metadata
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-es
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-en-es-finetuned-es-to-pbb-v0.1
results: []
opus-mt-en-es-finetuned-es-to-pbb-v0.1
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-es on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4966
- Bleu: 4.7931
- Gen Len: 75.4033
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|---|
| No log | 1.0 | 194 | 2.5190 | 0.4261 | 118.0446 |
| No log | 2.0 | 388 | 2.1735 | 0.6863 | 94.2307 |
| 2.6561 | 3.0 | 582 | 2.0123 | 0.8611 | 95.4628 |
| 2.6561 | 4.0 | 776 | 1.9069 | 1.164 | 94.7917 |
| 2.6561 | 5.0 | 970 | 1.8303 | 1.3784 | 91.8423 |
| 1.8552 | 6.0 | 1164 | 1.7642 | 1.9842 | 85.5714 |
| 1.8552 | 7.0 | 1358 | 1.7195 | 2.0005 | 89.3467 |
| 1.6609 | 8.0 | 1552 | 1.6790 | 2.0224 | 85.5595 |
| 1.6609 | 9.0 | 1746 | 1.6490 | 2.3548 | 86.1726 |
| 1.6609 | 10.0 | 1940 | 1.6269 | 2.3567 | 86.1652 |
| 1.5416 | 11.0 | 2134 | 1.5998 | 2.8122 | 84.0179 |
| 1.5416 | 12.0 | 2328 | 1.5780 | 2.5282 | 83.4792 |
| 1.449 | 13.0 | 2522 | 1.5585 | 2.6159 | 82.869 |
| 1.449 | 14.0 | 2716 | 1.5372 | 2.8756 | 81.6518 |
| 1.449 | 15.0 | 2910 | 1.5227 | 3.0051 | 80.8259 |
| 1.3724 | 16.0 | 3104 | 1.5121 | 3.1957 | 79.6518 |
| 1.3724 | 17.0 | 3298 | 1.5006 | 2.847 | 79.2798 |
| 1.3724 | 18.0 | 3492 | 1.4927 | 3.2975 | 77.375 |
| 1.3134 | 19.0 | 3686 | 1.4786 | 3.3924 | 76.744 |
| 1.3134 | 20.0 | 3880 | 1.4698 | 3.5146 | 78.2173 |
| 1.2583 | 21.0 | 4074 | 1.4638 | 3.2835 | 79.1548 |
| 1.2583 | 22.0 | 4268 | 1.4532 | 3.2862 | 78.3363 |
| 1.2583 | 23.0 | 4462 | 1.4521 | 3.5943 | 79.0923 |
| 1.2111 | 24.0 | 4656 | 1.4458 | 3.856 | 77.4092 |
| 1.2111 | 25.0 | 4850 | 1.4426 | 3.6296 | 77.9256 |
| 1.1664 | 26.0 | 5044 | 1.4384 | 3.6092 | 76.442 |
| 1.1664 | 27.0 | 5238 | 1.4342 | 3.7057 | 79.0357 |
| 1.1664 | 28.0 | 5432 | 1.4290 | 3.5534 | 77.8452 |
| 1.1259 | 29.0 | 5626 | 1.4323 | 3.8192 | 77.8304 |
| 1.1259 | 30.0 | 5820 | 1.4258 | 4.0245 | 76.2872 |
| 1.091 | 31.0 | 6014 | 1.4258 | 3.9815 | 75.0164 |
| 1.091 | 32.0 | 6208 | 1.4252 | 3.8806 | 78.3289 |
| 1.091 | 33.0 | 6402 | 1.4252 | 4.0585 | 76.9896 |
| 1.0555 | 34.0 | 6596 | 1.4213 | 4.1074 | 75.9777 |
| 1.0555 | 35.0 | 6790 | 1.4274 | 3.9179 | 79.1533 |
| 1.0555 | 36.0 | 6984 | 1.4220 | 3.8599 | 76.4717 |
| 1.0273 | 37.0 | 7178 | 1.4253 | 4.1578 | 77.6101 |
| 1.0273 | 38.0 | 7372 | 1.4204 | 4.0983 | 78.497 |
| 0.9949 | 39.0 | 7566 | 1.4280 | 4.2085 | 76.6057 |
| 0.9949 | 40.0 | 7760 | 1.4207 | 4.0804 | 75.0729 |
| 0.9949 | 41.0 | 7954 | 1.4234 | 4.1249 | 76.7411 |
| 0.9687 | 42.0 | 8148 | 1.4243 | 4.2974 | 76.2188 |
| 0.9687 | 43.0 | 8342 | 1.4298 | 4.417 | 76.4955 |
| 0.9432 | 44.0 | 8536 | 1.4241 | 4.2923 | 77.0759 |
| 0.9432 | 45.0 | 8730 | 1.4292 | 4.2664 | 77.5982 |
| 0.9432 | 46.0 | 8924 | 1.4316 | 4.2662 | 75.2708 |
| 0.9203 | 47.0 | 9118 | 1.4273 | 4.3311 | 74.3408 |
| 0.9203 | 48.0 | 9312 | 1.4265 | 4.4701 | 76.0432 |
| 0.8967 | 49.0 | 9506 | 1.4335 | 4.5713 | 76.7872 |
| 0.8967 | 50.0 | 9700 | 1.4336 | 4.5226 | 76.9926 |
| 0.8967 | 51.0 | 9894 | 1.4335 | 4.4275 | 77.7232 |
| 0.8732 | 52.0 | 10088 | 1.4416 | 4.5138 | 77.2589 |
| 0.8732 | 53.0 | 10282 | 1.4412 | 4.5469 | 76.0491 |
| 0.8732 | 54.0 | 10476 | 1.4347 | 4.4204 | 74.4568 |
| 0.8563 | 55.0 | 10670 | 1.4396 | 4.2991 | 77.0491 |
| 0.8563 | 56.0 | 10864 | 1.4448 | 4.5678 | 76.7768 |
| 0.8368 | 57.0 | 11058 | 1.4468 | 4.5362 | 76.1518 |
| 0.8368 | 58.0 | 11252 | 1.4487 | 4.5456 | 76.0923 |
| 0.8368 | 59.0 | 11446 | 1.4517 | 4.6951 | 76.692 |
| 0.8187 | 60.0 | 11640 | 1.4501 | 4.6062 | 75.753 |
| 0.8187 | 61.0 | 11834 | 1.4552 | 4.466 | 75.5193 |
| 0.8031 | 62.0 | 12028 | 1.4547 | 4.6685 | 75.8155 |
| 0.8031 | 63.0 | 12222 | 1.4593 | 4.6206 | 75.0625 |
| 0.8031 | 64.0 | 12416 | 1.4570 | 4.7326 | 75.7783 |
| 0.7885 | 65.0 | 12610 | 1.4586 | 4.6804 | 75.5774 |
| 0.7885 | 66.0 | 12804 | 1.4661 | 4.483 | 76.503 |
| 0.7885 | 67.0 | 12998 | 1.4630 | 4.8575 | 76.1146 |
| 0.7749 | 68.0 | 13192 | 1.4654 | 4.8867 | 75.9524 |
| 0.7749 | 69.0 | 13386 | 1.4713 | 4.8378 | 76.4152 |
| 0.7607 | 70.0 | 13580 | 1.4659 | 4.7737 | 77.058 |
| 0.7607 | 71.0 | 13774 | 1.4740 | 4.8789 | 76.3438 |
| 0.7607 | 72.0 | 13968 | 1.4738 | 4.7456 | 75.9554 |
| 0.7494 | 73.0 | 14162 | 1.4733 | 4.8289 | 75.811 |
| 0.7494 | 74.0 | 14356 | 1.4729 | 4.7033 | 75.2247 |
| 0.7369 | 75.0 | 14550 | 1.4749 | 4.7982 | 75.6815 |
| 0.7369 | 76.0 | 14744 | 1.4767 | 4.8117 | 75.8839 |
| 0.7369 | 77.0 | 14938 | 1.4781 | 4.5612 | 75.7188 |
| 0.7283 | 78.0 | 15132 | 1.4779 | 4.7852 | 75.933 |
| 0.7283 | 79.0 | 15326 | 1.4801 | 4.7405 | 76.0967 |
| 0.7196 | 80.0 | 15520 | 1.4833 | 4.6466 | 76.7961 |
| 0.7196 | 81.0 | 15714 | 1.4836 | 4.839 | 75.2604 |
| 0.7196 | 82.0 | 15908 | 1.4853 | 4.7503 | 75.9881 |
| 0.7102 | 83.0 | 16102 | 1.4907 | 4.9235 | 76.244 |
| 0.7102 | 84.0 | 16296 | 1.4889 | 4.8346 | 75.3259 |
| 0.7102 | 85.0 | 16490 | 1.4904 | 4.7364 | 75.9539 |
| 0.7043 | 86.0 | 16684 | 1.4896 | 5.0884 | 75.0208 |
| 0.7043 | 87.0 | 16878 | 1.4920 | 4.6834 | 75.2173 |
| 0.6971 | 88.0 | 17072 | 1.4907 | 4.7318 | 75.7128 |
| 0.6971 | 89.0 | 17266 | 1.4920 | 4.7857 | 75.8586 |
| 0.6971 | 90.0 | 17460 | 1.4923 | 4.661 | 75.0193 |
| 0.6933 | 91.0 | 17654 | 1.4935 | 4.8224 | 74.2054 |
| 0.6933 | 92.0 | 17848 | 1.4942 | 4.8344 | 75.5461 |
| 0.6873 | 93.0 | 18042 | 1.4953 | 4.9447 | 75.25 |
| 0.6873 | 94.0 | 18236 | 1.4949 | 4.734 | 74.7113 |
| 0.6873 | 95.0 | 18430 | 1.4946 | 4.7811 | 75.3973 |
| 0.6853 | 96.0 | 18624 | 1.4965 | 4.7307 | 75.6414 |
| 0.6853 | 97.0 | 18818 | 1.4957 | 4.8139 | 75.5878 |
| 0.682 | 98.0 | 19012 | 1.4963 | 4.799 | 75.2857 |
| 0.682 | 99.0 | 19206 | 1.4965 | 4.8997 | 75.2515 |
| 0.682 | 100.0 | 19400 | 1.4966 | 4.7931 | 75.4033 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3