t5-small_ro-finetuned-en-to-it
This model is a fine-tuned version of din0s/t5-small-finetuned-en-to-ro on the ccmatrix dataset. It achieves the following results on the evaluation set:
- Loss: 2.3301
- Bleu: 7.11
- Gen Len: 59.538
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 | 3.1873 | 1.8829 | 94.336 |
No log | 2.0 | 188 | 3.0132 | 2.5572 | 83.6413 |
No log | 3.0 | 282 | 2.9226 | 3.0999 | 76.166 |
No log | 4.0 | 376 | 2.8559 | 3.3859 | 73.906 |
No log | 5.0 | 470 | 2.8040 | 3.8793 | 70.5667 |
3.4136 | 6.0 | 564 | 2.7585 | 3.8974 | 69.404 |
3.4136 | 7.0 | 658 | 2.7191 | 4.1236 | 68.6387 |
3.4136 | 8.0 | 752 | 2.6836 | 4.3281 | 67.682 |
3.4136 | 9.0 | 846 | 2.6509 | 4.343 | 67.0113 |
3.4136 | 10.0 | 940 | 2.6231 | 4.561 | 66.094 |
3.0128 | 11.0 | 1034 | 2.5984 | 4.9314 | 64.3033 |
3.0128 | 12.0 | 1128 | 2.5755 | 5.0149 | 64.502 |
3.0128 | 13.0 | 1222 | 2.5529 | 5.0758 | 65.194 |
3.0128 | 14.0 | 1316 | 2.5332 | 5.2774 | 64.0773 |
3.0128 | 15.0 | 1410 | 2.5128 | 5.3165 | 64.5213 |
2.859 | 16.0 | 1504 | 2.4976 | 5.4714 | 62.884 |
2.859 | 17.0 | 1598 | 2.4803 | 5.532 | 63.9593 |
2.859 | 18.0 | 1692 | 2.4662 | 5.6314 | 63.7273 |
2.859 | 19.0 | 1786 | 2.4529 | 5.8855 | 62.8953 |
2.859 | 20.0 | 1880 | 2.4401 | 6.1225 | 61.496 |
2.859 | 21.0 | 1974 | 2.4285 | 6.2697 | 60.034 |
2.7544 | 22.0 | 2068 | 2.4171 | 6.4153 | 60.6027 |
2.7544 | 23.0 | 2162 | 2.4067 | 6.3731 | 61.08 |
2.7544 | 24.0 | 2256 | 2.3974 | 6.5355 | 61.3707 |
2.7544 | 25.0 | 2350 | 2.3882 | 6.5259 | 60.966 |
2.7544 | 26.0 | 2444 | 2.3808 | 6.7178 | 60.1707 |
2.6867 | 27.0 | 2538 | 2.3733 | 6.8196 | 60.398 |
2.6867 | 28.0 | 2632 | 2.3672 | 6.811 | 60.2753 |
2.6867 | 29.0 | 2726 | 2.3609 | 6.8322 | 60.0973 |
2.6867 | 30.0 | 2820 | 2.3551 | 6.8524 | 60.16 |
2.6867 | 31.0 | 2914 | 2.3502 | 6.8363 | 60.2867 |
2.6395 | 32.0 | 3008 | 2.3455 | 6.9495 | 59.8547 |
2.6395 | 33.0 | 3102 | 2.3422 | 6.9939 | 59.4813 |
2.6395 | 34.0 | 3196 | 2.3387 | 6.9953 | 59.812 |
2.6395 | 35.0 | 3290 | 2.3362 | 7.0318 | 59.8813 |
2.6395 | 36.0 | 3384 | 2.3339 | 7.1114 | 59.232 |
2.6395 | 37.0 | 3478 | 2.3322 | 7.1488 | 59.292 |
2.6138 | 38.0 | 3572 | 2.3310 | 7.1334 | 59.6093 |
2.6138 | 39.0 | 3666 | 2.3303 | 7.0984 | 59.6887 |
2.6138 | 40.0 | 3760 | 2.3301 | 7.11 | 59.538 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
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