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t5-small-de-finetuned-en-to-it
din0s/t5-small-de-finetuned-en-to-it
t5-small-de-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 2, 2022
- Updated
- Oct 2, 2022
- SHA
- 3b5fda79
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-small_de-finetuned-en-to-it
This model is a fine-tuned version of din0s/t5-small-finetuned-en-to-de on the ccmatrix dataset. It achieves the following results on the evaluation set:
- Loss: 2.3480
- Bleu: 6.7338
- Gen Len: 61.3273
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: 96
- eval_batch_size: 96
- 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 | 94 | 3.1064 | 2.9057 | 47.5067 |
| No log | 2.0 | 188 | 2.9769 | 2.7484 | 76.9273 |
| No log | 3.0 | 282 | 2.9015 | 3.0624 | 79.8873 |
| No log | 4.0 | 376 | 2.8444 | 3.2959 | 78.276 |
| No log | 5.0 | 470 | 2.7989 | 3.6694 | 74.6013 |
| 3.3505 | 6.0 | 564 | 2.7564 | 3.8098 | 74.3247 |
| 3.3505 | 7.0 | 658 | 2.7212 | 3.9596 | 72.554 |
| 3.3505 | 8.0 | 752 | 2.6886 | 4.2231 | 70.7673 |
| 3.3505 | 9.0 | 846 | 2.6572 | 4.1466 | 72.0113 |
| 3.3505 | 10.0 | 940 | 2.6294 | 4.2696 | 71.1647 |
| 3.0254 | 11.0 | 1034 | 2.6064 | 4.6375 | 67.7707 |
| 3.0254 | 12.0 | 1128 | 2.5838 | 4.7208 | 68.6707 |
| 3.0254 | 13.0 | 1222 | 2.5614 | 4.9191 | 68.5767 |
| 3.0254 | 14.0 | 1316 | 2.5427 | 4.9837 | 66.3867 |
| 3.0254 | 15.0 | 1410 | 2.5241 | 5.1011 | 66.7667 |
| 2.8789 | 16.0 | 1504 | 2.5093 | 5.283 | 64.944 |
| 2.8789 | 17.0 | 1598 | 2.4919 | 5.3205 | 65.738 |
| 2.8789 | 18.0 | 1692 | 2.4788 | 5.3046 | 65.3207 |
| 2.8789 | 19.0 | 1786 | 2.4651 | 5.5282 | 64.9407 |
| 2.8789 | 20.0 | 1880 | 2.4532 | 5.6745 | 63.0873 |
| 2.8789 | 21.0 | 1974 | 2.4419 | 5.7073 | 63.4973 |
| 2.7782 | 22.0 | 2068 | 2.4308 | 5.8513 | 62.8813 |
| 2.7782 | 23.0 | 2162 | 2.4209 | 5.8267 | 64.1033 |
| 2.7782 | 24.0 | 2256 | 2.4124 | 5.8534 | 64.2993 |
| 2.7782 | 25.0 | 2350 | 2.4037 | 6.0406 | 63.8313 |
| 2.7782 | 26.0 | 2444 | 2.3964 | 6.1517 | 63.4213 |
| 2.7116 | 27.0 | 2538 | 2.3897 | 6.2175 | 63.0573 |
| 2.7116 | 28.0 | 2632 | 2.3836 | 6.2551 | 62.876 |
| 2.7116 | 29.0 | 2726 | 2.3777 | 6.4412 | 62.4167 |
| 2.7116 | 30.0 | 2820 | 2.3717 | 6.4604 | 62.1087 |
| 2.7116 | 31.0 | 2914 | 2.3673 | 6.5471 | 62.1373 |
| 2.6662 | 32.0 | 3008 | 2.3634 | 6.5296 | 62.2533 |
| 2.6662 | 33.0 | 3102 | 2.3596 | 6.6623 | 61.276 |
| 2.6662 | 34.0 | 3196 | 2.3564 | 6.6591 | 61.392 |
| 2.6662 | 35.0 | 3290 | 2.3539 | 6.7201 | 61.0827 |
| 2.6662 | 36.0 | 3384 | 2.3516 | 6.675 | 61.3173 |
| 2.6662 | 37.0 | 3478 | 2.3500 | 6.6894 | 61.3507 |
| 2.6411 | 38.0 | 3572 | 2.3488 | 6.6539 | 61.5253 |
| 2.6411 | 39.0 | 3666 | 2.3482 | 6.7135 | 61.3733 |
| 2.6411 | 40.0 | 3760 | 2.3480 | 6.7338 | 61.3273 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
- Library
- transformers
- Type
- t5
- Created
- Oct 2, 2022
- Updated
- Oct 2, 2022