 | 5.9996 | 0.0 | 19.0 |
| 4.4275 | 37.0 | 222 | 6.0826 | 0.0 | 19.0 |
| 4.9545 | 38.0 | 228 | 6.0865 | 0.0 | 19.0 |
| 5.1466 | 39.0 | 234 | 5.9571 | 0.0 | 19.0 |
| 5.5095 | 40.0 | 240 | 5.9970 | 0.0 | 19.0 |
| 5.1998 | 41.0 | 246 | 5.9978 | 0.0 | 19.0 |
| 4.8406 | 42.0 | 252 | 6.0314 | 0.0 | 19.0 |
| 5.0467 | 43.0 | 258 | 6.0444 | 0.0 | 19.0 |
| 5.2282 | 44.0 | 264 | 6.0295 | 0.0 | 19.0 |
| 4.8847 | 45.0 | 270 | 6.0284 | 0.0 | 19.0 |
| 5.5734 | 46.0 | 276 | 6.0598 | 0.0 | 19.0 |
| 4.743 | 47.0 | 282 | 6.0396 | 0.0 | 19.0 |
| 5.3795 | 48.0 | 288 | 6.0567 | 0.0 | 19.0 |
| 4.9066 | 49.0 | 294 | 6.0615 | 0.0 | 19.0 |
| 4.9682 | 50.0 | 300 | 6.1018 | 0.0 | 19.0 |
| 4.828 | 51.0 | 306 | 6.0605 | 0.0 | 19.0 |
| 4.5153 | 52.0 | 312 | 6.0531 | 0.0 | 19.0 |
| 5.2316 | 53.0 | 318 | 5.9855 | 0.0 | 19.0 |
| 4.8071 | 54.0 | 324 | 6.0292 | 0.0 | 19.0 |
| 5.106 | 55.0 | 330 | 6.0541 | 0.0 | 19.0 |
| 4.9581 | 56.0 | 336 | 5.9499 | 0.0 | 19.0 |
| 4.8037 | 57.0 | 342 | 6.1083 | 0.0 | 19.0 |
| 4.7738 | 58.0 | 348 | 6.0111 | 0.0 | 19.0 |
| 5.3786 | 59.0 | 354 | 6.0164 | 0.0 | 19.0 |
| 4.8782 | 60.0 | 360 | 5.9442 | 0.0 | 19.0 |
| 4.8589 | 61.0 | 366 | 5.9036 | 0.8171 | 19.0 |
| 4.8486 | 62.0 | 372 | 5.7896 | 0.8171 | 19.0 |
| 4.4303 | 63.0 | 378 | 5.8475 | 0.8171 | 19.0 |
| 5.116 | 64.0 | 384 | 5.7361 | 0.8171 | 19.0 |
| 4.9206 | 65.0 | 390 | 5.7211 | 0.8171 | 19.0 |
| 4.5294 | 66.0 | 396 | 5.6845 | 0.8171 | 19.0 |
| 5.0969 | 67.0 | 402 | 5.6964 | 0.8171 | 19.0 |
| 4.4403 | 68.0 | 408 | 5.7035 | 0.8171 | 19.0 |
| 4.3498 | 69.0 | 414 | 5.7088 | 0.8171 | 19.0 |
| 5.0456 | 70.0 | 420 | 5.6742 | 0.8171 | 19.0 |
| 4.9812 | 71.0 | 426 | 5.6820 | 0.8171 | 19.0 |
| 4.4053 | 72.0 | 432 | 5.7010 | 0.8171 | 19.0 |
| 4.8459 | 73.0 | 438 | 5.8511 | 0.8171 | 19.0 |
| 4.3272 | 74.0 | 444 | 5.7204 | 0.8171 | 19.0 |
| 4.4791 | 75.0 | 450 | 5.7542 | 0.8171 | 19.0 |
| 4.5272 | 76.0 | 456 | 5.7444 | 0.8171 | 19.0 |
| 4.2581 | 77.0 | 462 | 5.7456 | 0.879 | 19.0 |
| 4.718 | 78.0 | 468 | 5.7187 | 0.8171 | 19.0 |
| 4.3661 | 79.0 | 474 | 5.8472 | 0.8291 | 19.0 |
| 4.8016 | 80.0 | 480 | 5.7478 | 0.8171 | 19.0 |
| 4.1973 | 81.0 | 486 | 5.8850 | 0.8171 | 19.0 |
| 4.0916 | 82.0 | 492 | 5.7678 | 0.8171 | 19.0 |
| 4.1624 | 83.0 | 498 | 5.8662 | 0.8171 | 19.0 |
| 4.2458 | 84.0 | 504 | 5.9224 | 0.8171 | 19.0 |
| 3.7141 | 85.0 | 510 | 5.8928 | 0.8171 | 19.0 |
| 3.5796 | 86.0 | 516 | 6.0489 | 0.937 | 19.0 |
| 4.8417 | 87.0 | 522 | 6.1602 | 0.8171 | 19.0 |
| 4.3568 | 88.0 | 528 | 5.9343 | 0.8171 | 19.0 |
| 4.6028 | 89.0 | 534 | 5.9039 | 0.8171 | 19.0 |
| 3.6638 | 90.0 | 540 | 6.1188 | 0.879 | 19.0 |
| 4.1465 | 91.0 | 546 | 6.0166 | 0.8171 | 19.0 |
| 4.32 | 92.0 | 552 | 6.0690 | 0.8171 | 19.0 |
| 4.0945 | 93.0 | 558 | 6.0812 | 0.8171 | 19.0 |
| 3.9572 | 94.0 | 564 | 5.9877 | 0.8171 | 19.0 |
| 3.9032 | 95.0 | 570 | 6.0960 | 0.2223 | 19.0 |
| 4.3571 | 96.0 | 576 | 6.1585 | 0.8171 | 19.0 |
| 3.768 | 97.0 | 582 | 6.1953 | 0.8171 | 19.0 |
| 3.94 | 98.0 | 588 | 6.2025 | 0.8171 | 19.0 |
| 3.8452 | 99.0 | 594 | 6.2129 | 0.8171 | 19.0 |
| 4.4174 | 100.0 | 600 | 6.2112 | 0.8171 | 19.0 |
Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
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