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t5-base_ro-finetuned-en-to-it
din0s/t5-base_ro-finetuned-en-to-it
t5-base_ro-finetuned-en-to-it is a machine learning model from din0s. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- t5
- Access
- Public
- Created
- Oct 9, 2022
- Updated
- Oct 9, 2022
- SHA
- 14a8658e
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
t5-base_ro-finetuned-en-to-it
This model is a fine-tuned version of j0hngou/t5-base-finetuned-en-to-ro on the ccmatrix dataset. It achieves the following results on the evaluation set:
- Loss: 1.4669
- Bleu: 19.6396
- Gen Len: 52.4247
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: 40
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| No log | 1.0 | 282 | 2.0942 | 5.6875 | 73.434 |
| 2.5108 | 2.0 | 564 | 1.9725 | 6.6631 | 72.6607 |
| 2.5108 | 3.0 | 846 | 1.9010 | 7.9227 | 67.01 |
| 2.1659 | 4.0 | 1128 | 1.8452 | 8.9935 | 65.1027 |
| 2.1659 | 5.0 | 1410 | 1.7979 | 9.4164 | 64.9827 |
| 2.0288 | 6.0 | 1692 | 1.7590 | 9.6035 | 66.6933 |
| 2.0288 | 7.0 | 1974 | 1.7264 | 10.7658 | 62.068 |
| 1.9238 | 8.0 | 2256 | 1.6955 | 11.5779 | 59.472 |
| 1.8435 | 9.0 | 2538 | 1.6729 | 12.7588 | 56.584 |
| 1.8435 | 10.0 | 2820 | 1.6541 | 13.3086 | 56.1153 |
| 1.775 | 11.0 | 3102 | 1.6337 | 13.8543 | 55.3307 |
| 1.775 | 12.0 | 3384 | 1.6148 | 14.3566 | 55.2853 |
| 1.7204 | 13.0 | 3666 | 1.5994 | 14.693 | 55.6607 |
| 1.7204 | 14.0 | 3948 | 1.5838 | 15.1284 | 55.5327 |
| 1.6705 | 15.0 | 4230 | 1.5742 | 15.6125 | 55.0087 |
| 1.632 | 16.0 | 4512 | 1.5600 | 15.9616 | 54.052 |
| 1.632 | 17.0 | 4794 | 1.5526 | 16.495 | 53.562 |
| 1.5868 | 18.0 | 5076 | 1.5392 | 16.4252 | 54.4613 |
| 1.5868 | 19.0 | 5358 | 1.5311 | 16.753 | 54.1853 |
| 1.5656 | 20.0 | 5640 | 1.5262 | 17.0308 | 54.2473 |
| 1.5656 | 21.0 | 5922 | 1.5186 | 17.3553 | 53.396 |
| 1.529 | 22.0 | 6204 | 1.5121 | 17.6177 | 53.472 |
| 1.529 | 23.0 | 6486 | 1.5058 | 17.6409 | 53.6847 |
| 1.5071 | 24.0 | 6768 | 1.5038 | 18.2009 | 53.2327 |
| 1.4903 | 25.0 | 7050 | 1.4962 | 18.4838 | 52.9587 |
| 1.4903 | 26.0 | 7332 | 1.4935 | 18.5545 | 52.688 |
| 1.4686 | 27.0 | 7614 | 1.4879 | 18.62 | 53.5 |
| 1.4686 | 28.0 | 7896 | 1.4850 | 19.0099 | 52.34 |
| 1.4511 | 29.0 | 8178 | 1.4813 | 19.0538 | 52.474 |
| 1.4511 | 30.0 | 8460 | 1.4787 | 18.89 | 53.0753 |
| 1.4364 | 31.0 | 8742 | 1.4756 | 19.2582 | 52.3587 |
| 1.4279 | 32.0 | 9024 | 1.4739 | 19.2973 | 52.69 |
| 1.4279 | 33.0 | 9306 | 1.4725 | 19.3624 | 52.694 |
| 1.4172 | 34.0 | 9588 | 1.4704 | 19.5421 | 52.1667 |
| 1.4172 | 35.0 | 9870 | 1.4689 | 19.4807 | 52.5487 |
| 1.4141 | 36.0 | 10152 | 1.4685 | 19.5972 | 52.2733 |
| 1.4141 | 37.0 | 10434 | 1.4676 | 19.5835 | 52.374 |
| 1.4058 | 38.0 | 10716 | 1.4674 | 19.6374 | 52.3447 |
| 1.4058 | 39.0 | 10998 | 1.4671 | 19.6105 | 52.5273 |
| 1.4027 | 40.0 | 11280 | 1.4669 | 19.6396 | 52.4247 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
- Library
- transformers
- Type
- t5
- Created
- Oct 9, 2022
- Updated
- Oct 9, 2022