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End of training

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@@ -17,12 +17,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.5355
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- - Rouge1: 0.4922
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- - Rouge2: 0.3075
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- - Rougel: 0.448
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- - Rougelsum: 0.4476
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- - Gen Len: 25.2419
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  ## Model description
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@@ -42,22 +42,37 @@ More information needed
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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: 2
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- - eval_batch_size: 2
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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: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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- | 2.16 | 1.0 | 3200 | 1.6302 | 0.4745 | 0.2896 | 0.4315 | 0.4311 | 25.1044 |
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- | 1.9936 | 2.0 | 6400 | 1.5803 | 0.4888 | 0.3046 | 0.4468 | 0.4463 | 25.2094 |
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- | 1.8414 | 3.0 | 9600 | 1.5484 | 0.4905 | 0.3048 | 0.446 | 0.4455 | 25.695 |
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- | 1.8103 | 4.0 | 12800 | 1.5363 | 0.4903 | 0.3058 | 0.4464 | 0.4465 | 24.9725 |
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- | 1.7387 | 5.0 | 16000 | 1.5355 | 0.4922 | 0.3075 | 0.448 | 0.4476 | 25.2419 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4850
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+ - Rouge1: 0.5287
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+ - Rouge2: 0.3416
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+ - Rougel: 0.4817
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+ - Rougelsum: 0.4819
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+ - Gen Len: 25.7612
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  ## Model description
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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: 4
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+ - eval_batch_size: 4
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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: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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+ | 2.2416 | 1.0 | 1600 | 1.6572 | 0.4727 | 0.2853 | 0.4283 | 0.4282 | 26.6931 |
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+ | 2.0392 | 2.0 | 3200 | 1.5864 | 0.4876 | 0.3019 | 0.4422 | 0.4423 | 25.9194 |
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+ | 1.9573 | 3.0 | 4800 | 1.5519 | 0.4968 | 0.3089 | 0.4509 | 0.451 | 26.8606 |
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+ | 1.8886 | 4.0 | 6400 | 1.5336 | 0.4986 | 0.3111 | 0.4511 | 0.4511 | 26.2788 |
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+ | 1.7906 | 5.0 | 8000 | 1.5149 | 0.5038 | 0.3166 | 0.4567 | 0.4567 | 25.8431 |
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+ | 1.729 | 6.0 | 9600 | 1.5076 | 0.505 | 0.3194 | 0.4584 | 0.4586 | 25.8794 |
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+ | 1.6885 | 7.0 | 11200 | 1.4985 | 0.5119 | 0.3264 | 0.465 | 0.4651 | 25.86 |
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+ | 1.6507 | 8.0 | 12800 | 1.4968 | 0.5154 | 0.329 | 0.4678 | 0.4678 | 26.0975 |
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+ | 1.6412 | 9.0 | 14400 | 1.4854 | 0.515 | 0.3289 | 0.4687 | 0.4689 | 25.4875 |
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+ | 1.5875 | 10.0 | 16000 | 1.4863 | 0.5178 | 0.3327 | 0.4715 | 0.4719 | 25.745 |
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+ | 1.5151 | 11.0 | 17600 | 1.4864 | 0.5197 | 0.3357 | 0.4744 | 0.4744 | 25.5288 |
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+ | 1.509 | 12.0 | 19200 | 1.4819 | 0.5213 | 0.3369 | 0.4755 | 0.4755 | 25.9488 |
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+ | 1.5232 | 13.0 | 20800 | 1.4833 | 0.5237 | 0.3377 | 0.4773 | 0.4778 | 25.5394 |
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+ | 1.4704 | 14.0 | 22400 | 1.4840 | 0.525 | 0.34 | 0.4791 | 0.4792 | 25.46 |
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+ | 1.4585 | 15.0 | 24000 | 1.4836 | 0.5239 | 0.3384 | 0.4779 | 0.4781 | 26.0238 |
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+ | 1.4607 | 16.0 | 25600 | 1.4848 | 0.5265 | 0.3396 | 0.4792 | 0.4794 | 25.8919 |
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+ | 1.42 | 17.0 | 27200 | 1.4889 | 0.5274 | 0.3409 | 0.4803 | 0.4804 | 26.2381 |
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+ | 1.4461 | 18.0 | 28800 | 1.4855 | 0.5283 | 0.342 | 0.4814 | 0.4815 | 25.605 |
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+ | 1.4384 | 19.0 | 30400 | 1.4852 | 0.5284 | 0.3412 | 0.4813 | 0.4816 | 25.8419 |
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+ | 1.4412 | 20.0 | 32000 | 1.4850 | 0.5287 | 0.3416 | 0.4817 | 0.4819 | 25.7612 |
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  ### Framework versions