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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ccmatrix |
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metrics: |
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- bleu |
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model-index: |
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- name: t5-small_fr-finetuned-en-to-it |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: ccmatrix |
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type: ccmatrix |
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config: en-it |
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split: train[3000:12000] |
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args: en-it |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 7.4222 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# t5-small_fr-finetuned-en-to-it |
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This model is a fine-tuned version of [din0s/t5-small-finetuned-en-to-fr](https://huggingface.co/din0s/t5-small-finetuned-en-to-fr) on the ccmatrix dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3225 |
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- Bleu: 7.4222 |
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- Gen Len: 59.1127 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 96 |
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- eval_batch_size: 96 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 40 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| |
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| No log | 1.0 | 94 | 3.0406 | 3.2546 | 52.6127 | |
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| No log | 2.0 | 188 | 2.9278 | 3.1206 | 62.774 | |
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| No log | 3.0 | 282 | 2.8573 | 3.4206 | 63.6707 | |
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| No log | 4.0 | 376 | 2.8030 | 3.4847 | 66.408 | |
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| No log | 5.0 | 470 | 2.7602 | 3.8933 | 64.362 | |
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| 3.2982 | 6.0 | 564 | 2.7185 | 3.9298 | 66.058 | |
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| 3.2982 | 7.0 | 658 | 2.6842 | 4.0344 | 65.5773 | |
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| 3.2982 | 8.0 | 752 | 2.6536 | 4.3243 | 65.0047 | |
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| 3.2982 | 9.0 | 846 | 2.6233 | 4.5078 | 64.5813 | |
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| 3.2982 | 10.0 | 940 | 2.5966 | 4.6657 | 63.654 | |
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| 2.9837 | 11.0 | 1034 | 2.5743 | 4.7664 | 63.326 | |
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| 2.9837 | 12.0 | 1128 | 2.5526 | 4.9535 | 62.7327 | |
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| 2.9837 | 13.0 | 1222 | 2.5303 | 5.1386 | 63.5887 | |
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| 2.9837 | 14.0 | 1316 | 2.5122 | 5.1037 | 64.1667 | |
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| 2.9837 | 15.0 | 1410 | 2.4937 | 5.3304 | 63.116 | |
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| 2.8416 | 16.0 | 1504 | 2.4797 | 5.5006 | 61.4953 | |
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| 2.8416 | 17.0 | 1598 | 2.4627 | 5.5892 | 62.01 | |
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| 2.8416 | 18.0 | 1692 | 2.4497 | 5.8497 | 61.42 | |
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| 2.8416 | 19.0 | 1786 | 2.4372 | 6.0074 | 61.1587 | |
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| 2.8416 | 20.0 | 1880 | 2.4256 | 6.1464 | 60.522 | |
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| 2.8416 | 21.0 | 1974 | 2.4148 | 6.3117 | 59.5567 | |
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| 2.7428 | 22.0 | 2068 | 2.4039 | 6.4626 | 59.532 | |
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| 2.7428 | 23.0 | 2162 | 2.3939 | 6.5287 | 60.2307 | |
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| 2.7428 | 24.0 | 2256 | 2.3857 | 6.6093 | 60.22 | |
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| 2.7428 | 25.0 | 2350 | 2.3772 | 6.8004 | 59.396 | |
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| 2.7428 | 26.0 | 2444 | 2.3703 | 6.9433 | 59.5027 | |
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| 2.6779 | 27.0 | 2538 | 2.3631 | 7.0153 | 59.1433 | |
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| 2.6779 | 28.0 | 2632 | 2.3575 | 7.1783 | 58.9793 | |
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| 2.6779 | 29.0 | 2726 | 2.3514 | 7.1639 | 59.362 | |
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| 2.6779 | 30.0 | 2820 | 2.3457 | 7.2176 | 58.9927 | |
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| 2.6779 | 31.0 | 2914 | 2.3411 | 7.2599 | 59.1433 | |
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| 2.6335 | 32.0 | 3008 | 2.3374 | 7.284 | 59.1787 | |
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| 2.6335 | 33.0 | 3102 | 2.3339 | 7.3678 | 59.07 | |
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| 2.6335 | 34.0 | 3196 | 2.3307 | 7.3364 | 58.9813 | |
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| 2.6335 | 35.0 | 3290 | 2.3281 | 7.3318 | 58.96 | |
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| 2.6335 | 36.0 | 3384 | 2.3259 | 7.394 | 59.0787 | |
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| 2.6335 | 37.0 | 3478 | 2.3245 | 7.4133 | 59.0393 | |
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| 2.609 | 38.0 | 3572 | 2.3232 | 7.383 | 59.1887 | |
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| 2.609 | 39.0 | 3666 | 2.3227 | 7.4105 | 59.1227 | |
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| 2.609 | 40.0 | 3760 | 2.3225 | 7.4222 | 59.1127 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.5.1 |
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- Tokenizers 0.11.0 |
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