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translation_eng_to_uk_94
This model is a fine-tuned version of EnglishVoice/t5-base-uk-to-us-english on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0221
- Bleu: 52.8724
- Gen Len: 6.9574
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: 2e-05
- 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: 94
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| No log | 1.0 | 141 | 0.8362 | 48.5749 | 7.4203 |
| No log | 2.0 | 282 | 0.6430 | 48.719 | 7.394 |
| No log | 3.0 | 423 | 0.5513 | 48.7133 | 7.3239 |
| 0.6744 | 4.0 | 564 | 0.4874 | 48.6957 | 7.2834 |
| 0.6744 | 5.0 | 705 | 0.4369 | 48.8005 | 7.2155 |
| 0.6744 | 6.0 | 846 | 0.3949 | 48.8607 | 7.1701 |
| 0.6744 | 7.0 | 987 | 0.3604 | 49.0349 | 7.1661 |
| 0.468 | 8.0 | 1128 | 0.3309 | 49.1391 | 7.1342 |
| 0.468 | 9.0 | 1269 | 0.3047 | 49.4194 | 7.1217 |
| 0.468 | 10.0 | 1410 | 0.2814 | 49.6172 | 7.1 |
| 0.3814 | 11.0 | 1551 | 0.2598 | 49.7945 | 7.0733 |
| 0.3814 | 12.0 | 1692 | 0.2410 | 49.8814 | 7.0538 |
| 0.3814 | 13.0 | 1833 | 0.2235 | 49.9916 | 7.0498 |
| 0.3814 | 14.0 | 1974 | 0.2081 | 50.0904 | 7.044 |
| 0.3283 | 15.0 | 2115 | 0.1942 | 50.231 | 7.0298 |
| 0.3283 | 16.0 | 2256 | 0.1810 | 50.4019 | 7.0204 |
| 0.3283 | 17.0 | 2397 | 0.1691 | 50.5744 | 7.0022 |
| 0.2858 | 18.0 | 2538 | 0.1578 | 50.6606 | 6.9996 |
| 0.2858 | 19.0 | 2679 | 0.1475 | 50.7137 | 6.9893 |
| 0.2858 | 20.0 | 2820 | 0.1383 | 50.7873 | 6.9827 |
| 0.2858 | 21.0 | 2961 | 0.1301 | 50.909 | 6.9822 |
| 0.2482 | 22.0 | 3102 | 0.1225 | 51.0025 | 6.9769 |
| 0.2482 | 23.0 | 3243 | 0.1151 | 51.079 | 6.9738 |
| 0.2482 | 24.0 | 3384 | 0.1085 | 51.1191 | 6.9809 |
| 0.2259 | 25.0 | 3525 | 0.1027 | 51.283 | 6.9778 |
| 0.2259 | 26.0 | 3666 | 0.0970 | 51.3516 | 6.98 |
| 0.2259 | 27.0 | 3807 | 0.0918 | 51.4316 | 6.9733 |
| 0.2259 | 28.0 | 3948 | 0.0870 | 51.5047 | 6.9716 |
| 0.2031 | 29.0 | 4089 | 0.0826 | 51.5492 | 6.9711 |
| 0.2031 | 30.0 | 4230 | 0.0790 | 51.6609 | 6.9685 |
| 0.2031 | 31.0 | 4371 | 0.0751 | 51.8304 | 6.9685 |
| 0.1996 | 32.0 | 4512 | 0.0715 | 51.8569 | 6.9698 |
| 0.1996 | 33.0 | 4653 | 0.0683 | 51.871 | 6.9693 |
| 0.1996 | 34.0 | 4794 | 0.0653 | 51.9288 | 6.9667 |
| 0.1996 | 35.0 | 4935 | 0.0624 | 51.9742 | 6.9662 |
| 0.1688 | 36.0 | 5076 | 0.0598 | 52.029 | 6.9653 |
| 0.1688 | 37.0 | 5217 | 0.0574 | 52.0484 | 6.9627 |
| 0.1688 | 38.0 | 5358 | 0.0552 | 52.0544 | 6.9609 |
| 0.1688 | 39.0 | 5499 | 0.0531 | 52.1035 | 6.9614 |
| 0.1614 | 40.0 | 5640 | 0.0512 | 52.211 | 6.9609 |
| 0.1614 | 41.0 | 5781 | 0.0495 | 52.2609 | 6.96 |
| 0.1614 | 42.0 | 5922 | 0.0477 | 52.3052 | 6.9609 |
| 0.1475 | 43.0 | 6063 | 0.0462 | 52.3149 | 6.96 |
| 0.1475 | 44.0 | 6204 | 0.0447 | 52.3175 | 6.9609 |
| 0.1475 | 45.0 | 6345 | 0.0435 | 52.3549 | 6.9618 |
| 0.1475 | 46.0 | 6486 | 0.0424 | 52.3493 | 6.9622 |
| 0.1446 | 47.0 | 6627 | 0.0410 | 52.3947 | 6.9631 |
| 0.1446 | 48.0 | 6768 | 0.0400 | 52.3947 | 6.9609 |
| 0.1446 | 49.0 | 6909 | 0.0388 | 52.3962 | 6.9609 |
| 0.1287 | 50.0 | 7050 | 0.0379 | 52.4539 | 6.9614 |
| 0.1287 | 51.0 | 7191 | 0.0369 | 52.4554 | 6.9618 |
| 0.1287 | 52.0 | 7332 | 0.0360 | 52.4554 | 6.9618 |
| 0.1287 | 53.0 | 7473 | 0.0352 | 52.4774 | 6.9618 |
| 0.1279 | 54.0 | 7614 | 0.0343 | 52.4901 | 6.9591 |
| 0.1279 | 55.0 | 7755 | 0.0336 | 52.4901 | 6.9591 |
| 0.1279 | 56.0 | 7896 | 0.0328 | 52.526 | 6.9591 |
| 0.1245 | 57.0 | 8037 | 0.0322 | 52.526 | 6.9591 |
| 0.1245 | 58.0 | 8178 | 0.0316 | 52.5469 | 6.9587 |
| 0.1245 | 59.0 | 8319 | 0.0310 | 52.5469 | 6.9587 |
| 0.1245 | 60.0 | 8460 | 0.0305 | 52.5837 | 6.9587 |
| 0.1172 | 61.0 | 8601 | 0.0299 | 52.5919 | 6.9578 |
| 0.1172 | 62.0 | 8742 | 0.0293 | 52.5919 | 6.9578 |
| 0.1172 | 63.0 | 8883 | 0.0288 | 52.5919 | 6.9574 |
| 0.1122 | 64.0 | 9024 | 0.0283 | 52.6317 | 6.9569 |
| 0.1122 | 65.0 | 9165 | 0.0278 | 52.6685 | 6.9569 |
| 0.1122 | 66.0 | 9306 | 0.0274 | 52.6685 | 6.9569 |
| 0.1122 | 67.0 | 9447 | 0.0271 | 52.6809 | 6.9569 |
| 0.1099 | 68.0 | 9588 | 0.0267 | 52.6809 | 6.9569 |
| 0.1099 | 69.0 | 9729 | 0.0263 | 52.7137 | 6.9569 |
| 0.1099 | 70.0 | 9870 | 0.0259 | 52.7137 | 6.9569 |
| 0.1006 | 71.0 | 10011 | 0.0256 | 52.725 | 6.9569 |
| 0.1006 | 72.0 | 10152 | 0.0252 | 52.7265 | 6.9578 |
| 0.1006 | 73.0 | 10293 | 0.0249 | 52.7265 | 6.9574 |
| 0.1006 | 74.0 | 10434 | 0.0246 | 52.7536 | 6.9574 |
| 0.0946 | 75.0 | 10575 | 0.0244 | 52.7536 | 6.9574 |
| 0.0946 | 76.0 | 10716 | 0.0242 | 52.8052 | 6.9574 |
| 0.0946 | 77.0 | 10857 | 0.0239 | 52.8052 | 6.9574 |
| 0.0946 | 78.0 | 10998 | 0.0237 | 52.8052 | 6.9574 |
| 0.0985 | 79.0 | 11139 | 0.0235 | 52.8201 | 6.9574 |
| 0.0985 | 80.0 | 11280 | 0.0233 | 52.8201 | 6.9574 |
| 0.0985 | 81.0 | 11421 | 0.0231 | 52.8201 | 6.9574 |
| 0.0978 | 82.0 | 11562 | 0.0230 | 52.8201 | 6.9574 |
| 0.0978 | 83.0 | 11703 | 0.0228 | 52.8201 | 6.9574 |
| 0.0978 | 84.0 | 11844 | 0.0227 | 52.8201 | 6.9574 |
| 0.0978 | 85.0 | 11985 | 0.0226 | 52.8201 | 6.9574 |
| 0.0859 | 86.0 | 12126 | 0.0225 | 52.8396 | 6.9574 |
| 0.0859 | 87.0 | 12267 | 0.0224 | 52.8396 | 6.9574 |
| 0.0859 | 88.0 | 12408 | 0.0223 | 52.8529 | 6.9574 |
| 0.0984 | 89.0 | 12549 | 0.0222 | 52.8529 | 6.9574 |
| 0.0984 | 90.0 | 12690 | 0.0222 | 52.8529 | 6.9574 |
| 0.0984 | 91.0 | 12831 | 0.0221 | 52.8529 | 6.9574 |
| 0.0984 | 92.0 | 12972 | 0.0221 | 52.8724 | 6.9574 |
| 0.0905 | 93.0 | 13113 | 0.0221 | 52.8724 | 6.9574 |
| 0.0905 | 94.0 | 13254 | 0.0221 | 52.8724 | 6.9574 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
